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IEEE Transactions on Services Computing
Journal Prestige (SJR): 0.87
Citation Impact (citeScore): 5
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1374
Published by IEEE Homepage  [228 journals]
  • Value Entropy: A Systematic Evaluation Model of Service Ecosystem
           Evolution

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      Authors: Xiao Xue;Zhaojie Chen;Shufang Wang;Zhiyong Feng;Yucong Duan;Zhangbing Zhou;
      Pages: 1760 - 1773
      Abstract: With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this article proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Reliability versus Vulnerability of N-Version Programming Cloud Service
           Component With Dynamic Decision Time Under Co-Resident Attacks

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      Authors: Gregory Levitin;Liudong Xing;Yanping Xiang;
      Pages: 1774 - 1784
      Abstract: The virtual machine (VM) co-resident architecture of cloud computing enables simultaneous provision of multiple services to different users, but also makes these services vulnerable to co-resident attacks. For example, by establishing side channels, a malicious attacker can access and even corrupt services performed by other VMs co-residing on the same server as the attacker's VM (AVM). We model a threshold-voting-based N-version programming service component with multiple independent versions simultaneously performing the same requested service to enhance the service reliability. However, the reliability enhancement can be greatly hindered by the co-resident attack, which may corrupt an adequate number of versions leading to a wrong output. We formulate and solve constrained optimization problems that determine the number of service component versions and the voting threshold to balance two conflicting service performance metrics: reliability (service component success probability) and vulnerability (service corruption attack success probability). Two cases respectively having certain and uncertain knowledge about the attacker's power in terms of the number of AVMs are considered. We also investigate impacts of different model parameters on the service performance as well as on solutions to the considered optimization problems through examples.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Task Recommendation in Crowdsourcing Based on Learning Preferences and
           Reliabilities

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      Authors: Qiyu Kang;Wee Peng Tay;
      Pages: 1785 - 1798
      Abstract: Workers participating in a crowdsourcing platform can have a wide range of abilities and interests. An important problem in crowdsourcing is the task recommendation problem, in which tasks that best match a particular worker's preferences and reliabilities are recommended to that worker. A task recommendation scheme that assigns tasks more likely to be accepted by a worker who is more likely to complete it reliably results in better performance for the task requester. Without prior information about a worker, his preferences and reliabilities need to be learned over time. In this article, we propose a multi-armed bandit (MAB) framework to learn a worker's preferences and his reliabilities for different categories of tasks. However, unlike the classical MAB problem, the reward from the worker's completion of a task is unobservable. We therefore include the use of gold tasks (i.e., tasks whose solutions are known a priori and which do not produce any rewards) in our task recommendation procedure. Our model could be viewed as a new variant of MAB, in which the random rewards can only be observed at those time steps where gold tasks are used, and the accuracy of estimating the expected reward of recommending a task to a worker depends on the number of gold tasks used. We show that the optimal regret is $O(sqrt{n})$O(n), where $n$n is the number of tasks recommended to the worker. We develop three task recommendation strategies to-determine the number of gold tasks for different task categories, and show that they are order optimal. Simulations verify the efficiency of our approaches.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Prime Inner Product Encoding for Effective Wildcard-Based Multi-Keyword
           Fuzzy Search

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      Authors: Qin Liu;Yu Peng;Shuyu Pei;Jie Wu;Tao Peng;Guojun Wang;
      Pages: 1799 - 1812
      Abstract: With the prevalence of cloud computing, a growing number of users are delegating clouds to host their sensitive data. To preserve user privacy, it is suggested that data is encrypted before outsourcing. However, data encryption makes keyword-based searches over ciphertexts extremely difficult. This is even challenging for fuzzy search that allows uncertainties or misspellings of keywords in a query. In this article, we propose a prime inner product encoding (PIPE) scheme, which makes use of the indecomposable property of prime numbers to provide efficient, highly accurate, and flexible multi-keyword fuzzy search. Our main idea is to encode either a query keyword or an index keyword into a vector filled with primes or reciprocals of primes, such that the result of vectors’ inner product is an integer only when two keywords are similar. Specifically, we first construct $text{PIPE}_{0}$PIPE0 that is secure in the known ciphertext model. Unlike existing works that have difficulty supporting AND and OR semantics simultaneously, $text{PIPE}_{0}$PIPE0 gives users the flexibility to specify different search semantics in their queries. Then, we construct $text{PIPE}_{text{S}}$PIPES that subtly adds random noises to a query vec-or to resist linear analyses. Both theoretical analyses and experiment results demonstrate the effectiveness of our scheme.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Dynamic Proof of Data Possession and Replication With Tree Sharing and
           Batch Verification in the Cloud

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      Authors: Wei Guo;Sujuan Qin;Fei Gao;Hua Zhang;Wenmin Li;Zhengping Jin;Qiaoyan Wen;
      Pages: 1813 - 1824
      Abstract: Cloud storage attracts a lot of clients to join the paradise. For a high data availability, some clients require their files to be replicated and stored on multiple servers. Because clients are generally charged based on the redundancy level required by them, it is critical for clients to obtain convincing evidence that all replicas are stored correctly and are updated to the up-to-date version. In this article, we propose a dynamic proof of data possession and replication (DPDPR) scheme, which is proved to be secure in the defined security model. Our scheme shares a single authenticated tree across multiple replicas, which reduces the tree's storage cost significantly. Our scheme allows for batch verification for multiple challenged leaves and can verify multiple replicas in a single batch way, which considerably save bandwidth and computation resources during audit process. We also evaluate the DPDPR's performance and compare it with the most related scheme. The evaluation results show that our scheme saves almost 66 percent tree's storage cost for three replicas, and obtains almost 60 and 80 percent efficiency improvements in terms of the overall bandwidth and computation costs, respectively, when three replicas are checked and each challenged with 460 blocks.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • SenSchedule: Scheduling Heterogeneous Resources in Sensor-Cloud
           Infrastructure

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      Authors: Sunanda Bose;Nandini Mukherjee;
      Pages: 1825 - 1840
      Abstract: In a sensor-cloud infrastructure scheduling and allocation of resources are challenging tasks. Unlike computational cloud infrastructure, sensor may have varying capabilities and contexts of services. Moreover, some sensors may be available for a fixed time duration periodically. Depending on some dynamic factors, performance of a sensor may also vary. Such non-uniform performance may be predicted from previous observations. This article proposes novel scheduling algorithms for heterogeneous resources having varying capabilities, contexts of usage and non-uniform performance over time. Simulation results show that the algorithms perform efficiently.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Efficient and Privacy-Preserving Ride Matching Using Exact Road Distance
           in Online Ride Hailing Services

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      Authors: Haining Yu;Xiaohua Jia;Hongli Zhang;Jiangang Shu;
      Pages: 1841 - 1854
      Abstract: Online Ride Hailing (ORH) services enable a rider to request a taxi via a smartphone app in real time. When using ORH services, users (including riders and taxis) have to submit their locations to the ORH server. With received locations, the ORH server makes online ride matching between riders and taxis. There are serious privacy concerns for users to reveal location information to ORH servers. In this article, we propose an efficient and privacy-preserving ride matching scheme for ORH services, named EPRide. EPRide can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location information of both taxis and riders against ORH servers or other curious servers. In EPRide, we propose an efficient exact shortest road distance computation approach over encrypted data, which converts road distance computation into Hamming distance computation over packed ciphertexts by using road network hypercube embedding and somewhat homomorphic encryption. Meanwhile, we design a secure comparison protocol, which efficiently compares encrypted distances in parallel by using ciphertexts blinding and packing, without leaking any distance. Theoretical analysis and experimental evaluations show that EPRide is secure, accurate and efficient.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • FASE: A Fast and Accurate Privacy-Preserving Multi-Keyword Top-k Retrieval
           Scheme Over Encrypted Cloud Data

