JournalTOCs

Distributed and Parallel Databases
Journal Prestige (SJR): 0.279

Citation Impact (citeScore): 1
Number of Followers: 2

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7578 - ISSN (Online) 0926-8782
Published by Springer-Verlag

[2469 journals]

BlockGraph: a scalable secure distributed ledger that exploits locality
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  Abstract: Abstract Distributed public ledgers, the key to modern cryptocurrencies and the heart of many novel applications, have scalability problems. Ledgers such as the blockchain underlying Bitcoin can process fewer than 10 transactions per second (TPS). The cost of transactions is high, and the time to confirm a transaction is in the minutes. We present the BlockGraph, a scalable distributed public ledger inspired by principles of computer architecture. The BlockGraph exploits the natural locality of transactions to allow publishing independent transactions in parallel. It extends the blockchain with three new transactions to create a unified consistent ledger out of essentially independent blockchains. The most important change is the introduction of the blockstamp transaction, which essentially checkpoints a local blockchain and secures it against attack. The result is a locality-based, simple, secure, sharding protocol which keeps all transactions readable. This paper introduces the BlockGraph protocol, proves that it is consistent and can achieve many thousands of TPS. Using our implementation (a small extension to Bitcoin core) we demonstrate that it, in practice, can significantly improve throughput.
  PubDate: 2022-05-21
OptSmart: a space efficient Optimistic concurrent execution of Smart
contracts
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  Abstract: Abstract Popular blockchains such as Ethereum and several others execute complex transactions in the block through user-defined scripts known as smart contracts. Serial execution of smart contract transactions/atomic units (AUs) fails to harness the multiprocessing power offered by the prevalence of multi-core processors. By adding concurrency to the execution of AUs, we can achieve better efficiency and higher throughput. In this paper, we develop a concurrent miner that proposes a block by executing AUs concurrently using optimistic Software Transactional Memory systems (STMs). It efficiently captures independent AUs in the concurrent bin and dependent AUs in the block graph (BG). Later, we propose a concurrent validator that re-executes the same AUs concurrently and deterministically using the concurrent bin followed by the BG given by the miner to verify the block. We rigorously prove the correctness of concurrent execution of AUs. The performance benchmark shows that the average speedup for the optimized concurrent miner is \(5.21 \times\) , while the maximum is \(14.96 \times\) over the serial miner. The optimized validator obtains an average speedup of \(8.61 \times\) to a maximum of \(14.65 \times\) over the serial validator. The proposed miner outperforms \(1.02 \times\) to \(1.18\times\) , while the proposed validator outperforms \(1 \times\) to \(4.46 \times\) over state-of-the-art concurrent miners and validators, respectively. Moreover, the proposed efficient BG saves an average of \(2.29 \times\) more block space when compared with the state-of-the-art.
  PubDate: 2022-05-09
BBoxDB streams: scalable processing of multi-dimensional data streams
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  Abstract: Abstract BBoxDB Streams is a distributed stream processing system, which allows the handling of multi-dimensional data. Multi-dimensional streams consist of n-dimensional elements, such as position data (e.g., two-dimensional positions of cars or three-dimensional positions of aircraft). The software is an enhancement of BBoxDB, a distributed key-bounding-box-value store that allows the handling of n-dimensional big data. BBoxDB Streams supports continuous range queries and continuous spatial joins; n-dimensional point and non-point data are supported. Operations in BBoxDB Streams are performed primarily on the bounding boxes of the data. With user-defined filters (UDFs), custom data formats can be decoded, and the bounding box-based operations are refined (e.g., a UDF decodes and performs intersection tests on the real geometries of WKT encoded stream elements). A unique feature of BBoxDB Streams is the ability to perform continuous spatial joins between stream elements and previously stored multi-dimensional big data. For example, the dynamic position of a car can be efficiently joined with the static spatial data of a street network.
