Subjects -> BUSINESS AND ECONOMICS (Total: 3530 journals)
    - ACCOUNTING (132 journals)
    - BANKING AND FINANCE (306 journals)
    - BUSINESS AND ECONOMICS (1229 journals)
    - CONSUMER EDUCATION AND PROTECTION (20 journals)
    - COOPERATIVES (4 journals)
    - ECONOMIC SCIENCES: GENERAL (201 journals)
    - ECONOMIC SYSTEMS, THEORIES AND HISTORY (235 journals)
    - FASHION AND CONSUMER TRENDS (20 journals)
    - HUMAN RESOURCES (103 journals)
    - INSURANCE (26 journals)
    - INTERNATIONAL COMMERCE (145 journals)
    - INTERNATIONAL DEVELOPMENT AND AID (103 journals)
    - INVESTMENTS (22 journals)
    - LABOR AND INDUSTRIAL RELATIONS (61 journals)
    - MACROECONOMICS (17 journals)
    - MANAGEMENT (595 journals)
    - MARKETING AND PURCHASING (106 journals)
    - MICROECONOMICS (23 journals)
    - PRODUCTION OF GOODS AND SERVICES (143 journals)
    - PUBLIC FINANCE, TAXATION (37 journals)
    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals)

PRODUCTION OF GOODS AND SERVICES (143 journals)                     