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      Authors: Guoxiu Liu;Geng Yang;Shuangjie Bai;Huaqun Wang;Yang Xiang;
      Pages: 1855 - 1867
      Abstract: With the advance of cloud computing technology, increasingly more documents are encrypted before being outsourced to the cloud for great convenience and economic savings. Thus, how to design a fast and accurate multi-keyword ranked search scheme over encrypted cloud data is of paramount importance. In this article, we propose a fast and accurate searchable encryption (FASE) scheme that supports accurate top-k multi-keyword retrieval. We utilize a homomorphic order-preserving encryption algorithm to encrypt the index and query vectors. The encryption method supports homomorphic addition, homomorphic multiplication, and order comparison over encrypted data, and it implements the secure calculation of relevance score between encrypted index and query vectors. The encryption method can not only ensure that the calculation of relevance score ($SI_i * T$SIi*T) is not exposed to the cloud server, but also protect the privacy of ranking operator. Compared to the traditional method, there are no dummy keywords added to the query vector and document vector, and the top-k search precision of the FASE scheme is 100 percent. To improve the search efficiency, a large number of irrelevant documents are effectively filtered by matching the document mark vector and query mark vector, and the time cost for calculating the relevance score and ranking is greatly reduced. Furthermore, according to the two-round ranking of the keyword matching degree and the relevance score, not only more accurate search result is returned, but the search efficiency is also further improved. The theoretical analysis and experimental results show that the FASE scheme can achieve fast and accurate multi-keyword-ranking search. In addition to ensuring data privacy and security, it can also effectively improve the search efficiency and reduce the time cost of creating an index, and it can return ranking results which more satisfy the user needs.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • SDRP: Safe, Efficient, and SLO-Aware Workload Consolidation Through Secure
           and Dynamic Resource Partitioning

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      Authors: Myeonggyun Han;Woongki Baek;
      Pages: 1868 - 1882
      Abstract: Workload consolidation is a widely-used technique to improve the resource utilization of services computing systems by consolidating latency-critical (LC) and batch workloads on the same physical server. The resource manager for workload consolidation dynamically allocates hardware resources (e.g., cores, caches) to the workloads to maximize the resource utilization while satisfying the service-level objective (SLO) of the LC workloads. Since security-critical hardware resources are dynamically allocated across consolidated workloads, information leakages can be created among workloads through microarchitectural side-channel (SC) attacks. Despite extensive prior works, it is yet to investigate efficient system software support for achieving high resource utilization without compromising the SLO and security of consolidated workloads. To bridge this gap, we propose SDRP, secure and dynamic resource partitioning for safe, efficient, and SLO-aware workload consolidation. As with the state-of-the-art techniques, SDRP dynamically allocates hardware resources to enhance the resource utilization and provide the SLO guarantees. In contrast to the state-of-the-art techniques, SDRP dynamically sanitizes security-critical hardware resources to robustly defeat microarchitectural SC attacks. Our quantitative evaluation demonstrates that SDRP achieves high resource sanitization quality, introduces low performance overheads, delivers high resource utilization with the SLO and security guarantees, and defeats the last-level cache (LLC)-based SC attack.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Two-Level Stackelberg Game for IoT Computational Resource Trading
           Mechanism: A Smart Contract Approach

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      Authors: Zetao Yang;Kang Liu;Yufei Chen;Wuhui Chen;Mingdong Tang;
      Pages: 1883 - 1895
      Abstract: To support the increasing computation-intensive applications in the Internet of Things (IoT), edge computing is introduced to provide mobile devices computing resources for performing low-latency tasks. Therefore, how to design an effective and secure computing resource allocation mechanism is attracting increasing attention. A lot of works have been done to design an effective computational resource market for IoT, but the problems of vulnerability and inefficiency still exist. In this article, we propose a two-level Stackelberg game-based computing resource trading mechanism for mobile IoT devices with a credit-based payment approach, which is implemented by smart contracts on blockchain. In our model, the Stackelberg game consists of two levels, i.e., leader-level and user-level. In the leader-level, the computing service provider (CSP) and its agent constitute a composite leader. The agent purchases computing resource from CSP on credit and acts as a broker among leader-level and user-level reselling these computing resources to users. In the user-level, every user experiences social externality, which means users are interdependent. The leader-level subgame makes credit payment easier by making loaning and trading become a joint credit payment. The user-level subgame makes the market more active and closer to reality by introducing social externality. Besides, smart contracts can prevent malicious behaviors such as delay payment. We also conduct equilibrium analysis and prove the existence and uniqueness of the Nash equilibrium in our Stackelberg game-based model. Finally, we conduct numerical experiments to evaluate the cost of smart contracts and the performance of each entity with the proposed pricing mechanism.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Delivering Scientific Influence Analysis as a Service on Research Grants
           Repository

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      Authors: Yuming Wang;Yanbo Long;Lai Tu;Ling Liu;
      Pages: 1896 - 1911
      Abstract: Research grants have played an important role in seeding and promoting fundamental research projects worldwide. There is a growing demand for developing and delivering scientific influence analysis as a service on research grant repositories. Such analysis can provide insight on how research grants help foster new research collaborations, encourage cross-organization collaborations, influence new research trends, and identify technical leadership. This article presents the design and development of a grant-based scientific influence analysis service, coined as GImpact. It takes a graph-theoretic approach to design and develop the scientific influence analysis algorithms over a real research-grant repository with three original contributions. First, we model the scientific influence analysis problem as a graph-based analysis problem by constructing heterogeneous graphs from the grants dataset, including mining the dataset to identify and extract important features and represent such features as a research grants information network. Second, we develop the scientific influence analysis algorithms over the research grants information network, which compute the overall scientific influence score by integrating self-influence score and multiple co-influence scores. The self-influence score reflects the grant-based research collaborations among institutions, and the co-influence scores reflect various types of cross-institution collaborations in terms of disciplines and keywords (subject areas). Third, we leverage the cluster analysis on the institution graph as an example application of scientific influence analysis service. By partitioning the institution graph into $K$K clusters, with $K$- as one of the service interface parameters, we show how different disciplines and different keywords are co-related through the grant-based influence analysis. We evaluate GImpact using a real grants dataset, consisting of 2512 institutions and their grants received over a period of 14 years. Our experimental results show that the GImpact influence analysis approach can effectively identify the grant-based research collaboration groups and provide valuable insight on an in-depth understanding of the scientific influence of research grants on research programs, institution leadership, and future collaboration opportunities.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Secure V2V and V2I Communication in Intelligent Transportation Using
           Cloudlets

