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Record Nr. |
UNISA996464402403316 |
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Titolo |
Big data platforms and applications : case studies, methods, techniques, and performance evaluation / / Florin Pop, Gabriel Neagu, editors |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (300 pages) |
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Collana |
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Computer Communications and Networks |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Acknowledgments -- Contents -- About the Editors -- 1 Data Center for Smart Cities: Energy and Sustainability Issue -- 1.1 Introduction -- 1.2 State-of-The-Art Overview -- 1.3 Methodology -- 1.3.1 Data Center Facilities and Dataset Description -- 1.3.2 Data Analysis -- 1.3.3 Metrics -- 1.3.4 Energy Waste Analysis -- 1.4 Results: DC Cluster Energy Consumption -- 1.4.1 Energy Use by Applications -- 1.4.2 Energy Analysis of Queues of Jobs -- 1.4.3 Energy Use by Parallel and Serial Jobs -- 1.4.4 Assessment of Useful Work -- 1.4.5 Assessment of Energy Waste -- 1.4.6 Sustainability Analysis -- 1.5 Discussion -- 1.5.1 Energy Efficiency Benefits and Concerns of Jobs Execution in Parallel Mode -- 1.5.2 Data Center Energy Efficiency Policies and Strategies -- 1.5.3 Sustainability-Oriented DC -- 1.6 Conclusion -- References -- 2 Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Processes -- 2.1 Introduction -- 2.2 Background -- 2.2.1 IoT for Smart Manufacturing Processes -- 2.2.2 Machine Learning Approaches for Manufacturing Process Analysis -- 2.2.3 Manufacturing Processes Optimization Literature Approaches -- 2.2.4 Bio-Inspired Techniques for Tuning the Parameters of Machine Learning Models -- 2.2.5 Approaches Used in Our Research for the Analysis of the Faults in Manufacturing Processes -- 2.3 Materials and Methods -- 2.3.1 Architectural Prototype for Simulating the Manufacturing of FL Series |
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Regulators -- 2.3.2 Machine Learning Methodology for Detecting Faulty Products in Discrete Manufacturing Processes -- 2.3.3 Data Preprocessing in KNIME (Konstanz Information Miner) -- 2.3.4 Discrete Manufacturing Processes Optimization Based on Big Data Technologies -- 2.4 Results -- 2.4.1 Description of the Datasets Used in Experiments -- 2.4.2 Classification Results -- 2.5 Discussion -- 2.6 Conclusions. |
References -- 3 An Empirical Study on Teleworking Among Slovakia's Office-Based Academics -- 3.1 Introduction -- 3.2 Methodology -- 3.3 Meaning of Telecommuting or Teleworking -- 3.3.1 Teleworking in Slovakia -- 3.4 Office-Based Teleworking Results -- 3.5 Discussion -- 3.6 Conclusions -- References -- 4 Data and Systems Heterogeneity: Analysis on Data, Processing, Workload, and Infrastructure -- 4.1 Introduction -- 4.2 Data Types, Formats, and Models -- 4.3 Processing Models and Platforms -- 4.4 Workload Types -- 4.5 Infrastructure Types -- 4.6 Conclusion -- References -- 5 exhiSTORY: Smart Self-organizing Exhibits -- 5.1 Introduction -- 5.2 The Stories Told by Exhibits -- 5.3 The Smart Exhibit -- 5.3.1 Centralized System Control -- 5.3.2 Automated Exhibit Geolocation -- 5.3.3 Security Aspects -- 5.3.4 Selecting an Implementation Option for Smart Exhibits -- 5.4 System Architecture -- 5.4.1 The Smart Space -- 5.4.2 The Knowledge Base -- 5.4.3 The Intelligent Modules -- 5.5 The exhiSTORY System in Operation -- 5.6 Discussion and Conclusions -- References -- 6 IoT Cloud Security Design Patterns -- 6.1 Introduction -- 6.2 Design of IoT Architecture Layers -- 6.2.1 Security Aspects -- 6.3 IoT Network Design Patterns -- 6.3.1 Security of IoT Networks -- 6.3.2 Design Patterns for a Secure IoT Network -- 6.4 IoT Cloud Platform Design Patterns -- 6.4.1 Security Division Pattern -- 6.4.2 Digital Twin Pattern -- 6.4.3 Secure Design Through Microservices -- 6.4.4 Push Notification Pattern -- 6.4.5 Cloud and Smartphone Management Pattern -- 6.4.6 Cloud-Assisted Network Access Pattern -- 6.5 Discussion and Conclusion -- References -- 7 Cloud-Based mHealth Streaming IoT Processing -- 7.1 Introduction -- 7.2 Overview of Underlying Technology for mHealth Solutions -- 7.3 Overview of IoT mHealth Solutions -- 7.4 Cloud-Based Architectures. |