Showing 1 - 137 of 137 Journals sorted alphabetically
Asia Pacific Journal of Marketing and Logistics     Hybrid Journal   (Followers: 8)
Asian Journal of Marketing     Open Access   (Followers: 6)
Australasian Marketing Journal (AMJ)     Hybrid Journal   (Followers: 4)
BMC Health Services Research     Open Access   (Followers: 26)
Capital Markets Law Journal     Hybrid Journal   (Followers: 4)
Cleaner Environmental Systems     Open Access  
Cleaner Production Letters     Hybrid Journal  
Cleaner Waste Systems     Open Access   (Followers: 9)
Consumption Markets & Culture     Hybrid Journal   (Followers: 6)
Customer Needs and Solutions     Hybrid Journal   (Followers: 4)
Direct Marketing An International Journal     Hybrid Journal   (Followers: 4)
Disaster Prevention and Management     Hybrid Journal   (Followers: 28)
Economic & Labour Market Review     Hybrid Journal   (Followers: 13)
Electronic Markets     Hybrid Journal   (Followers: 6)
Emerging Markets Review     Hybrid Journal   (Followers: 10)
European Journal of Marketing     Hybrid Journal   (Followers: 22)
Financial Markets, Institutions & Instruments     Hybrid Journal   (Followers: 38)
Food Packaging and Shelf Life     Hybrid Journal   (Followers: 3)
Foundations and Trends® in Marketing     Full-text available via subscription   (Followers: 12)
Future Business Journal     Open Access   (Followers: 2)
Global Journal of Emerging Market Economies     Hybrid Journal   (Followers: 1)
Health Services and Outcomes Research Methodology     Hybrid Journal   (Followers: 6)
Health Services Management Research     Hybrid Journal   (Followers: 16)
Health Services Research     Hybrid Journal   (Followers: 20)
i+Diseño : Revista científico-académica internacional de Innovación, Investigación y Desarrollo en Diseño     Open Access  
Independent Journal of Management & Production     Open Access   (Followers: 1)
Ingeniería y Competitividad     Open Access  
International Journal of Advanced Operations Management     Hybrid Journal   (Followers: 7)
International Journal of Bank Marketing     Hybrid Journal   (Followers: 4)
International Journal of Business and Emerging Markets     Hybrid Journal   (Followers: 1)
International Journal of Business Forecasting and Marketing Intelligence     Hybrid Journal   (Followers: 3)
International Journal of Electronic Marketing and Retailing     Hybrid Journal   (Followers: 5)
International Journal of Emerging Markets     Hybrid Journal   (Followers: 3)
International Journal of Entrepreneurial Venturing     Hybrid Journal   (Followers: 1)
International Journal of Financial Services Management     Hybrid Journal   (Followers: 1)
International Journal of Information Systems and Supply Chain Management     Full-text available via subscription   (Followers: 9)
International Journal of Inventory Research     Hybrid Journal  
International Journal of Lean Six Sigma     Hybrid Journal   (Followers: 8)
International Journal of Logistics Economics and Globalisation     Hybrid Journal   (Followers: 3)
International Journal of Managing Projects in Business     Hybrid Journal   (Followers: 3)
International Journal of Market Research     Hybrid Journal   (Followers: 14)
International Journal of Nonprofit & Voluntary Sector Marketing     Hybrid Journal   (Followers: 7)
International Journal of Pharmaceutical and Healthcare Marketing     Hybrid Journal   (Followers: 4)
International Journal of Planning and Scheduling     Hybrid Journal   (Followers: 2)
International Journal of Product Development     Hybrid Journal   (Followers: 1)
International Journal of Production Economics     Hybrid Journal   (Followers: 19)
International Journal of Production Management and Engineering     Open Access   (Followers: 4)
International Journal of Production Research     Hybrid Journal   (Followers: 13)
International Journal of Productivity and Quality Management     Hybrid Journal   (Followers: 4)
International Journal of Quality and Service Sciences     Hybrid Journal   (Followers: 2)
International Journal of Quality Innovation     Open Access   (Followers: 4)
International Journal of Research in Marketing     Hybrid Journal   (Followers: 18)
International Journal of Service Industry Management     Hybrid Journal   (Followers: 2)
International Journal of Services and Standards     Hybrid Journal   (Followers: 1)
International Journal of Services Operations and Informatics     Hybrid Journal   (Followers: 1)
International Journal of Services Sciences     Hybrid Journal  
International Journal of Supply Chain and Inventory Management     Hybrid Journal   (Followers: 6)
International Journal of Supply Chain and Operations Resilience     Hybrid Journal   (Followers: 2)
International Journal of Supply Chain Management     Open Access   (Followers: 14)
International Journal of Systems Science : Operations & Logistics     Hybrid Journal  
International Journal of Technology Marketing     Hybrid Journal   (Followers: 3)
International Journal of Trade and Global Markets     Hybrid Journal   (Followers: 2)
Internet Reference Services Quarterly     Hybrid Journal   (Followers: 33)
JCMS : Journal of Common Market Studies     Hybrid Journal   (Followers: 51)
Journal of Advances in Management Research     Hybrid Journal   (Followers: 1)
Journal of Benefit-Cost Analysis     Hybrid Journal   (Followers: 2)