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      Authors: Maanak Gupta;James Benson;Farhan Patwa;Ravi Sandhu;
      Pages: 1912 - 1925
      Abstract: Intelligent Transportation System (ITS) is a vision which offers safe, secure and smart travel experience to drivers. This futuristic plan aims to enable vehicles, roadside transportation infrastructures, pedestrian smart-phones and other devices to communicate with one another to provide safety and convenience services. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication in ITS offers ability to exchange speed, heading angle, position and other environment related conditions amongst vehicles and with surrounding smart infrastructures. In this intelligent setup, vehicles and users communicate and exchange data with random untrusted entities (like vehicles, smart traffic lights or pedestrians) whom they don’t know or have met before. The concerns of location privacy and secure communication further deter the adoption of this smarter and safe transportation. In this article, we present a secure and trusted V2V and V2I communication approach using edge infrastructures where instead of direct peer to peer communication, we introduce trusted cloudlets to authorize, check and verify the authenticity, integrity and ensure anonymity of messages exchanged in the system. Moving vehicles or road side infrastructure are dynamically connected to nearby cloudlets, where security policies can be implemented to sanitize or stop fake messages and prevent rogue vehicles to exchange messages with other vehicles. We also present a formal attribute-based model for V2V and V2I communication, called AB-ITS, along with proof of concept implementation of the proposed solution in AWS IoT platform. This cloudlet supported architecture complements direct V2V or V2I communication, and serves important use cases such as accident or ice-threat warning and other safety applications. Performance metrics of our proposed architecture are also discussed and compared with existing ITS technologies.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • ANNPDP: An Efficient and Stable Evaluation Engine for Large-Scale Policy
           Sets

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      Authors: Fan Deng;Zhenhua Yu;Liyong Zhang;Jiawei Wang;Kexin Feng;Wenbin Kong;Lingyu Li;Jiawen Wu;
      Pages: 1926 - 1939
      Abstract: As interactions between individuals and services increase, requests are more frequent and policy sets are larger. The evaluation performance of PDP (Policy Decision Point) plays a key role in the operation of a system. In order to solve bottlenecks of improving the PDP evaluation performance for large-scale policy sets, we propose an evaluation engine based on artificial neural networks, namely ANNPDP. We transform rules in a large-scale policy set described in the XACML (eXtensible Access Control Markup Language) into numerical rules. Evaluation networks are established and trained by the numerical rules. In order to ensure the accuracy, a misjudgment set is constructed for error corrections and stored by hash indexes. By simulating the arrival of requests, ANNPDP is compared with the Sun PDP, HPEngine, XEngine, and SBA-XACML. The experiment results show that ANNPDP has: 1) high performance: if the number of requests reaches 10,000, the evaluation time of ANNPDP on the large-scale policy set with 100,000 rules is approximately 0.46, 0.93, 0.71, and 1.43 percent of that of the Sun PDP, HPEngine, XEngine, and SBA-XACML, respectively, and 2) stability: as the size of the large-scale policy set and the number of requests increase, the evaluation time of ANNPDP grows linearly. ANNPDP can satisfy the requirements of an authorization system with large-scale policy sets.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • DeepWSC: Clustering Web Services via Integrating Service Composability
           into Deep Semantic Features

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      Authors: Guobing Zou;Zhen Qin;Qiang He;Pengwei Wang;Bofeng Zhang;Yanglan Gan;
      Pages: 1940 - 1953
      Abstract: With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Safe-Serv: Energy-Efficient Decision Delivery for Provisioning
           Safety-as-a-Service

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      Authors: Chandana Roy;Sudip Misra;Jhareswar Maiti;Ujjayini Chakravarty;
      Pages: 1954 - 1966
      Abstract: In this article, we introduce an energy-efficient decision delivery mechanism, Safe-Serv, in the Safety-as-a-Service (Safe-aaS) infrastructure for the road transportation industry. A Safe-aaS architecture provides safety-related customized dynamic decisions to the registered end-users. Moreover, the concept of decision virtualization enables to deliver the same decision to multiple end-users at the same time. The sensor nodes sense and transmit the data to the edge node/cloud, which is further processed to generate a decision. As the sensor nodes are energy-constrained in nature, energy efficiency is one of the important parameters to be considered for Safe-aaS infrastructure. Safe-Serv reduces energy consumption through the elimination of redundant data transmission from the sensor node to the edge node or cloud. We use the cooperative Nash bargaining approach among different homogeneous sensor nodes, which bargain among themselves to transmit data to the edge node/cloud. Based on the total dissipated energy, effective proportional distance, duty factor, nodal delay, and cost-efficient state, the appropriate sensor node is chosen. Thus, the selected sensor node transmits data to the edge layer or cloud. We incorporate the cost of data transmitted by the sensor node, which leads to cost-effective utilization of the resources. Through extensive simulation, we observe that the energy dissipated by the sensor nodes using the proposed scheme, Safe-Serv, is reduced by 85 and 78 percent approximately compared to the existing schemes – SASPENCE and manoeuvre-based trajectory planning – respectively.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Divide-and-Iterate Approach to Big Data Systems

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      Authors: Shih Yu Chang;Hsiao-Chun Wu;
      Pages: 1967 - 1979
      Abstract: Matrix calculations are often required for the analysis of any big-data cloud computing system. It is quite common to process big-data associated matrices possessing the sparsity and low-rank properties. In order to efficiently deal with big-data matrices, we propose a new divide-and-iterate framework, which can be invoked to solve an enormously large linear system of equations by taking advantage of factored matrices. The Kaczmarz algorithm (KA) is utilized here to design the parallel iterative algorithms which are capable of solving a large system of equations by iteratively updating the solution through the reduction into the factorized subsystems in parallel. The convergences of our proposed new iterative algorithms are justified by the rigorous proofs. Besides, the time- and memory-complexities are studied to demonstrate the resource efficiency of the proposed algorithms. Numerical experiments are also presented to illustrate the effectiveness of this proposed new framework.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Trading off Between Multi-Tenancy and Interference: A Service User
           Allocation Game

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      Authors: Guangming Cui;Qiang He;Feifei Chen;Hai Jin;Yun Yang;
      Pages: 1980 - 1992
      Abstract: Edge computing, as an emerging and prospective distributed computing paradigm, allows a service provider to serve its users by allocating them to nearby edge servers delivering services with low latency. From the service provider’s perspective, a cost-effective service user allocation aims to allocate maximum service users to minimum edge servers. Such an allocation leverages multi-tenancy to reduce the resources hired by the service provider for serving the service users. However, the allocation of excessive service users to an edge server may result in severe interference and consequently impact their data rates. There is a trade-off between multi-tenancy and interference in the pursuit of a cost-effective service user allocation. In this article, we formally model this service user allocation (SUA) problem, and prove that it is NP-hard to find the optimal solution to an SUA problem. To solve the SUA problem effectively and efficiently, we propose a game-theoretic approach, namely MI-SUAGame, to formulate the SUA problem as a potential game. We analyze the game and prove its admission to a Nash equilibrium. Then, a novel decentralized algorithm is designed for finding a Nash equilibrium in the game as the solution to the SUA problem. The performance of MI-SUAGame is theoretically analyzed and experimentally evaluated against the state-of-the-art approach. The results show that it can solve the SUA problem effectively and efficiently.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • An Edge Storage Acceleration Service for Collaborative Mobile Devices

