The Internet of Things and Big Data Analytics: integrated platforms and industry use cases
This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunc...
Gespeichert in:
| Weitere beteiligte Personen: | , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
2020
|
| Ausgabe: | First edition |
| Schlagwörter: | |
| Links: | https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=6222707 https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6222707 |
| Zusammenfassung: | This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights |
| Beschreibung: | An Auerbach Book |
| Umfang: | 1 Online-Ressource (xiii, 323 Seiten) |
| ISBN: | 9781000057355 9781003036739 |
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| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Author Biography -- Contributors -- 1 Taxonomy of Big Data and Analytics Solutions for Internet of Things -- 1.1 Introduction -- 1.1.1 IoT Emergence -- 1.1.2 IoT Architecture -- 1.1.2.1 Three Layers of IoT -- 1.1.2.2 IoT Devices -- 1.1.2.3 Cloud Server -- 1.1.2.4 End User -- 1.1.3 IoT Challenges -- 1.1.4 IoT Opportunities -- 1.1.4.1 IoT and the Cloud -- 1.1.4.2 IoT and Security -- 1.1.4.3 IoT at the Edge -- 1.1.4.4 IoT and Integration -- 1.1.5 IoT Applications -- 1.1.5.1 Real-Time Applications of IoT | |
| 505 | 8 | |a 1.1.6 Big Data and Analytics Solutions for IoT -- 1.1.6.1 Big Data in IoT -- 1.1.6.2 Big Data Challenges -- 1.1.6.3 Different Patterns of Data -- 1.7 Big Data Sources -- 1.7.1 Media -- 1.7.2 Business Data -- 1.7.2.1 Customer's Details -- 1.7.2.2 Transaction Details -- 1.7.2.3 Interactions -- 1.7.3 IoT Data -- 1.8 Big Data System Components -- 1.8.1 Data Acquisition (DAQ) -- 1.8.2 Data Retention -- 1.8.3 Data Transportation -- 1.8.4 Data Processing -- 1.8.5 Data Leverage -- 1.9 Big Data Analytics Types -- 1.9.1 Predictive Analytics -- 1.9.1.1 What Will Happen If ...? -- 1.9.2 Descriptive Analytics | |
| 505 | 8 | |a 1.9.2.1 What Has Happened? -- 1.9.3 Diagnostic Analytics -- 1.9.3.1 Why Did It Happen? -- 1.9.3.2 Real-Time Example -- 1.9.4 Prescriptive Analytics -- 1.9.4.1 What Should We Do about This? -- 1.10 Big Data Analytics Tools -- 1.10.1 Hadoop -- 1.10.1.1 Features of Hadoop -- 1.10.2 Apache Spark -- 1.10.3 Apache Storm -- 1.10.4 NoSQL Databases -- 1.10.5 Cassandra -- 1.10.6 RapidMiner -- 1.11 Conclusion -- References -- 2 Big Data Preparation and Exploration -- 2.1 Understanding Original Data Analysis -- 2.2 Benefits of Big Data Pre-Processing | |
| 505 | 8 | |a 2.3 Data Pre-Processing and Data Wrangling Techniques for IoT -- 2.3.1 Data Pre-Processing -- 2.3.2 Steps Involved in Data Pre-Processing -- 2.3.3 Typical Use of Data Wrangling -- 2.3.4 Data Wrangling versus ETL -- 2.3.5 Data Wrangling versus Data Pre-Processing -- 2.3.6 Major Challenges in Data Cleansing -- 2.4 Challenges in Big Data Processing -- 2.4.1 Data Analysis -- 2.4.2 Countermeasures for Big-Data-Related Issues -- 2.4.2.1 Increasing Collection Coverage -- 2.4.2.2 Dimension Reduction and Processing Algorithms -- 2.5 Opportunities of Big Data | |
| 505 | 8 | |a 2.5.1 Big Data in Biomedical Image Processing -- 2.5.2 Big Data Opportunity for Genome -- References -- 3 Emerging IoT-Big Data Platform Oriented Technologies -- 3.1 Introduction -- 3.2 Ubiquitous Wireless Communication -- 3.2.1 Ubiquitous Computing -- 3.2.1.1 Ubiquitous Architecture -- 3.2.1.2 Communication Technologies -- 3.2.1.3 Applications -- 3.3 Real-Time Analytics: Overview -- 3.3.1 Challenges in Real-Time Analytics -- 3.3.2 Real-Time Analytics Platforms -- 3.4 Cloud Computing -- 3.4.1 Cloud Computing Era -- 3.4.2 Relationship between IoT and Cloud | |
| 520 | |a This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights | ||
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| 700 | 1 | |a Poongodi, T. |4 edt | |
| 700 | 1 | |a Balusamy, Balamurugan |4 edt | |
| 700 | 1 | |a Khari, Manju |0 (DE-588)1206900520 |4 edt | |