Journal of Business & Industrial Marketing     Hybrid Journal   (Followers: 8)
Journal of Business Logistics     Hybrid Journal   (Followers: 8)
Journal of Business Venturing     Hybrid Journal   (Followers: 29)
Journal of Cleaner Production     Hybrid Journal   (Followers: 27)
Journal of Consumer Marketing     Hybrid Journal   (Followers: 19)
Journal of Database Marketing & Customer Strategy Management     Hybrid Journal   (Followers: 5)
Journal of Direct Data and Digital Marketing Practice     Hybrid Journal   (Followers: 7)
Journal of Emerging Knowledge on Emerging Markets     Open Access  
Journal of Entrepreneurial Finance     Open Access   (Followers: 1)
Journal of Financial Markets     Hybrid Journal   (Followers: 31)
Journal of Food Products Marketing     Hybrid Journal   (Followers: 1)
Journal of Foodservice Business Research     Hybrid Journal  
Journal of Global Marketing     Hybrid Journal   (Followers: 3)
Journal of Global Operations and Strategic Sourcing     Hybrid Journal   (Followers: 1)
Journal of Health Services Research and Policy     Hybrid Journal   (Followers: 16)
Journal of International Consumer Marketing     Hybrid Journal   (Followers: 9)
Journal of International Financial Markets, Institutions and Money     Hybrid Journal   (Followers: 19)
Journal of Loss Prevention in the Process Industries     Hybrid Journal   (Followers: 7)
Journal of Marketing     Full-text available via subscription   (Followers: 54)
Journal of Marketing Communications     Hybrid Journal   (Followers: 11)
Journal of Marketing Education     Hybrid Journal   (Followers: 7)
Journal of Marketing Research     Full-text available via subscription   (Followers: 74)
Journal of Nonprofit & Public Sector Marketing     Hybrid Journal   (Followers: 5)
Journal of Operations and Supply Chain Management     Open Access   (Followers: 5)
Journal of Political Marketing     Hybrid Journal   (Followers: 3)
Journal of Prediction Markets     Full-text available via subscription   (Followers: 1)
Journal of Product Innovation Management     Hybrid Journal   (Followers: 23)
Journal of Production Research & Management     Full-text available via subscription   (Followers: 3)
Journal of Productivity Analysis     Hybrid Journal   (Followers: 4)
Journal of Progressive Human Services     Hybrid Journal   (Followers: 1)
Journal of Public Policy & Marketing     Full-text available via subscription   (Followers: 14)
Journal of Relationship Marketing     Hybrid Journal   (Followers: 7)
Journal of Retailing and Consumer Services     Hybrid Journal   (Followers: 5)
Journal of Service Research     Hybrid Journal   (Followers: 6)
Journal of Services Marketing     Hybrid Journal   (Followers: 11)
Journal of Strategic Marketing     Hybrid Journal   (Followers: 9)
Journal of Targeting Measurement and Analysis for Marketing     Hybrid Journal   (Followers: 1)
Journal of Technology Management & Innovation     Open Access   (Followers: 5)
Journal of the Academy of Marketing Science     Hybrid Journal   (Followers: 26)
Journal of Vacation Marketing     Hybrid Journal   (Followers: 2)
Logistics     Open Access   (Followers: 1)
Logistics Journal     Open Access   (Followers: 2)
Management and Administrative Sciences Review     Open Access  
Management and Production Engineering Review     Open Access   (Followers: 1)
Manufacturing & Service Operations Management     Full-text available via subscription   (Followers: 18)
Marketing Intelligence & Planning     Hybrid Journal   (Followers: 4)
Marketing Letters     Hybrid Journal   (Followers: 10)
Marketing Review     Full-text available via subscription  
Marketing Science     Full-text available via subscription   (Followers: 37)
Psychological Services     Full-text available via subscription   (Followers: 4)
Psychology & Marketing     Hybrid Journal   (Followers: 11)
Qualitative Market Research: An International Journal     Hybrid Journal   (Followers: 3)
Quantitative Marketing and Economics     Hybrid Journal   (Followers: 4)
Reproduction Fertility and Development     Hybrid Journal   (Followers: 4)
Review of Pacific Basin Financial Markets and Policies     Hybrid Journal  
Revista Eletrônica Academicus     Open Access  
Revue Interventions économiques     Open Access   (Followers: 1)
Service Business     Hybrid Journal   (Followers: 1)
Service Oriented Computing and Applications     Hybrid Journal   (Followers: 2)
Service Science     Full-text available via subscription   (Followers: 1)
Services Marketing Quarterly     Hybrid Journal   (Followers: 5)
Social Marketing Quarterly     Hybrid Journal   (Followers: 6)
Strategy Management Logistics     Open Access   (Followers: 2)
Supply Chain Forum : an International Journal     Full-text available via subscription   (Followers: 6)
Sustainable Production and Consumption     Full-text available via subscription   (Followers: 1)
Technology Operation Management     Hybrid Journal  
The Journal of Futures Markets     Hybrid Journal   (Followers: 6)
The Service Industries Journal     Hybrid Journal   (Followers: 4)
Universal Journal of Industrial and Business Management     Open Access   (Followers: 1)
Venture Capital: An International Journal of Entrepreneurial Finance     Hybrid Journal   (Followers: 1)
WPOM - Working Papers on Operations Management     Open Access   (Followers: 1)