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      Authors: Xiong Gao;Weidong Bao;Xiaomin Zhu;Guanlin Wu;Ling Liu;
      Pages: 1993 - 2006
      Abstract: Fueled by the advances in the Internet of Things, and the growing capacity of smart mobile devices at the edge of the Internet, we have witnessed a growing trend in research and development for edge computing and edge storage, which extends the abilities of single mobile device on the edge through on-demand collaboration among multiple geographically distributed mobile devices. In this article, we address several technical challenges that are unique for collaborative storage at the edge due to the unique characteristics of mobile devices. First, we formalize the collaborative storage problem as an optimization problem. Second, we design an Acceleration Algorithm for Collaborative Storage, called A2CS, based on the architecture of Alternating Direction Method of Multipliers (ADMM). Specifically, we use the Nesterov’s Acceleration strategy and the step size rules in the process of updating variables and determining the optimal speed of convergence. We develop a novel collaborative storage policy in order to guide the whole lifecycle of collaborative storage. Finally, we conduct a series of experiments for acceleration performance analysis and validation. We show that A2CS delivers a better convergence performance with different step size rules, compared with two existing approaches: the ADMM baseline and the ADMM-OR (ADMM with Over-Relaxation), achieving the acceleration percentage by at least 25.33 percent and at most 64.01 percent. In addition, by conducting the utility performance comparison analysis with the existing Average Distribution Strategy (ADS) and the existing Distance Preferred Distribution Strategy (DPDS), we show the advantage of A2CS over both ADS and DPDS with respect to the total utility and energy consumption.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog
           Computing Environment

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      Authors: Sara Ghanavati;Jemal Abawajy;Davood Izadi;
      Pages: 2007 - 2017
      Abstract: Fog computing has become a platform of choice for executing emerging applications with low latency requirements. Since the devices in fog computing tend to be resource constraint and highly distributed, how fog computing resources can be effectively utilized for executing delay-sensitive tasks is a fundamental challenge. To address this problem, we propose and evaluate a new task scheduling algorithm with the aim of reducing the total system makespan and energy consumption for fog computing platform. The proposed approach consists of two key components: 1) a new bio-inspired optimization approach called Ant Mating Optimization (AMO) and 2) optimized distribution of a set of tasks among the fog nodes within proximity. The objective is to find an optimal trade-off between the system makespan and the consumed energy required by the fog computing services, established by end-user devices. Our empirical performance evaluation results demonstrate that the proposed approach outperforms the bee life algorithm, traditional particle swarm optimization and genetic algorithm in terms of makespan and consumed energy.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • A Framework for Dynamic Composition and Management of Emergency Response
           Processes

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      Authors: Abeer Elahraf;Ayesha Afzal;Ahmed Akhtar;Basit Shafiq;Jaideep Vaidya;Shafay Shamail;Nabil R. Adam;
      Pages: 2018 - 2031
      Abstract: An emergency response process outlines the workflow of different activities that need to be performed in response to an emergency. Effective emergency response requires communication and coordination with the operational systems belonging to different collaborating organizations. Therefore, it is necessary to establish information sharing and system-level interoperability among the diverse operational systems. Unlike typical e-government processes that are well structured and have a well-defined outcome, emergency response processes are knowledge-centric and their workflow structure and execution may evolve as the incident unfolds. It is impractical to define static plans and response process workflows for every possible situation. Instead, a dynamic response should be adaptable to the changing situation. We present an integrated approach that facilitates the dynamic composition of an executable response process. The proposed approach employs ontology-based reasoning to determine the default actions and resource requirements for the given incident and to identify relevant response organizations based on their jurisdictional and mutual aid agreement rules. The Web service APIs of the identified response organizations are then used to generate an executable response process that evolves dynamically. The proposed approach is implemented and experimentally validated using an example scenario derived from the FEMA Hazardous Materials Tabletop Exercises Manual.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Context-Aware Multi-Criteria Handover at the Software Defined Network Edge
           for Service Differentiation in Next Generation Wireless Networks

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      Authors: Peng Zhao;Wei Yu;Xinyu Yang;Duolun Meng;Li Wang;Shusen Yang;Jie Lin;
      Pages: 2032 - 2046
      Abstract: The densified deployment of heterogeneous networks coexisting with a variety of overlapping cells has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, the heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider the subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing the control plane and data plane, software defined network(SDN) offers a flexible architectural paradigm to overcome these challenges. In this article, we first develop an SDN-driven handover architecture that is capable of perceiving global network status and requirements from various perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN edge to provide differentiated services. Considering the numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms the baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Enabling Fast Public Auditing and Data Dynamics in Cloud Services

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      Authors: Changhee Hahn;Hyunsoo Kwon;Daeyeong Kim;Junbeom Hur;
      Pages: 2047 - 2059
      Abstract: Public auditing enables efficient integrity checks of data assigned to cloud servers. In this article, we revisit the public auditing for encrypted data, in which a major concern is how to effectively support data dynamics, i.e., data modification, insertion, and deletion. We first determine which factor in existing auditing schemes most limits data dynamics from a cost perspective. We then propose a novel public auditing scheme that provides data dynamics that are orders of magnitude faster than previous methods. Our auditing challenge-response protocol reduces the computation cost of the third-party auditor (TPA) significantly, thus increasing the verification speed for the auditing results. Performance and security analysis demonstrates that the proposed scheme generates minimal computation costs while guaranteeing data integrity and privacy against an untrusted cloud.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • FedCrowd: A Federated and Privacy-Preserving Crowdsourcing Platform on
           Blockchain

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      Authors: Yu Guo;Hongcheng Xie;Yinbin Miao;Cong Wang;Xiaohua Jia;
      Pages: 2060 - 2073
      Abstract: Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Data Caching Optimization in the Edge Computing Environment

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      Authors: Ying Liu;Qiang He;Dequan Zheng;Xiaoyu Xia;Feifei Chen;Bin Zhang;
      Pages: 2074 - 2085
      Abstract: With the rapid increase in the use of mobile devices in people’s daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed in close proximity to mobile users, caching popular data on edge servers can ensure mobile users’ low-latency access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data caching problem with a focus on the reduction of network delay and the improvement of mobile devices’ energy efficiency. In this article, we tackle this data caching problem in the edge computing environment from a service provider’s perspective with the aim to maximize its data caching revenue. This problem is challenging because there is a trade-off between the benefit produced and the cost incurred by caching data on edge servers. In the meantime, the constraint for data access latency must also be fulfilled. In this article, we formulate the data caching problem in the edge computing environment as an integer programming (IP) problem and prove its NP-completeness. To solve this problem effectively and efficiently in large-scale scenarios, we propose an approximation approach to find near-optimal solutions. Extensive experiments are conducted on a widely-used real-world dataset to evaluate our approaches.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • LCDD: Detecting Business Process Drifts Based on Local Completeness