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| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Author Biography -- Contributors -- 1 Taxonomy of Big Data and Analytics Solutions for Internet of Things -- 1.1 Introduction -- 1.1.1 IoT Emergence -- 1.1.2 IoT Architecture -- 1.1.2.1 Three Layers of IoT -- 1.1.2.2 IoT Devices -- 1.1.2.3 Cloud Server -- 1.1.2.4 End User -- 1.1.3 IoT Challenges -- 1.1.4 IoT Opportunities -- 1.1.4.1 IoT and the Cloud -- 1.1.4.2 IoT and Security -- 1.1.4.3 IoT at the Edge -- 1.1.4.4 IoT and Integration -- 1.1.5 IoT Applications -- 1.1.5.1 Real-Time Applications of IoT 1.1.6 Big Data and Analytics Solutions for IoT -- 1.1.6.1 Big Data in IoT -- 1.1.6.2 Big Data Challenges -- 1.1.6.3 Different Patterns of Data -- 1.7 Big Data Sources -- 1.7.1 Media -- 1.7.2 Business Data -- 1.7.2.1 Customer's Details -- 1.7.2.2 Transaction Details -- 1.7.2.3 Interactions -- 1.7.3 IoT Data -- 1.8 Big Data System Components -- 1.8.1 Data Acquisition (DAQ) -- 1.8.2 Data Retention -- 1.8.3 Data Transportation -- 1.8.4 Data Processing -- 1.8.5 Data Leverage -- 1.9 Big Data Analytics Types -- 1.9.1 Predictive Analytics -- 1.9.1.1 What Will Happen If ...? -- 1.9.2 Descriptive Analytics 1.9.2.1 What Has Happened? -- 1.9.3 Diagnostic Analytics -- 1.9.3.1 Why Did It Happen? -- 1.9.3.2 Real-Time Example -- 1.9.4 Prescriptive Analytics -- 1.9.4.1 What Should We Do about This? -- 1.10 Big Data Analytics Tools -- 1.10.1 Hadoop -- 1.10.1.1 Features of Hadoop -- 1.10.2 Apache Spark -- 1.10.3 Apache Storm -- 1.10.4 NoSQL Databases -- 1.10.5 Cassandra -- 1.10.6 RapidMiner -- 1.11 Conclusion -- References -- 2 Big Data Preparation and Exploration -- 2.1 Understanding Original Data Analysis -- 2.2 Benefits of Big Data Pre-Processing 2.3 Data Pre-Processing and Data Wrangling Techniques for IoT -- 2.3.1 Data Pre-Processing -- 2.3.2 Steps Involved in Data Pre-Processing -- 2.3.3 Typical Use of Data Wrangling -- 2.3.4 Data Wrangling versus ETL -- 2.3.5 Data Wrangling versus Data Pre-Processing -- 2.3.6 Major Challenges in Data Cleansing -- 2.4 Challenges in Big Data Processing -- 2.4.1 Data Analysis -- 2.4.2 Countermeasures for Big-Data-Related Issues -- 2.4.2.1 Increasing Collection Coverage -- 2.4.2.2 Dimension Reduction and Processing Algorithms -- 2.5 Opportunities of Big Data 2.5.1 Big Data in Biomedical Image Processing -- 2.5.2 Big Data Opportunity for Genome -- References -- 3 Emerging IoT-Big Data Platform Oriented Technologies -- 3.1 Introduction -- 3.2 Ubiquitous Wireless Communication -- 3.2.1 Ubiquitous Computing -- 3.2.1.1 Ubiquitous Architecture -- 3.2.1.2 Communication Technologies -- 3.2.1.3 Applications -- 3.3 Real-Time Analytics: Overview -- 3.3.1 Challenges in Real-Time Analytics -- 3.3.2 Real-Time Analytics Platforms -- 3.4 Cloud Computing -- 3.4.1 Cloud Computing Era -- 3.4.2 Relationship between IoT and Cloud |
| ctrlnum | (OCoLC)1164651369 (DE-599)BVBBV046801759 |
| discipline | Informatik |
| edition | First edition |
| format | Electronic eBook |
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| id | DE-604.BV046801759 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-11T14:47:41Z |
| institution | BVB |
| isbn | 9781000057355 9781003036739 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032210505 |
| oclc_num | 1164651369 |
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| owner | DE-1050 DE-91G DE-BY-TUM |
| owner_facet | DE-1050 DE-91G DE-BY-TUM |
| physical | 1 Online-Ressource (xiii, 323 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE FHD01_PQE_Kauf ZDB-30-PQE TUM_Einzelkauf |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press |
| record_format | marc |