7.5 Issues for Streaming mHealth IoT Solutions -- 7.6 Architectures for Streaming mHealth IoT Solutions -- 7.7 Discusion -- 7.7.1 Comparison of Architectural Concepts -- 7.7.2 Benefits -- 7.7.3 Use Case: A Monitoring Center Based on Streaming IoT mHealth Solutions -- 7.8 Conclusion -- References -- 8 A System for Monitoring Water Quality Parameters in Rivers. Challenges and Solutions -- 8.1 Introduction -- 8.2 Water Quality Monitoring Systems Challenges -- 8.2.1 Water Quality Parameters Acquisition Using WSNs -- 8.2.2 Pollution Detection -- 8.2.3 Standards for Hydrographic and Monitoring Data -- 8.3 A Service-Based System Architecture for Water Quality Monitoring -- 8.3.1 Data Sources -- 8.3.2 Data Storage, Processing and Data Provision Services -- 8.3.3 Information Services -- 8.4 A Pollution Detection System for Somes River -- 8.4.1 Data Acquisitions and Storage -- 8.4.2 Discharge Computation -- 8.4.3 The Rule-Based Automatic Assessment of Water Quality and Pollution Alert Service -- 8.4.4 Simulation of Pollutant Propagation -- 8.4.5 The Water Quality Information Web Application -- 8.5 Conclusions -- References -- 9 A Survey on Privacy Enhancements for Massively Scalable Storage Systems in Public Cloud Environments -- 9.1 Introduction -- 9.2 Cloud Storage Encryption Prerequisites -- 9.3 Scalable Cloud Storage Encryption Schemes -- 9.4 Technology Survey Regarding Service Providers -- 9.5 Technology Survey Regarding Classic and Emerging Cryptographic Primitives -- 9.5.1 Confidentiality Primitives -- 9.5.2 Integrity Primitives -- 9.6 Technology Survey Regarding Third-Party Applications |
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-- 9.6.1 Viivo -- 9.6.2 AES Crypt -- 9.7 Proposed Solution -- 9.7.1 Architecture -- 9.7.2 General Description -- 9.7.3 The Java Card Applet -- 9.7.4 Storage Layout and Data Structures -- References. |
10 Energy Efficiency of Arduino Sensors Platform Based on Mobile-Cloud: A Bicycle Lights Use-Case -- 10.1 Introduction -- 10.2 Mobile Cloud Computing -- 10.3 The System for Energy Efficiency of Arduino Sensors -- 10.4 Smart Bicycle Lighting Architecture -- 10.5 Conclusions -- References -- 11 Cloud-Enabled Modeling of Sensor Networks in Educational Settings -- 11.1 Introduction -- 11.2 Related Work -- 11.2.1 Sensor Cloud -- 11.2.2 Education Cloud -- 11.3 Sensor Network Modeling -- 11.3.1 Language and Tools -- 11.3.2 Extensions and Model Interpreters -- 11.4 System Architecture -- 11.5 Educational Service in Cloud -- 11.5.1 Service Request and Handling -- 11.5.2 The Provisioning Process -- 11.6 Experimental Results -- 11.7 Conclusion -- References -- 12 Methods and Techniques for Automatic Identification System Data Reduction -- 12.1 Introduction -- 12.2 Related Work -- 12.3 AIS Technology -- 12.4 Algorithm Analysis -- 12.4.1 Analyzing the Data Set -- 12.5 Experimental Evaluation -- 12.5.1 Analyzed Data -- 12.5.2 Data Reduction Applied on AIS Data Set -- 12.5.3 Data Visualization -- 12.6 Conclusion and Future Work -- References -- 13 Machine-to-Machine Model for Water Resource Sharing in Smart Cities -- 13.1 Introduction -- 13.2 Current Stage of Development in the Field -- 13.2.1 EOMORES Project-Copernicus Platform -- 13.2.2 AquaWatch Project -- 13.2.3 SmartWater4Europe Project -- 13.2.4 OPC UA with MEGA Model Architecture -- 13.2.5 WATER-M Project -- 13.3 Smart City Water Management Available Technologies -- 13.3.1 GIS (Geographic Information System) -- 13.3.2 IBM Water Management Platform -- 13.3.3 TEMBOO Platform-IoT Applications -- 13.3.4 RoboMQ -- 13.4 Proposed Model and Possible Directions -- 13.5 Possibilities of Implementation -- 13.5.1 Message-Oriented Middleware-RabbitMQ -- 13.6 Conclusions -- References -- Index. |
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