           

Similar Journals
Journal Cover
Service Oriented Computing and Applications
Journal Prestige (SJR): 0.339
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1863-2394 - ISSN (Online) 1863-2386
Published by Springer-Verlag Homepage  [2469 journals]
  • Detection of cyber attacks in smart grids using SVM-boosted machine
           learning models

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      Abstract: Abstract False data injection, sometimes known as FDI, is a common form of assault that is launched against smart grids. The faulty data detection methods that are now in use are unable to detect covert FDI attacks, making it impossible to do so (El Mrabet et al. 67:469–482, 2018). The detection of FDI assaults can be accomplished using a multitude of methods, one of which is machine learning. In this paper, six alternative supervised learning (SVM-FS) hybrid methods, each with its own set of six unique boosted and feature selection (FS) procedures, are analyzed. These strategies are based on SVM-boosting algorithmic frameworks. Using data obtained from the smart grid, an analysis of the adaptability of these approaches is carried out. The accuracy of each detection method in terms of its classification is what is used to evaluate them. When supervised learning and hybrid methods are utilized in a simulation exercise, the performance of the classification techniques that are used to detect FDI attacks is significantly improved.
      PubDate: 2022-09-20
       
  • On using CPSV-AP to publish public service descriptions as linked open
           data

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      Abstract: Abstract Public authorities worldwide provide a large number of Public Services (PS) to citizens, businesses and other authorities. In this context, they publish PS catalogues containing descriptions of these services, e.g. about cost, required documents, contact details, etc. Two main challenges in the design of PS catalogues are standardisation and interoperability. To address these challenges, the European Union (EU) has developed the Core Public Service Vocabulary (CPSV), as the proposed EU standard for PS modelling. CPSV-AP is an application profile of CPSV that uses linked data as an underpinning technology to exploit its benefits, e.g. interlinking PS descriptions with each other and with other web of data resources. Despite its potential however, there is only limited research on CPSV-AP use in practice. The aim of this paper is to devise a process for using CPSV-AP and conduct a pilot implementation, using this process, to investigate potential benefits or challenges from CPSV-AP practical usage. In the framework of this pilot implementation, we publish a set of PS descriptions, included in an official PS catalogue, as CPSV-AP compliant linked data. The contribution of this paper is a detailed process for CPSV-AP use in practice and relevant lessons learnt. We anticipate this research will be beneficial to both academics working on PS semantic interoperability and practitioners aiming to migrate existing PS catalogues exploiting CPSV-AP.
      PubDate: 2022-08-19
       
  • A sound response to long-tailed changes in business process management

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      Abstract: Abstract Enterprises rely on their process-aware information system (PAIS) to conduct business. Therefore, appropriately responding to environmental changes is vital for enterprises to maintain competitiveness. However, one type of change, namely the long-tailed change (LTC), has been overlooked by traditional business process management practice because of its variety and infrequency. Just as the long-tailed effect reveals, the impact of LTC on enterprise PAIS might be no less dramatic than the impact of high-frequency changes. Since business process models are core assets of an enterprise, it is profitable to reuse them efficiently while tackling the conflict of flexibility and applicability in a timely way. This paper proposes a process model maintenance approach to responding to LTCs. By supporting business analysts to add syntax-correct annotations to existing business process models, the approach achieves an agile, error-free, and low-cost mechanism for dealing with LTCs.
      PubDate: 2022-08-18
       
  • The process of risk management needs to evolve with the changing
           technology in the digital world

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      PubDate: 2022-08-12
       
  • A time interval-based approach for business process fragmentation over
           cloud and edge resources

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      Abstract: Abstract This paper presents an approach for fragmenting business processes over 2 types of complementary platforms referred to as cloud resources and edge resources. Fragmentation caters to the separate needs and requirements of business processes’ owners. Indeed, some owners prioritize the security of their fragmented processes over availability while others prioritize the reliability of their fragmented processes over performance. Despite its benefits, fragmentation raises many concerns like how to reduce communication delays between disparate fragments and how to maintain acceptable loads over all the distributed resources. To identify the necessary cloud and edge resources that would accommodate fragmented business processes, the approach resorts to Allen’s time algebra allowing to simultaneously reason over both resources’ availability-time intervals and processes’ use-time intervals. This reasoning covers a good range of time relations like overlaps, during, and meets, is aware of resources’ properties like limited-but-extensible, and satisfies business processes’ requirements like data freshness. The fragmentation approach, in this paper, is illustrated with a banking case-study, validated through a system developed on top of Google Colaboratory, and evaluated through a set of real experiments.
      PubDate: 2022-08-07
       