  PubDate: 2022-05-02
Subscribing to big data at scale
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  Abstract: Abstract Today, data is being actively generated by a variety of devices, services, and applications. Such data is important not only for the information that it contains, but also for its relationships to other data and to interested users. Most existing Big Data systems focus on passively answering queries from users, rather than actively collecting data, processing it, and serving it to users. To satisfy both passive and active requests at scale, application developers need either to heavily customize an existing passive Big Data system or to glue one together with systems like Streaming Engines and Pub-sub services. Either choice requires significant effort and incurs additional overhead. In this paper, we present the BAD (Big Active Data) system as an end-to-end, out-of-the-box solution for this challenge. It is designed to preserve the merits of passive Big Data systems and introduces new features for actively serving Big Data to users at scale. We show the design and implementation of the BAD system, demonstrate how BAD facilitates providing both passive and active data services, investigate the BAD system’s performance at scale, and illustrate the complexities that would result from instead providing BAD-like services with a “glued” system.
  PubDate: 2022-04-07
MICAR: multi-inhabitant context-aware activity recognition in home
environments
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  Abstract: Abstract The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments enables several important applications, including the continuous monitoring of fragile subjects in their homes for healthcare systems. The majority of the approaches in the literature assume that only one resident is living in the home. Multi-inhabitant ADLs recognition is significantly more challenging, and only a limited effort has been devoted to address this setting by the research community. One of the major open problems is called data association, which is correctly associating each environmental sensor event (e.g., the opening of a fridge door) with the inhabitant that actually triggered it. Moreover, existing multi-inhabitant approaches rely on supervised learning, assuming a high availability of labeled data. However, collecting a comprehensive training set of ADLs (especially in multiple-residents settings) is prohibitive. In this work, we propose MICAR: a novel multi-inhabitant ADLs recognition approach that combines semi-supervised learning and knowledge-based reasoning. Data association is performed by semantic reasoning, combining high-level context information (e.g., residents’ postures and semantic locations) with triggered sensor events. The personalized stream of sensor events is processed by an incremental classifier, that is initialized with a limited amount of labeled ADLs. A novel cache-based active learning strategy is adopted to continuously improve the classifier. Our results on a dataset where up to 4 subjects perform ADLs at the same time show that MICAR reliably recognizes individual and joint activities while triggering a significantly low number of active learning queries.
  PubDate: 2022-04-05
A novel role-mapping algorithm for enhancing highly collaborative access
control system
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  Abstract: Abstract The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, Role-Based Access-Control (RBAC) model is deployed in large organizations. This paper addresses the scalability problem of the online stored rules. This problem affects the performance of the access control system due to increasing number of shared resources and/or number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with Role-To-Role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed Role-Mapping algorithm. A comparative study is performed to evaluate the proposed algorithm’s performance with concerning the Rule-Store size and the authorization response time. According to the results, it is found that the proposed algorithm reduces the number of stored rules which minimizes the Rule-Store size and reduces the authorization response time. Additionally, this paper proposes applying a concurrent approach on the RTR mapping model using the proposed Role-Mapping algorithm to achieve more savings in the authorization response time. Therefore, it will be suitable in highly-collaborative cloud environments.
  PubDate: 2022-03-31
Introduction to the special issue on self‑managing and
hardware‑optimized database systems 2020
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  PubDate: 2022-03-01
Deep multilayer percepted policy attribute Lamport certificateless
signcryption for secure data access and sharing in cloud
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  Abstract: Abstract Data sharing is a method that allows users to legally access data over the cloud. Cloud computing architecture is used to enable the data sharing capabilities only to the authorized users from the data stored in the cloud server. In the cloud, the number of users is extremely large and the users connect and leave randomly hence that the system needs to protect the data access. Many algorithms have been reviewed but the most challenging issue in cloud computing is a data-sharing system and access policy for an authorized user. A novel technique called Deep Multilayer Percepted Policy Attribute Lamport Certificateless Signcryption (DMPPALCS) is introduced for improving the security level of data access in the cloud with multiple layers. Initially, the users register their details to the cloud server for retrieving the numerous services. After that the cloud server generates the private and public keys for each registered user using Lamport Certificateless Signcryption. After the key generation, the user sends a request to the cloud server for acquiring the data. The cloud server validates that the requested user is authorized or not based on the policy attributes. Then the cloud server facilities the user requested data in the form of ciphertext and generates the signature to the cloud user. Finally, the signature is verified at the user side to decrypt the data. If the signature is valid, then the authorized users obtain the original data and improve secure data access. The proposed DMPPALCS technique is used for evaluating various security parameters.