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      Authors: Leilei Lin;Lijie Wen;Li Lin;Jisheng Pei;Hedong Yang;
      Pages: 2086 - 2099
      Abstract: Flexibility and evolution have been a hot topic in the context of business process management. However, contemporary process mining techniques assume processes to be in a steady state, which will result in a mixed-model mined from the event log. Business process drift detection is a family of methods to detect changes by analyzing the event log, but existing methods have some disadvantages in dealing with concept drifts. First, most of these methods detect changes depending on an exploration of a potentially large feature space and are time consuming. Second, with the size of delay period becoming small, the accuracies of some methods will drop rapidly. In this article, we address these problems by proposing a novel drift detection technique called LCDD that significantly differs from existing methods. The core idea is to find out new features and disappeared features from traces after the log fragment meets local completeness. To begin with, we use the relations of direct succession as the lightweight feature, which are compared between two windows (complete window and detection window). Then, we find new direct successions and stable disappeared direct successions by just moving detection window. Finally, the forgetting mechanism is used to abandon some direct successions after finding a change point. An extensive empirical evaluation shows that LCDD is faster and more accurate.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Composing Web Services Using a Multi-Agent Framework

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      Authors: Yu Zhao;Daniel Alencar da Costa;Ying Zou;
      Pages: 2100 - 2113
      Abstract: Different web services can be composed to perform increasingly complex tasks (e.g., making an on-line payment). However, existing approaches compose web services with hard-coded control and data flows. To proactively and autonomously compose web services, developers can develop agents. However, the development of agents for service composition is complex, due to the reasons that: 1) developers may not have the knowledge from various domains to identify the necessary tasks to carry out the required web services; and 2) a deep understanding of the agent specific code is required in order to implement agents. To alleviate the required efforts to develop agents, we propose an approach to separate the development of agent specific code from the business logic code in the service composition. More specifically, we provide an easy-to-understand syntax that abstracts agent specific code and automatically generates executable agent code. Our experimental results show that our approach can accurately identify tasks for service composition with an Area Under the Curve (AUC) of 0.88. Our experiments also demonstrate that our approach can correctly generate agent code from seven agent specifications. Finally, our user studies reveal that developers are satisfied with our approach to develop agents for service composition.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Computing the Number of Loop-Free k-hop Paths of Networks

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      Authors: Jianshe Wu;Nan Chen;Chaojie Zhou;Hefei Che;Chunlei Han;Qin Liu;
      Pages: 2114 - 2128
      Abstract: Computing the number of k-hop paths is crucial for selecting services in social networks and analyzing graph data, for example, a service consumer require to evaluate the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider, there are usually many social trust paths between two unconnected participants, people need to know the number of loop-free k-hop trust propogation paths; other applications include the similarity computation for services recommendation, information diffusion, etc. Previously, the number of k-hop paths is roughly estimated by the elements in the k multiplications of the network adjacency matrix. This method calculates much more k-hop paths than those actually exist, due to many paths with loops counted as k-hop paths, which may result in obvious errors in applications. Based on the idea of loops removing, accurate mathematical formulas for counting loop-free paths are obtained in this article for paths with five or less hops, an approximate method is provided for larger hops. Based on the proposed loop removing algorithm (LRA), the typical method for predicting trust between any two people in social networks is improved, the error rate is dramatically reduced; the traditional path based similarity indices are improved, which are much accurate than their antecedent counterparts; and a method for computing the spreading probability for information spreading between two unconnected vertices in the famous independent cascade (IC) model is also obtained. To reveal the effectiveness of the proposed LRA, this article also provide a traversal depth-first search algorithm (DFSA) for finding the true number of k-hop loop-free paths.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Long-Term IaaS Selection Using Performance Discovery

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      Authors: Sheik Mohammad Mostakim Fattah;Athman Bouguettaya;Sajib Mistry;
      Pages: 2129 - 2143
      Abstract: We propose a novel framework to select IaaS providers according to a consumer’s long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider’s long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Feedback Adaptive Learning for Medical and Educational Application
           Recommendation

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      Authors: Cem Tekin;Sepehr Elahi;Mihaela van der Schaar;
      Pages: 2144 - 2157
      Abstract: Recommending applications (apps) to improve health or educational outcomes requires long-term planning and adaptation based on the user feedback, as it is imperative to recommend the right app at the right time to improve engagement and benefit. We model the challenging task of app recommendation for these specific categories of apps—or alike—using a new reinforcement learning method referred to as episodic multi-armed bandit (eMAB). In eMAB, the learner recommends apps to individual users and observes their interactions with the recommendations on a weekly basis. It then uses this data to maximize the total payoff of all users by learning to recommend specific apps. Since computing the optimal recommendation sequence is intractable, as a benchmark, we define an oracle that sequentially recommends apps to maximize the expected immediate gain. Then, we propose our online learning algorithm, named FeedBack Adaptive Learning (FeedBAL), and prove that its regret with respect to the benchmark increases logarithmically in expectation. We demonstrate the effectiveness of FeedBAL on recommending mental health apps based on data from an app suite and show that it results in a substantial increase in the number of app sessions compared with episodic versions of $epsilon _n$εn-greedy, Thompson sampling, and collaborative filtering methods.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Attribute-Based Encryption Scheme for Secure Multi-Group Data Sharing in
           Cloud

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      Authors: MD Azharul Islam;Sanjay K Madria;
      Pages: 2158 - 2172
      Abstract: Most of the organizations using the cloud-based data sharing platforms are multi-group in nature. The existing directly revocable attribute-based encryption (ABE) schemes though seem to be a good fit, but they fail to provide any effective solution for secure multi-group data sharing scenarios. To bridge this gap, we first propose Revocable ABE with Verifiable Outsourced decryption (ReVO-ABE)- a directly revocable collusion-resistant ABE scheme that allows any number of user revocation and joining without affecting the secret membership keys of the nonrevoked users. Based on ReVO-ABE, we build a Dynamic Multi-Group Secure Data Sharing scheme called DMG-SDS. For operations that are exclusive to multi-groups like group merge and split can be performed without affecting the attribute secret keys or membership keys of the nonrevoked users, which is not possible with any of the existing schemes. Our proposed scheme meets the necessary security requirements, and the performance assessment shows that it has much better performance benefits when compared with most the recent competitive schemes.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • PoTS: A Secure Proof of TEE-Stake for Permissionless Blockchains

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      Authors: Sébastien Andreina;Jens-Matthias Bohli;Ghassan O. Karame;Wenting Li;Giorgia Azzurra Marson;
      Pages: 2173 - 2187
      Abstract: Proof of Stake (PoS) blockchain protocols emerged as a promising alternative to the largely energy-wasteful proof of work mechanisms currently in place. In contrast to computing power, however, “stake” is a virtual resource that can be replicated or reused, opening the door to attack vectors that have no counterpart in a PoW setting, and are much harder to defeat. We present PoTS (Proof of TEE-Stake), a novel PoS protocol that leverages properties of trusted execution environments (TEEs) to limit the attack surface of malicious validators, and employs techniques such as forward security to guarantee protection against posterior-corruption attacks. We show that PoTS is secure against nothing at stake, grinding, and long range attacks down to realistic hardware assumptions on TEE and well-established cryptographic assumptions, and retains reasonable security even in face of compromised TEEs. We evaluate the performance of our proposal by means of implementation. Our evaluation results demonstrate that PoTS offers an excellent trade-off between security and performance.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Secure Hot Path Crowdsourcing With Local Differential Privacy Under Fog
           Computing Architecture