  • A cloud weather forecasting service and its relationship with anomaly
           detection

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      Abstract: Abstract The main goal of the anomaly detection analysis is to identify the observations that do not adhere to general patterns considered normal behavior. In distributed systems, anomaly detection helps manage and monitor the system’s performance. In the literature, several machine learning (ML) algorithms have been proposed to detect anomalies, each one returning anomalies according to the particular mechanism used in the search process. For a cloud orchestrator managing a data center, this paper aims to present new insights for its health monitoring based on the coupling of different anomaly detection techniques based on the portfolio principle. We do not compute the weather forecasting of the whole cloud but the weather forecasting of a single cloud system. The Portfolio’s principle executes several ML anomaly detection algorithms with different anomaly detection mechanisms. The idea is to detect a maximum number of anomalies, then consolidate the results according to their similarities to keep only the relevant anomalies from a system administrator’s point of view. We also offer a metric for the health of the cloud and a new cloud service, which consists of adding a barometer indicator that helps experts maintain the cloud in operational conditions and determine the quality of the analyzed cloud. The proposed cloud service allows us to compare the behavior of the cloud at different times, enabling us to trigger actions to remedy the problems of anomalies. On the experimental plan, and to validate our approach, we use datasets present in the traces of Alibaba’s Cloud 2018 that include about 4000 machines over eight days. Compared to a baseline algorithm, we show that the portfolio approach is more stringent with the quality of the detection of anomalies. We also demonstrate that our method can effectively help the cloud administrator predict cloud weather. Thus, for example, he can daily monitor the state of the cloud easily in a quasi-automatic way.
      PubDate: 2022-07-29
       
  • Description, discovery, and recommendation of Cloud services: a survey

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      Abstract: Abstract Cloud computing has become the most popular concept for on-demand delivery of Cloud computing services. Due to its high flexibility, many Cloud computing services are designed and implemented to meet the users’ needs and expectations. As a result, new challenges have emerged in the search for relevant Cloud services. In fact, the description, discovery, and recommendation of Cloud services are the main challenges of Cloud computing. It stems from the lack of standardization for the Cloud services description and publication, as well as the exponential growth in the number and functionality of Cloud services. Our objective in this paper is to present a comparative study of the different approaches that address the Cloud services description, discovery, and recommendation issues. This comparison study’s findings are separated into three parts. First, the approaches to Cloud service description have several flaws, such as the lack of a unified description that encompasses all types of Cloud services, the lack of a definition for several properties (such as Cloud characteristics, actors, and pricing model), and the failure to consider several critical SLA elements (for example, QoS guarantee, compensation, monitoring, notification, and termination). Second, we determined that web-based approaches, also known as crawling approaches, are the most likely to be adopted in the field of Cloud service discovery, in order to keep up with the ever-changing nature of Cloud computing. Hence, the crawling approach’s main purpose is to update the Cloud service registry with new services that are available on the internet. Existing crawling methods, on the other hand, have a set of shortcomings, including the types and categories of discovered Cloud services, the constant increase in web-published Cloud services, and the automatic updating of Cloud vocabulary. Finally, major Cloud service recommendation issues such as cold start, data sparsity, attack resistance, and diversity remain unaddressed.
      PubDate: 2022-07-14
      DOI: 10.1007/s11761-022-00343-7
       
  • Compositional testing of management conformance for multi-component
           enterprise applications

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      Abstract: Abstract The management of modern enterprise applications is automated by coordinating the deployment, configuration, enactment, and termination of their components. Choosing among different candidate implementations for a specified application component requires such implementations to conform to the specified management behaviour. This holds especially if we wish to ensure that the overall application management can continue as planned, or that no additional (potentially undesired) management activity gets enabled. To this end, we introduce a formal framework for testing “management conformance”, i.e., to test whether a candidate implementation can be managed according to the management protocol specifying the allowed management for a component. We also illustrate how our framework enables to run four different conformance tests, each providing a different trade-off between implementation freedom and guarantees on the overall application management. We formally prove that testing management conformance with constraints reducing implementation freedom results in preserving all already allowed management activities when implementing a specification by choosing a conforming implementation and that no additional (potentially undesired) management activity gets enabled. Finally, we assess our framework by means of a prototype implementation and its use in an experimental evaluation.
      PubDate: 2022-07-12
      DOI: 10.1007/s11761-022-00341-9
       