  PubDate: 2022-03-01
Selective caching: a persistent memory approach for multi-dimensional
index structures
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  Abstract: Abstract After the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads.
  PubDate: 2022-03-01
Self-adapting data migration in the context of schema evolution in NoSQL
databases
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  Abstract: Abstract When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.
  PubDate: 2022-03-01
Hybridization of immune with particle swarm optimization in task
scheduling on smart devices
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  Abstract: Abstract The cloud environment allows enhanced task scheduling techniques for allocating tasks efficiently for smart devices. In this article, the task scheduling technique of artificial immune system (AIS), randomized gossip algorithm (RGA), and particle swarm optimization (PSO) implemented as proposed design to achieve uniform distribution in an optimized manner. The AIS technique is mainly focused on optimization and network security which is comprised of many applications. The peer-to-peer networks of sharing the information and make the interconnection possible are achieved by a RGA. For this kind of broadcasting the information, the RGA algorithms are mainly suitable. The PSO algorithm was executed for the independent task and allocated in a sensible self-organized way. The proposed method response time, performance ratio, and the makespan ratio defines as the total length of the schedule measured and compared with other time scheduling algorithms discussed later in this method. The above-proposed algorithm is used to allocate the resources efficiently even though the tasks have increased further. The comparative analysis of this proposed work was figured and tabulated. The decrease in makespan ratio, reduced response time, uniform distribution of tasks, no failures or crashes as disruption, and reduced overload make the proposed system optimized.
  PubDate: 2022-03-01
HTD: heterogeneous throughput-driven task scheduling algorithm in
MapReduce
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  Abstract: Abstract As one of the most popular parallel data processing models, data analysis system MapReduce has been widely used in many fields. Task scheduling is the core module in MapReduce system, and the quality of the scheduling algorithm directly affects the processing capacity of the system. Since new nodes need to be continuously added in the cluster to improve the processing capacity of the cluster, objectively, the heterogeneity of the cluster is caused. Heterogeneous environment is common in practical application scenarios, but there has been little research on task scheduling in heterogeneous environment. For this reason, this paper presents an in-depth study of task scheduling in heterogeneous environment and proposes a new task scheduling algorithm HTD. First, we give a formal definition of the throughput-driven task scheduling problem in a heterogeneous environment. Second, we design the scheduling algorithm HTD, which quickly obtains the completion sequence of a jobs set and optimizes the task scheduling details in heterogeneous environment. Finally, a series of experiments show the efficiency and effectiveness of the algorithm.
  PubDate: 2022-03-01
On the necessity of explicit cross-layer data formats in near-data
processing systems
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  Abstract: Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-Data processing (NDP) and a shift to code-to-data designs may represent a viable solution as packaging combinations of storage and compute elements on the same device has become feasible. The shift towards NDP system architectures calls for revision of established principles. ions such as data formats and layouts typically spread multiple layers in traditional DBMS, the way they are processed is encapsulated within these layers of abstraction. The NDP-style processing requires an explicit definition of cross-layer data formats and accessors to ensure in-situ executions optimally utilizing the properties of the underlying NDP storage and compute elements. In this paper, we make the case for such data format definitions and investigate the performance benefits under RocksDB and the COSMOS hardware platform.