| spellingShingle | The Internet of Things and Big Data Analytics integrated platforms and industry use cases Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Author Biography -- Contributors -- 1 Taxonomy of Big Data and Analytics Solutions for Internet of Things -- 1.1 Introduction -- 1.1.1 IoT Emergence -- 1.1.2 IoT Architecture -- 1.1.2.1 Three Layers of IoT -- 1.1.2.2 IoT Devices -- 1.1.2.3 Cloud Server -- 1.1.2.4 End User -- 1.1.3 IoT Challenges -- 1.1.4 IoT Opportunities -- 1.1.4.1 IoT and the Cloud -- 1.1.4.2 IoT and Security -- 1.1.4.3 IoT at the Edge -- 1.1.4.4 IoT and Integration -- 1.1.5 IoT Applications -- 1.1.5.1 Real-Time Applications of IoT 1.1.6 Big Data and Analytics Solutions for IoT -- 1.1.6.1 Big Data in IoT -- 1.1.6.2 Big Data Challenges -- 1.1.6.3 Different Patterns of Data -- 1.7 Big Data Sources -- 1.7.1 Media -- 1.7.2 Business Data -- 1.7.2.1 Customer's Details -- 1.7.2.2 Transaction Details -- 1.7.2.3 Interactions -- 1.7.3 IoT Data -- 1.8 Big Data System Components -- 1.8.1 Data Acquisition (DAQ) -- 1.8.2 Data Retention -- 1.8.3 Data Transportation -- 1.8.4 Data Processing -- 1.8.5 Data Leverage -- 1.9 Big Data Analytics Types -- 1.9.1 Predictive Analytics -- 1.9.1.1 What Will Happen If ...? -- 1.9.2 Descriptive Analytics 1.9.2.1 What Has Happened? -- 1.9.3 Diagnostic Analytics -- 1.9.3.1 Why Did It Happen? -- 1.9.3.2 Real-Time Example -- 1.9.4 Prescriptive Analytics -- 1.9.4.1 What Should We Do about This? -- 1.10 Big Data Analytics Tools -- 1.10.1 Hadoop -- 1.10.1.1 Features of Hadoop -- 1.10.2 Apache Spark -- 1.10.3 Apache Storm -- 1.10.4 NoSQL Databases -- 1.10.5 Cassandra -- 1.10.6 RapidMiner -- 1.11 Conclusion -- References -- 2 Big Data Preparation and Exploration -- 2.1 Understanding Original Data Analysis -- 2.2 Benefits of Big Data Pre-Processing 2.3 Data Pre-Processing and Data Wrangling Techniques for IoT -- 2.3.1 Data Pre-Processing -- 2.3.2 Steps Involved in Data Pre-Processing -- 2.3.3 Typical Use of Data Wrangling -- 2.3.4 Data Wrangling versus ETL -- 2.3.5 Data Wrangling versus Data Pre-Processing -- 2.3.6 Major Challenges in Data Cleansing -- 2.4 Challenges in Big Data Processing -- 2.4.1 Data Analysis -- 2.4.2 Countermeasures for Big-Data-Related Issues -- 2.4.2.1 Increasing Collection Coverage -- 2.4.2.2 Dimension Reduction and Processing Algorithms -- 2.5 Opportunities of Big Data 2.5.1 Big Data in Biomedical Image Processing -- 2.5.2 Big Data Opportunity for Genome -- References -- 3 Emerging IoT-Big Data Platform Oriented Technologies -- 3.1 Introduction -- 3.2 Ubiquitous Wireless Communication -- 3.2.1 Ubiquitous Computing -- 3.2.1.1 Ubiquitous Architecture -- 3.2.1.2 Communication Technologies -- 3.2.1.3 Applications -- 3.3 Real-Time Analytics: Overview -- 3.3.1 Challenges in Real-Time Analytics -- 3.3.2 Real-Time Analytics Platforms -- 3.4 Cloud Computing -- 3.4.1 Cloud Computing Era -- 3.4.2 Relationship between IoT and Cloud COMPUTERS / Networking / Intranets & Extranets bisacsh COMPUTERS / Data Transmission Systems / Wireless bisacsh |
| title | The Internet of Things and Big Data Analytics integrated platforms and industry use cases |
| title_auth | The Internet of Things and Big Data Analytics integrated platforms and industry use cases |
| title_exact_search | The Internet of Things and Big Data Analytics integrated platforms and industry use cases |
| title_full | The Internet of Things and Big Data Analytics integrated platforms and industry use cases edited by Pethuru Raj, T. Poongodi, Balamurugan Balusamy, and Manju Khari |
| title_fullStr | The Internet of Things and Big Data Analytics integrated platforms and industry use cases edited by Pethuru Raj, T. Poongodi, Balamurugan Balusamy, and Manju Khari |
| title_full_unstemmed | The Internet of Things and Big Data Analytics integrated platforms and industry use cases edited by Pethuru Raj, T. Poongodi, Balamurugan Balusamy, and Manju Khari |
| title_short | The Internet of Things and Big Data Analytics |
| title_sort | the internet of things and big data analytics integrated platforms and industry use cases |
| title_sub | integrated platforms and industry use cases |
| topic | COMPUTERS / Networking / Intranets & Extranets bisacsh COMPUTERS / Data Transmission Systems / Wireless bisacsh |
| topic_facet | COMPUTERS / Networking / Intranets & Extranets COMPUTERS / Data Transmission Systems / Wireless |
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