  • Shift from game-as-a-product to game-as-a-service research trends

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      PubDate: 2022-06-15
      DOI: 10.1007/s11761-022-00335-7
       
  • Speaker extraction network with attention mechanism for speech dialogue
           system

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      Abstract: Abstract Speech Dialogue System is currently widely used in various fields. Users can interact and communicate with the system through natural language. While in practical situations, there exist third-person background sounds and background noise interference in real dialogue scenes. This issue seriously damages the intelligibility of the speech signal and decreases speech recognition performance. To tackle this, in this paper, we exploit a speech separation method that can help us to separate target speech from complex multi-person speech. We propose a multi-task-attention mechanism, and we select TFCN as our audio feature extraction module. Based on the multi-task method, we use SI-SDR and cross-entropy speaker classification loss function for joint training, and then we use the attention mechanism to further excludes the background vocals in the mixed speech. We not only test our result in Distortion indicators SI-SDR and SDR, but also test with a speech recognition system. To train our model and demonstrate its effectiveness, we build a background vocal removal data set based on a common data set. Experimental results empirically show that our model significantly improves the performance of speech separation model.
      PubDate: 2022-06-13
      DOI: 10.1007/s11761-022-00340-w
       
  • CAM-based non-local attention network for weakly supervised fire detection

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      Abstract: Abstract Many available object detectors are already used in fire detection, such as Faster RCNN, SSD, YOLO, etc., to localize the fire in images. Although these approaches perform well, they require object-level annotations for training, which are manually labeled and very expensive. In this paper, we propose a method based on the Class Activation Map (CAM) and non-local attention to explore the Weakly Supervised Fire Detection (WSFD) given only image-level annotations. Specifically, we first train a deep neural network with non-local attention as the classifier for identifying fire and non-fire images. Then, we use the classifier to create a CAM for every fire image in the inference stage and finally generate a corresponding bounding box according to each connected domain of the CAM. To evaluate the availability of our method, a benchmark dataset named WS-FireNet is constructed, and comprehensive experiments are performed on the WS-FireNet dataset. The experimental results demonstrate that our approach is effective in image-level supervised fire detection.
      PubDate: 2022-06-10
      DOI: 10.1007/s11761-022-00336-6
       
  • Two-layers service middleware for non-smart IoT sensors: case studies on
           industrial applications

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      Abstract: Abstract In 2021, more than 5.5 million new devices will be connected to the Internet of things every day. The discovery and access of IoT sensors are difficult to adapt to the growth rate of machines. Especially for non-smart IoT sensors, due to their inability to actively match and lack of self-description, the existing IoT technology is challenging to find and access. This paper provides a two-layers service middleware for non-smart IoT sensors accessing. An edge-side middleware service is proposed to model and discover the non-smart IoT. A server-side IoT middleware service is proposed to encapsulate non-smart IoT sensors into standard web services for downstream applications. Then, the data from non-smart devices are then integrated into a knowledge graph-based database with unified web service APIs to solve the heterogeneity problem. The proposed method is applied to the two industrial projects of “rural sewage treatment automatic station service” and “material digital production line service” to solve the discovery and access of many non-smart IoT devices. This paper provides a novel and feasible technical way to discover and access non-smart IoT devices in two aspects of theoretical research and industrial practice.
      PubDate: 2022-06-09
      DOI: 10.1007/s11761-022-00339-3
       
  • A lattice LSTM-based framework for knowledge graph construction from power
           plants maintenance reports