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      Authors: Mengmeng Yang;Ivan Tjuawinata;Kwok Yan Lam;Jun Zhao;Lin Sun;
      Pages: 2188 - 2201
      Abstract: Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collect sensor data for marketing or research purpose. In this article, we consider crowdsourcing these worker's hot travel path. Each worker is required to report his real-time location information, which is sensitive and has to be protected. Encryption-based methods are the most direct way to protect the location, but not suitable for resource-limited devices. Besides, local differential privacy is a strong privacy concept and has been deployed in many software systems. However, the local differential privacy technology needs a large number of participants to ensure the accuracy of the estimation, which is not always the case for crowdsourcing. To solve this problem, we proposed a trie-based iterative statistic method, which combines additive secret sharing and local differential privacy technologies. The proposed method has excellent performance even with a limited number of participants without the need of complex computation. Specifically, the proposed method contains three main components: iterative statistics, adaptive sampling, and secure reporting. We theoretically analyze the effectiveness of the proposed method and perform extensive experiments to show that the proposed method not only provides a strict privacy guarantee, but also significantly improves the performance from the previous existing solutions.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Secure Data Delivery With Identity-Based Linearly Homomorphic Network
           Coding Signature Scheme in IoT

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      Authors: Yumei Li;Futai Zhang;Xin Liu;
      Pages: 2202 - 2212
      Abstract: With the appearance and flourishing development of the Internet of Things (IoT), wireless sensor networks technology has been attracting increasing attention. Network coding is an indispensable technology in the wireless sensor networks, which can improve network transmission throughput. However, pollution attacks is a serious security problem that must be faced in the process of data coding. Although the homomorphic network coding signature schemes can solve this troublesome, the high signature generation and verification cost of these schemes will reduce the transmission efficiency. In this article, we propose an efficient identity-based linearly homomorphic network coding signature scheme for wireless sensor networks to guarantee data integrity and authenticity. In our scheme, the computation cost of signature generation and verification are both independent of the size of the data packet. The scheme is proved secure against existential forgery under adaptive chosen identity and adaptive chosen subspace attacks in random oracle model. Using Java pairing-based Cryptography Library (JPBC), the simulation results illustrate that our scheme is more efficient in practical application.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Predicting Product Review Helpfulness – A Hybrid Method

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      Authors: Li Kong;Chuanyi Li;Jidong Ge;Vincent Ng;Bin Luo;
      Pages: 2213 - 2225
      Abstract: Recent years have seen a rapidly growing number of online reviews of products. As a result, it is often not possible for customers to go through each review before making purchase decisions. One way to address this problem is to build a system for automatically addressing the helpfulness of reviews and present only those reviews that are determined to be helpful by the system to an end user. The vast majority of existing approaches to the task of review helpfulness prediction are based on hand-crafted features, thus making system performance heavily dependent on the quality of these features. In light of this weakness, we propose a new model of review helpfulness prediction using a combination of Convolutional Neural Network (CNN) and TransE wherein hand-crafted features can also be incorporated to improve the output. Specifically, CNN enables us to learn the semantic information from a review and TransE is used to capture the relationship between different entities mentioned in the review. Experiments on the Amazon product review datasets demonstrate that our approach significantly outperforms the state of the art.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Analysis and Enhancement of a Lattice-Based Data Outsourcing Scheme With
           Public Integrity Verification

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      Authors: Qingxuan Wang;Chi Cheng;Rui Xu;Jintai Ding;Zhe Liu;
      Pages: 2226 - 2231
      Abstract: Recently, Zhang et al. proposed a lattice-based data outsourcing scheme with public integrity verification (DOPIV), which enables an original data owner to delegate a proxy to generate the signatures of data and outsource them to the cloud server. They employed a third party auditor (TPA) to check the integrity of the outsourced data and any TPA can verify the data integrity efficiently. DOPIV is claimed to achieve proxy-oriented secure data outsourcing as well as storage correctness guarantee. Unfortunately, we find that there exist vulnerabilities in DOPIV which allow the cloud server to simply delete the received data without being noticed by the TPA. Fortunately, we come up with a simple and efficient solution to thwart the proposed attack. Our improved scheme maintains all the features claimed in DOPIV.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • CAHPHF: Context-Aware Hierarchical QoS Prediction With Hybrid Filtering

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      Authors: Ranjana Roy Chowdhury;Soumi Chattopadhyay;Chandranath Adak;
      Pages: 2232 - 2247
      Abstract: With the proliferation of Internet-of-Things and continuous growth in the number of web services at the Internet-scale, the service recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service recommendation is the Quality-of-Service (QoS) parameter, which depicts the performance of a web service. In general, the service provider furnishes the value of the QoS parameters before service deployment. However, in reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task, and thus, the QoS prediction has gained significant research attention. Multiple approaches are available in the literature for predicting service QoS. However, these approaches are yet to reach the desired accuracy level. In this article, we study the QoS prediction problem across different users, and propose a novel solution by taking into account the contextual (more specifically, location) information of both services and users. Our proposal includes two key steps: (a) hybrid filtering, and (b) hierarchical prediction mechanism. On the one hand, the hybrid filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical prediction mechanism is to estimate the QoS value accurately by leveraging hierarchical neural-regression. We evaluated our framework on the publicly available WS-DREAM datasets. The experimental results show the outperformance of our framework over the major state-of-the-art approaches.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Scalable Rate Allocation for SDN With Diverse Service Requirements

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      Authors: Jian-Jhih Kuo;Chih-Hang Wang;Yishuo Shi;De-Nian Yang;Wen-Tsuen Chen;
      Pages: 2248 - 2260
      Abstract: Flow consolidation has been proposed for merging multiple flows from different services into an aggregate flow to remedy the state explosion problem in software-defined networks (SDN). However, we observe that the Quality of Service (QoS) requirements are no longer sustained in aggregate flows since the bandwidth decided by TCP is usually different from the desired rate of each service. Therefore, this article explores an idea to control the rates of only a few service flows so that the rates of all uncontrolled flows allocated by TCP will meet their QoS requirements. We design a new architecture, called Scalable Per-Flow Rate Allocation (SPFRA), and formulate a new optimization problem, termed Scalable Rate Allocation for Aggregate Flows (SRAF), to find a minimum number of controlled flows to increase the scalability of SDN with diverse service requirements. We prove the NP-hardness and inapproximability of SRAF. To solve the problem, we design an algorithm, named Aggregate Flow Selection and Flow Release (AFSFR), to achieve the tightest bound and extend it to support distributed computation and dynamic traffic for instant services. Simulations and implementation on an SDN testbed manifest that AFSFR performs nearly optimally in real networks, and the number of controlled flows can be effectively reduced by 50 percent.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • ESync: Accelerating Intra-Domain Federated Learning in Heterogeneous Data
           Centers