  PubDate: 2022-03-01
A framework for discovering popular paths using transactional modeling and
pattern mining
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  Abstract: Abstract While the problems of finding the shortest path and k-shortest paths have been extensively researched, the research community has been shifting its focus towards discovering and identifying paths based on user preferences. Since users naturally follow some of the paths more than other paths, the popularity of a given path often reflects such user preferences. Given a set of user traversals in a road network and a set of paths between a given source and destination pair, we address the problem of performing top-k ranking of the paths in that set based on path popularity. In this paper, we introduce a new model for computing the popularity scores of paths. Our main contributions are threefold. First, we propose a framework for modeling user traversals in a road network as transactions. Second, we present an approach for efficiently computing the popularity score of any path based on the itemsets extracted from the transactions using pattern mining techniques. Third, we conducted an extensive performance evaluation with two real datasets to demonstrate the effectiveness of the proposed scheme.
  PubDate: 2022-03-01
MISS: finding optimal sample sizes for approximate analytics
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  Abstract: Abstract Nowadays, sampling-based Approximate Query Processing (AQP) is widely regarded as a promising way to achieve interactivity in big data analytics. To build such an AQP system, finding the minimal sample size for a query regarding given error constraints in general, called Sample Size Optimization (SSO), is an essential yet unsolved problem. Ideally, the goal of solving the SSO problem is to achieve statistical accuracy, computational efficiency and broad applicability all at the same time. Existing approaches either make idealistic assumptions on the statistical properties of the query, or completely disregard them. This may result in overemphasizing only one of the three goals while neglect the others. To overcome these limitations, we first examine carefully the statistical properties shared by common analytical queries. Then, based on the properties, we propose a linear model describing the relationship between sample sizes and the approximation errors of a query, which is called the error model. Then, we propose a Model-guided Iterative Sample Selection (MISS) framework to solve the SSO problem generally. Afterwards, based on the MISS framework, we propose a concrete algorithm, called \(L^{2}\textsc{Miss}\) , to find optimal sample sizes under the \(L^{2}\) norm error metric. Moreover, we extend the \(L^{2}\textsc{Miss}\) algorithm to handle other error metrics. Finally, we show theoretically and empirically that the \(L^{2}\textsc{Miss}\) algorithm and its extensions achieve satisfactory accuracy and efficiency for a considerably wide range of analytical queries.
  PubDate: 2022-03-01
Virtual machines pre-copy live migration cost modeling and prediction: a
survey
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  Abstract: Abstract Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers’ research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments.
  PubDate: 2021-12-06
Parallel query processing in a polystore
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  Abstract: Abstract The blooming of different data stores has made polystores a major topic in the cloud and big data landscape. As the amount of data grows rapidly, it becomes critical to exploit the inherent parallel processing capabilities of underlying data stores and data processing platforms. To fully achieve this, a polystore should: (i) preserve the expressivity of each data store’s native query or scripting language and (ii) leverage a distributed architecture to enable parallel data integration, i.e. joins, on top of parallel retrieval of underlying partitioned datasets. In this paper, we address these points by: (i) using the polyglot approach of the CloudMdsQL query language that allows native queries to be expressed as inline scripts and combined with SQL statements for ad-hoc integration and (ii) incorporating the approach within the LeanXcale distributed query engine, thus allowing for native scripts to be processed in parallel at data store shards. In addition, (iii) efficient optimization techniques, such as bind join, can take place to improve the performance of selective joins. We evaluate the performance benefits of exploiting parallelism in combination with high expressivity and optimization through our experimental validation.