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      Abstract: Abstract Historical experience plays a significant role in the intelligent maintenance of power plants. While maintaining power equipment, engineers would record the experience in maintenance documents called status reports. Through decades of maintenance, massive status reports have been accumulated. These text data contains rich knowledge about power equipment, and they can be a strong support for intelligent maintenance. However, to fully utilize the knowledge from these reports is not easy because of two main reasons. First, there are a huge amount of data, making it difficult to find the specific knowledge we want. Second, the knowledge contained in reports is unorganized, and few previous works have been attempted to automatically mine the knowledge from these text data. To address this problem, we propose an innovative framework for automatic construction and reasoning of Chinese knowledge graph toward intelligent maintenance of power plants. In this framework, the lattice LSTM and multi-grained lattice framework (MG lattice) are adopted to extract entities and relations respectively from text data. What’s more, we present a dataset for Chinese Named Entity Recognition, which contains four categories of entities and consists of 864 sentences from status reports. Comprehensive experiments are carried out on this dataset. The experimental results show that the lattice LSTM method is significantly superior to classic LSTM-CRF model on power plant maintenance data, implying the effectiveness and potential of our proposed framework.
      PubDate: 2022-06-07
      DOI: 10.1007/s11761-022-00338-4
       
  • A knowledge extraction framework for domain-specific application with
           simplified pre-trained language model and attention-based feature
           extractor

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      Abstract: Abstract With the advancement of industrial informatics, intelligent algorithms are increasingly applied in various industrial products and applications. In this paper, we proposed a knowledge extraction framework for domain-specific text. This framework can extract entities from text the subsequent tasks such as knowledge graph construction. The proposed framework contains three modules, namely domain feature pre-trained model, LSTM-based named entity recognition and the attention-based nested named entity recognition. The domain feature pre-trained model can effectively learn the features of domain corpus such as professional terms that are not included in the general domain corpus. Flat named entity recognition can use the vector from pre-trained model to obtain the entity from domain-specific text. The nested named entity recognition based on the attention mechanism and the weight sliding balance strategy can effectively identify entity types with higher nesting rates. The framework achieves good results in the field of nuclear power plant maintenance reports, and the methods for domain pre-trained model and LSTM-based flat named entity recognition have been successfully applied to practical tasks.
      PubDate: 2022-06-03
      DOI: 10.1007/s11761-022-00337-5
       
  • A deep reinforcement learning-based approach for pricing in the competing
           auction-based cloud market

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      Abstract: Abstract In the cloud market, there exist multiple cloud providers adopting auction-based mechanisms to offer cloud resources to users. These auction-based cloud providers need to compete against each other to maximize the profits by setting the cloud resource prices effectively. In this paper, we analyze how an auction-based cloud provider sets the auction price effectively when competing against other cloud providers in the evolutionary market where the amount of participated cloud users is changing. The pricing strategy is affected by many factors, such as the auction price of its opponents, the prices charged to users in the previous round, the bidding behavior of cloud users, and so on. Therefore, we model this problem as a partially observable Markov game and adopt a gradient-based multi-agent deep reinforcement learning algorithm to generate the competing pricing strategy. We also run extensive experiments to evaluate our pricing strategy against other five benchmark pricing strategies in the auction-based cloud market. The experimental results show that our generated pricing strategy can beat other pricing strategies in terms of long-term profits and the amount of participated users, and it can also learn cloud users’ marginal values and their choice of cloud providers effectively.
      PubDate: 2022-04-13
      DOI: 10.1007/s11761-022-00334-8
       
  • Community-based service ecosystem evolution analysis

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      Abstract: Abstract Services are flourishing dramatically, and continuously increasing interactions among them are resulting in a new phenomenon called “service ecosystems,” which has become a focus of academia and industry. Driven by technology innovation, changes in regulations, and changes in the competitive strategies of individual businesses, service ecosystems are constantly evolving. Service ecosystem evolution analysis is an emerging research problem of great significance. By analyzing service ecosystem evolution history, common evolution patterns can be identified, underlying driving forces can be discovered, and future evolution trends can be predicated so that service providers can adjust their competitive strategies in a timely manner to adapt to evolution trends. In this paper, a framework for identifying service ecosystem evolution patterns from the service community perspective is presented. First, following the approaches of community detection and community evolution analysis, time-series community evolution traces are identified from historical service ecosystem evolution, and a service community evolution prediction model is trained in accordance with such traces. Second, the prediction model is explained to show how different factors affect the evolution of service communities. Finally, an approach for assisting service providers in making business decisions is presented according to interpretable prediction results and prior domain knowledge. Experiments on a real-world dataset showed that this work can indeed provide business-level insights on service ecosystem evolution. Additionally, all the data and source code have been made fully open-source for service ecosystem researchers.
      PubDate: 2022-04-11
      DOI: 10.1007/s11761-022-00333-9
       