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      Authors: Zonghang Li;Huaman Zhou;Tianyao Zhou;Hongfang Yu;Zenglin Xu;Gang Sun;
      Pages: 2261 - 2274
      Abstract: Federated Learning (FL) serves privacy-preserving collaborative learning among multiple isolated parties, while retaining their privacy data locally. Cross-device and cross-silo FL have achieved great success in cross-domain applications, in which the scarce communication resource is the primary bottleneck. Driven by the need to combine heterogeneous machines from different parties to build a shared data center, we found intra-domain FL, a new type of FL in which isolated parties collaborate in the shared data center, and strong computational heterogeneity becomes the primary bottleneck. To mitigate the training inefficiency caused by stragglers, this article proposes an efficient synchronization algorithm ESync, which allows parties to train different iterations locally under the coordination of a novel scheduler State Server. We give the boundaries of weight divergence and optimality gap of ESync, and analyze the trade-off between convergence accuracy and communication efficiency. Extensive experiments are conducted to compare ESync with SSGD, ASGD, DC-ASGD, FedAvg, FedAsync, TiFL, and FedDrop under strong computational heterogeneity. Numerical results show that ESync achieves great speed up without loss of accuracy, and therefore demonstrate the effectiveness of ESync in both training efficiency and converged accuracy.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Fairness-Aware Mechanism for Load Balancing in Distributed Systems

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      Authors: Avadh Kishor;Rajdeep Niyogi;Bharadwaj Veeravalli;
      Pages: 2275 - 2288
      Abstract: When a set of self-interested users shares multiple resources in a distributed system, we face the problem of allocating resources, called the load balancing problem. In particular, load balancing is defined as allocating the load to the servers of the distributed system such that jobs’ response time is minimized, and the utilization of servers is improved. In this article, the load balancing problem in a distributed system consists of a finite set of servers, and a finite set of users is studied. The load balancing problem considered here is a bi-objective problem with two highly probable conflicting objectives: (i) minimizing jobs’ response time (ii) providing the fair utilization of servers. In order to satisfy these two objectives simultaneously, both the objectives are considered in an integrated manner. Next, the load balancing problem is formulated as a noncooperative game; and to solve the game (i.e., to find the Nash equilibrium), a distributed load balancing algorithm (DLBA) is proposed. An experimental study is carried out to ascertain the efficacy of the proposed DLBA. Further, we compare DBLA with three existing load balancing approaches to evaluate its comparative effectiveness. The experimental results validate the effectiveness of the DLBA over the existing approaches.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Fast and Secure Data Accessing by Using DNA Computing for the Cloud
           Environment

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      Authors: Suyel Namasudra;
      Pages: 2289 - 2300
      Abstract: In a cloud environment, traditional approaches are used to encrypt any data by using 0 and 1 that increase data security issues because of the presence of numerous malicious users and hackers over the internet. Deoxyribonucleic Acid (DNA) computing can be one of the best solutions to improve data security in which data are encrypted using the DNA bases: Thymine (T), Guanine (G), Cytosine (C) and Adenine (A). Along with data security, access control is another major issue of a cloud environment as the searching time of the owner of any data, the system overheard and the accessing time of a file or data are high during data access. A novel DNA computing based secure and fast Access Control Model (ACM) is proposed in this article to solve all these major problems. In the proposed scheme, the Cloud Service Provider (CSP) keeps a table or list for fast data accessing. Here, a 1024-bit DNA computing based random key is generated by using the user's secret information, and the same key is utilized for data encryption. Theoretical analysis along with many experimental results prove the efficiency and effectiveness of the proposed access control model over some well-known existing models.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Temporal-Perturbation Aware Reliability Sensitivity Measurement for
           Adaptive Cloud Service Selection

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      Authors: Lei Wang;Qiang He;Demin Gao;Jing Wan;Yunqiu Zhang;
      Pages: 2301 - 2313
      Abstract: Benefiting from the pay-as-you-go business model, cloud-based software applications are becoming more and more popular. A composite cloud system can be constructed by integrating existing component cloud services available over the internet as its system components. In order to fulfill the service-level agreements (SLAs), as well as users’ quality of experience (QoE), a stable execution of the constructed system is desirable in the long term. To achieve this goal, system components at high risk of failing must be identified and fault-tolerated. This is extremely challenging in the dynamic cloud environment that host the component cloud services. However, existing approaches are constrained by their lack of modeling and analysis of system components’ fluctuating reliability time series. To systematically address these issues, in this article, we propose PARS, a perturbation-aware approach, for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services’ historical reliability time series. Then, it calculates the reliability sensitivity of the component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. Based on PARS, we propose a proactive adaptation approach for constructing and operating composite cloud systems with 1-out-of-2 N-version Programming fault-tolerance. This approach takes the reliability sensitivity of component cloud services estimated by PARS as input to assure the reliability of the cloud system. The results of experiments conducted on two widely used datasets demonstrate the effectiveness and efficiency of the proposed approaches in ensuring the reliability of composite cloud systems.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Joint Deep Networks Based Multi-Source Feature Learning for QoS Prediction

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      Authors: Youhao Xia;Ding Ding;Zhenhua Chang;Fan Li;
      Pages: 2314 - 2327
      Abstract: The ever-increasing diversity and dynamic of cloud environment pose many challenges on QoS prediction in service recommendation. One such challenge is how to extract and learn deep features of users/services from multi-source information to improve prediction accuracy. In this article, we propose a novel Joint Deep Networks based Multi-source Feature Learning(JDNMFL) framework for QoS prediction. JDNMFL has two parts: Multi-source Feature Extraction and Feature Interaction Learning. In the first part, a latent factor embedding method is first proposed to capture implicit features from QoS matrix, and then the multi-source features, combined by explicit features from WSDL(Web Services Description Language) document and contextual data as well as implicit features, are extracted based on the combination of matrix factorization and neural networks. In the second part, the CNN(Convolutional Neural Network)-based joint deep networks are built to learn both local and global high-order feature interactions, and to complete the final QoS prediction based on mixed features. Experimental results demonstrate that our JDNMFL approach can not only extract and integrate implicit and explicit features of various multi-source data, but also can learn feature sequence and feature interactions, so that it is very effective in improving the accuracy of QoS prediction with sparse data.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Energy and Cost Efficient Resource Allocation for Blockchain-Enabled NFV

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      Authors: Shiva Kazemi Taskou;Mehdi Rasti;Pedro H. J. Nardelli;
      Pages: 2328 - 2341
      Abstract: Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators’ OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users’ service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users’ SFCs without the need for an NFVO. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously. To address this problem, we propose an Approximation-based Resource Allocation algorithm (ARA) using Majorization-Minimization approximation method to convexify NFV-RA problem. Furthermore, due to the high complexity of ARA algorithm, we propose a low complexity Hungarian-based Resource Allocation (HuRA) algorithm using Hungarian algorithm for server allocation. Through the simulation results, we show that our proposed ARA and HuRA algorithms achieve near-optimal performance with lower computational complexity. Also, ARA algorithm outperforms the existing algorithms in terms of number of active servers, energy consumption, and average latency. Moreover, the mining process is the foundation of BC tec-nology. In wireless networks, mining is performed by resource-limited mobile users. Since the mining process requires high computational complexity, miner users cannot perform it alone. So, in this article, we assume that miner users can perform mining process with participating of other users. For mining process, the problem of minimizing the energy consumption and cost of users’ processing resources is formulated as a linear programming problem that can be optimally solved in polynomial time.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Specification and Automated Analysis of Inter-Parameter Dependencies in
           Web APIs