  PubDate: 2021-12-01
Distributed arrays: an algebra for generic distributed query processing
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  Abstract: Abstract We propose a simple model for distributed query processing based on the concept of a distributed array. Such an array has fields of some data type whose values can be stored on different machines. It offers operations to manipulate all fields in parallel within the distributed algebra. The arrays considered are one-dimensional and just serve to model a partitioned and distributed data set. Distributed arrays rest on a given set of data types and operations called the basic algebra implemented by some piece of software called the basic engine. It provides a complete environment for query processing on a single machine. We assume this environment is extensible by types and operations. Operations on distributed arrays are implemented by one basic engine called the master which controls a set of basic engines called the workers. It maps operations on distributed arrays to the respective operations on their fields executed by workers. The distributed algebra is completely generic: any type or operation added in the extensible basic engine will be immediately available for distributed query processing. To demonstrate the use of the distributed algebra as a language for distributed query processing, we describe a fairly complex algorithm for distributed density-based similarity clustering. The algorithm is a novel contribution by itself. Its complete implementation is shown in terms of the distributed algebra and the basic algebra. As a basic engine the Secondo system is used, a rich environment for extensible query processing, providing useful tools such as main memory M-trees, graphs, or a DBScan implementation.
  PubDate: 2021-12-01
Finding the most profitable candidate product by dynamic skyline and
parallel processing
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  Abstract: Abstract Given a set of existing products in the market and a set of customer preferences, we set a price for a specific product selected from a pool of candidate products to launch to market to gain the most profit. A customer preference represents his/her basic requirements. The dynamic skyline of a customer preference identifies the products that the customer may purchase. Each time the price of a candidate product is adjusted, it needs to compete with all of the existing products to determine whether it can be one of the dynamic skyline products of some customer preferences. To compute in parallel, we use a Voronoi-Diagram-based partitioning method to separate the set of existing products and that of customer preferences into cells. For these cells, a large number of combinations can be generated. For each price under consideration of a candidate product, we process all the combinations in parallel to determine whether this candidate product can be one of the dynamic skyline products of the customer preferences. We then integrate the results to decide the price for each candidate product to achieve the most profit. To further improve the performance, we design two efficient pruning strategies to avoid computing all combinations. A set of experiments using real and synthetic datasets are performed and the experiment results reveal that the pruning strategies are effective.
  PubDate: 2021-12-01
An intelligent surveillance video analytics framework using
NACT-Hadoop/MapReduce on cloud services
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  Abstract: Abstract Video analytics has gradually increased in recent years. The intelligent CCTV cameras in public places, you-tube videos, etc. generate an enormous amount of video data. Generally, video analytics required more time as it contains several processes like encoding, decoding, etc. There are several existing approaches are evolved in improving the efficiency of video analytics but performance delay and loss of data still existing challenges. With our analysis, we strongly state VM migration will be an effective solution to overcome this delay and performance issues. In this paper, we propose NACT based map reducing mechanism (NACT-Map) for processing the real-time streaming videos. The NACT (Novel Awaiting Computation Time) enables the prediction of VM allocation and automatic migration. The scheduling and allocating of the optimal resource are done by task monitor who utilizes the Task manager (TM) system. The NACT based VM migration and MapReduce technique with Hadoop simplifies the process and minimizes the execution time. The splitting of video into chunks of frames speedup the process. Further efficiency is improved by the Map Reduce technique which uses video and its related content for clusters. The performance of our proposed system is executed in the cloudsim with a large dataset contains two real-time videos. Further, the result is compared with the existing methodologies such as distributed video decoding mechanism with extended FFmpeg and VideoRecordReader (VDMFF) (Yoon et al. in Distributed video decoding on Hadoop. IEICE Trans Inf Syst E101-D(1):2933–2941, 2018) and distributed Video Analytics Framework for Intelligent Video Surveillance (SIAT) (Uddin et al. in SIAT: a distributed video analytics framework for intelligent video surveillance. Symmetry 11:911, 2019). The obtained result shows our proposed NACT_Map consumes minimum Task processing time \(({\text{p}}_{{{\text{tix}}}} )\) and about 90% of efficiency in overall system performance is increased.
  PubDate: 2021-12-01