  • Publisher Correction to: A third-party replication service for dynamic
           hidden databases

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      Abstract: In the original publication of the article, the figure legends of Figs. 2 and 4 were published with an error, due to two missed corrections. The corrections had been provided by the authors during the proofing process, but were missed by the typesetters.
      PubDate: 2022-03-01
      DOI: 10.1007/s11761-021-00316-2
       
  • Access control based on entity matching for secure data sharing

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      Abstract: Abstract Useful information for analysis and learning purposes is often located in heterogeneous and autonomous sources. We consider the problem where data owners want to share data that has access control policies associated with it. Data sources share information relying on entity matching rules (Conditions for which two records from different sources are considered as a match, i.e., represent the same real-world object) between their contents. In this paper, we propose an entity matching-oriented and policy-oriented methodology to provide a secure data sharing framework. We present an algorithm for translating a query submitted against one schema into an augmented query for the other schema to capture concerned tuples, based on entity matching rules. Then, we provide a methodology to answer queries while preserving local access control policies and also avoiding any inference leakage that could result from entity matching. Furthermore, we adduce details on our implementation and describe the key findings of the experiments.
      PubDate: 2022-01-07
      DOI: 10.1007/s11761-021-00331-3
       
  • Data-driven method for mobile game publishing revenue forecast

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      Abstract: Abstract Games as a service is similar to software as a service, which provides players with game content on a continuous monetization model. Game revenue forecast is vital to game developers to make the right business decisions, such as determining the marketing budget, controlling the development cost, and setting up benchmarks for evaluating game publishing performance. How to make the revenue forecast and integrate it with the game publishing process is hard for small and medium-sized independent (indie) game developers. This includes all steps of the process, from forecasting to decision-making based on the results. This paper provides a data-driven method that uses the mobile game revenue forecast based on different time-series prediction models to drive the game publishing. We demonstrate how to use the data-driven method to guide an indie game studio to forecast revenue and then set the revenue forecast as the internal benchmark to drive game publishing. In practice, we involve a real game project from an indie game studio and provide guidance for one of their casual game projects. Then, based on the revenue forecast, we discuss how to set the revenue forecast as an internal benchmark and drive the actions for mobile game publishing. Finally, we make a conclusion on how our data-driven method can be used to drive mobile game publishing and also discuss future research work.
      PubDate: 2021-12-23
      DOI: 10.1007/s11761-021-00332-2
       
  • SPReaD: service-oriented process for reengineering and DevOps

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      Abstract: Abstract The reengineering of systems into a microservice-based architecture can be seen as an implementation of a service-oriented architecture (SOA). However, the deployment of SOA into an enterprise is a challenging task, as it may involve the modernization of mission-critical systems with high technical debt and high maintenance costs. To this end, a process is required to provide an appropriate set of steps and techniques that minimize risks and at the same time ensure the quality of the systems during the migration process. Thus, this work presents the Service-oriented Process for Reengineering and DevOps—SPReaD, an instantiation of the mainstream SOA methodology focusing on the reengineering of legacy systems integrating DevOps aspects for developing microservices systems. This process has been defined during a real software reengineering project of legacy systems from a Brazilian State Department of Taxation. The results obtained include a substantial improvement in the quality of the main taxation system used by the state, including not only code-related metrics but also performance improvements of the services offered, and a change in the methodology adopted by the software development team.
      PubDate: 2021-10-20
      DOI: 10.1007/s11761-021-00329-x
       
 
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