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      Authors: Alberto Martin-Lopez;Sergio Segura;Carlos Müller;Antonio Ruiz-Cortés;
      Pages: 2342 - 2355
      Abstract: Web services often impose inter-parameter dependencies that restrict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification (OAS) provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. In this article, we present an approach for the specification and automated analysis of inter-parameter dependencies in web APIs. We first present a domain-specific language, called Inter-parameter Dependency Language (IDL), for the specification of dependencies among input parameters in web services. Then, we propose a mapping to translate an IDL document into a constraint satisfaction problem (CSP), enabling the automated analysis of IDL specifications using standard CSP-based reasoning operations. Specifically, we present a catalogue of seven analysis operations on IDL documents allowing to compute, for example, whether a given request satisfies all the dependencies of the service. Finally, we present a tool suite including an editor, a parser, an OAS extension, a constraint programming-aided library, and a test suite supporting IDL specifications and their analyses. Together, these contributions pave the way for a new range of specification-driven applications in areas such as code generation and testing.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Identifying Health Insurance Claim Frauds Using Mixture of Clinical
           Concepts

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      Authors: Md Enamul Haque;Mehmet Engin Tozal;
      Pages: 2356 - 2367
      Abstract: Patients depend on health insurance provided by the government systems, private systems, or both to utilize the high-priced healthcare expenses. This dependency on health insurance draws some healthcare service providers to commit insurance frauds. Although the number of such service providers is small, it is reported that the insurance providers lose billions of dollars every year due to frauds. In this article, we formulate the fraud detection problem over a minimal, definitive claim data consisting of medical diagnosis and procedure codes. We present a solution to the fraudulent claim detection problem using a novel representation learning approach, which translates diagnosis and procedure codes into Mixtures of Clinical Codes (MCC). We also investigate extensions of MCC using Long Short Term Memory networks and Robust Principal Component Analysis. Our experimental results demonstrate promising outcomes in identifying fraudulent records.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Partitioning Stateful Data Stream Applications in Dynamic Edge Cloud
           Environments

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      Authors: Shaoshuai Ding;Lei Yang;Jiannong Cao;Wei Cai;Mingkui Tan;Zhenyu Wang;
      Pages: 2368 - 2381
      Abstract: Computation partitioning is an important technique to improve the application performance by selectively offloading some computations from the mobile devices to the nearby edge cloud. In a dynamic environment in which the network bandwidth to the edge cloud may change frequently, the partitioning of the computation needs to be updated accordingly. The frequent updating of partitioning leads to high state migration cost between the mobile side and edge cloud. However, existing works don’t take the state migration overhead into consideration. Consequently, the partitioning decisions may cause significant network congestion and increase overall completion time tremendously. In this article, with considering the state migration overhead, we propose a set of novel algorithms to update the partitioning based on the changing network bandwidth. To the best of our knowledge, this is the first work on computation partitioning for stateful data stream applications in dynamic environments. The algorithms aim to alleviate the network congestion and minimize the make-span through selectively migrating state in dynamic edge cloud environments. Extensive simulations show our solution not only could selectively migrate state but also outperforms other classical benchmark algorithms in terms of make-span. The proposed model and algorithms will enrich the scheduling theory for stateful tasks, which has not been explored before.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • A Multi-View Deep Learning Approach for Predictive Business Process
           Monitoring

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      Authors: Vincenzo Pasquadibisceglie;Annalisa Appice;Giovanna Castellano;Donato Malerba;
      Pages: 2382 - 2395
      Abstract: The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces based on the knowledge learned from historical event logs. In this article, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Multi-Perspective Trust Management Framework for Crowdsourced IoT Services

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      Authors: Mohammed Bahutair;Athman Bouguettaya;Azadeh Ghari Neiat;
      Pages: 2396 - 2409
      Abstract: We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a multi-perspective trust model that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of attributes that contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a trust model for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Resource Provisioning and Allocation in Function-as-a-Service Edge-Clouds

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      Authors: Onur Ascigil;Argyrios G. Tasiopoulos;Truong Khoa Phan;Vasilis Sourlas;Ioannis Psaras;George Pavlou;
      Pages: 2410 - 2424
      Abstract: Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various “computation spots”, each with very limited capacity. In this article, we consider Function-as-a-Service (FaaS) edge-clouds where application providers deploy their latency-critical functions to process user requests with strict response time deadlines. In this setting, we investigate the problem of resource provisioning and allocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Identifying a Minimum Sequence of High-Level Changes Between Workflows

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      Authors: Wei Song;Fangfei Chen;Hans-Arno Jacobsen;Chengzhen Zhang;
      Pages: 2425 - 2438
      Abstract: Adaptive workflow management systems allow workflows to be changed in both the modeling and runtime stages, resulting in many workflow variants. Identifying a minimum sequence of high-level changes between two workflows represents a fundamental yet critical issue. The state-of-the-art approach utilizes digital logic to seek the optimal solution; however, this approach may face difficulties when advanced workflow patterns (e.g., loops) are involved, and it does not scale well. To address this problem, we first propose a naive approach that applies all valid changes to one workflow until the other workflow is found. Then, the approach is optimized from two aspects. First, we present advanced heuristics that significantly reduce the search space without pruning the optimal solution. Second, we employ the A$^ast$* search algorithm to direct the search procedure. Because the heuristic function used in the A$^ast$* algorithm is problem specific, we devise a consistent heuristic function to approximate the edit distance between two workflows, thereby accelerating the search. We implement our approach in a prototype tool and conduct extensive experiments on two data sets to evaluate its effectiveness and efficiency. The experimental results demonstrate that our approach outperforms the state of the art in terms of both application scope and scalability.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • A Survey on Web Service QoS Prediction Methods

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      Authors: Seyyed Hamid Ghafouri;Seyyed Mohsen Hashemi;Patrick C. K. Hung;
      Pages: 2439 - 2454
      Abstract: Nowadays, there are many Web services with similar functionality on the Internet. Users consider Quality of Service (QoS) of the services to select the best service from among them. The prediction of QoS values of the Web services and recommendations of the best service based on these values to the users is one of the major challenges in the web service area. Major studies in this field use collaboration filtering based methods for prediction. The paper introduced prediction methods and divided them into three main categories: memory-based methods, model-based methods, and Collaborative Filtering (CF) methods combined with other methods. In each category, some of the most famous studies were introduced, and then the problems and benefits of each category were reviewed. Finally, we have a discussion about these methods and propose suggestions for future works.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Web Service QoS Prediction via Collaborative Filtering: A Survey

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      Authors: Zibin Zheng;Xiaoli Li;Mingdong Tang;Fenfang Xie;Michael R. Lyu;
      Pages: 2455 - 2472
      Abstract: With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Evaluation Goals for Online Process Mining: A Concept Drift Perspective

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      Authors: Paolo Ceravolo;Gabriel Marques Tavares;Sylvio Barbon Junior;Ernesto Damiani;
      Pages: 2473 - 2489
      Abstract: Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent years, but there is no consensus on the exact set of properties these techniques have to achieve. This article fills the gap by identifying a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
  • Blockchain Security: A Survey of Techniques and Research Directions

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      Authors: Jiewu Leng;Man Zhou;J. Leon Zhao;Yongfeng Huang;Yiyang Bian;
      Pages: 2490 - 2510
      Abstract: Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey, we examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines.
      PubDate: July-Aug. 1 2022
      Issue No: Vol. 15, No. 4 (2022)
       
 
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