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001482684 001__ 1482684
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001482684 019__ $$a1406018642
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001482684 020__ $$a9819960347$$q(electronic bk.)
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001482684 0247_ $$a10.1007/978-981-99-6034-7$$2doi
001482684 035__ $$aSP(OCoLC)1406409618
001482684 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO
001482684 049__ $$aISEA
001482684 050_4 $$aTK5105.8857$$bT68 2023
001482684 08204 $$a004.67/8$$223/eng/20231103
001482684 24500 $$aTowards the integration of IoT, cloud and big data :$$bservices, applications and standards /$$cVinay Rishiwal, Pramod Kumar, Anuradha Tomar, Priyan Malarvizhi Kumar, editors.
001482684 260__ $$aSingapore :$$bSpringer,$$c2023.
001482684 300__ $$a1 online resource (164 p.).
001482684 4901_ $$aStudies in Big Data ;$$vv.137
001482684 500__ $$a7.1 Advantages of Using Convolution Neural Network (CNN)
001482684 5050_ $$aIntro -- Preface -- Contents -- Editors and Contributors -- Introduction to Big Data Analytics -- 1 Introduction to Big Data -- 2 The Distinction Between Small and Big Data -- 3 Classification of Big Data -- 4 Characteristics of Big Data -- 5 Who's Generating Big Data? -- 6 Why Is Big Data Important? -- 7 Challenges in Big-Data -- 8 Big Data Applications -- 9 How Big Data Analysis Differs from Business Intelligence Analysis? -- 9.1 Business Intelligence -- 9.2 Big Data -- 9.3 Differences Between Business Intelligence (BI) and Big Data -- 10 The Analytical Lifestyle of Big Data
001482684 5058_ $$a10.1 Phase 1: Discovery -- 10.2 Phase 2: Data Preparation -- 10.3 Phase 3: Model Planning -- 10.4 Phase 4: Model Building -- 10.5 Phase 5: Communicate Results -- 10.6 Phase 6: Operationalize -- 11 Big Data Analysis Necessitates a Set of Skills -- 12 Big Data Domain -- 13 Introduction to Big Data Analytics -- 14 Overview of the Hadoop Ecosystem -- 14.1 HDFS -- 14.2 YARN -- 14.3 MapReduce -- 14.4 Spark -- 15 Overview of Big Data Analysis and Its Need -- 16 Use Cases of Big Data Analytics -- 17 Challenges in Analyzing Big Data -- 18 Big Data Quality Dimensions -- 19 Conclusion -- References
001482684 5058_ $$aDCD_PREDICT: Using Big Data on Prediction for Chest Diseases by Applying Machine Learning Algorithms -- 1 Introduction -- 1.1 Introduction -- 1.2 Background -- 1.3 Objective -- 2 Literature Survey -- 2.1 Summary -- 3 System Design -- 3.1 Existing System -- 3.2 Identification of Common Risks -- 3.3 Types of Heart Diseases -- 3.4 Problem Statement -- 3.5 Scope -- 3.6 Proposed System -- 4 Methodology -- 4.1 Supervised Learning -- 4.2 Symptom-Based Questionnaire -- 4.3 Dataset Training and Testing -- 5 Process and Analysis -- 5.1 General Process -- 5.2 Use Case Diagram -- 5.3 Data Flow Diagram
001482684 5058_ $$a5.4 System Flow -- 6 Implementation and Results -- 6.1 Details of Algorithms -- 6.2 Data Set and Its Parameters -- 6.3 Dataset Attributes -- 6.4 Execution and Screenshots -- 7 Conclusion and Future Scope -- 7.1 Conclusion -- 7.2 Future Scope -- References -- Design of Energy Efficient IoMT Electrocardiogram (ECG) Machine on 28 nm FPGA -- 1 Introduction -- 2 Background -- 3 Environmental Settings for Energy Efficient IoMT ECG Machine -- 4 Power Analysis of IoMT ECG Machine -- 5 Conclusion -- References -- Automatic Smart Irrigation Method for Agriculture Data -- 1 Introduction -- 2 Motivation
001482684 5058_ $$a3 Contribution of the Chapter -- 4 Organization and Roadmap of the Article -- 5 Related Works -- 6 About the Dataset and Features -- 7 Methodology and Applied Algorithms -- 7.1 Data Processing -- 7.2 Machine Learning -- 8 Result and Analysis -- 9 Challenges in Proposed Work -- 10 Conclusion and Future Work -- References -- Artificial Intelligence Based Plant Disease Detection -- 1 Introduction -- 2 Motivation -- 3 Contribution of the Chapter -- 4 Organization of the Chapter -- 5 Literature Survey -- 6 Issues and Challenges -- 7 Methodology
001482684 506__ $$aAccess limited to authorized users.
001482684 520__ $$aThis book discusses integration of internet of things (IoT), cloud computing, and big data. It presents a unique platform where IoT, cloud computing, and big data are fused together and can be foreseen as a perfect solution to many applications. Usually, IoT, cloud computing, and big data are researched separately on the basis of their properties, underlying technologies, and other open issues. Integration of IoT, cloud computing and big data is not that easy and can face key open issues like standardization of interfaces, power and energy efficiency in both data processing and transmission, security and privacy, storage mechanisms for future applications, scalability and flexibility, and QoS provisioning for end user applications. Integration of IoT, cloud computing, and big data represents the next big rise for future industry and business applications. This integration opens new exhilarating directions for research and it is discussed in this book.
001482684 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 3, 2023).
001482684 650_6 $$aInternet des objets.
001482684 650_6 $$aInfonuagique.
001482684 650_6 $$aDonnées volumineuses.
001482684 650_0 $$aInternet of things.$$0(DLC)sh2013000266
001482684 650_0 $$aCloud computing.$$0(DLC)sh2008004883
001482684 650_0 $$aBig data.$$0(DLC)sh2012003227
001482684 655_0 $$aElectronic books.
001482684 7001_ $$aRishiwal, Vinay.
001482684 7001_ $$aKumar, Pramod.
001482684 7001_ $$aTomar, Anuradha.
001482684 7001_ $$aMalarvizhi Kumar, Priyan.
001482684 77608 $$iPrint version:$$aRishiwal, Vinay$$tTowards the Integration of IoT, Cloud and Big Data$$dSingapore : Springer,c2023$$z9789819960330
001482684 830_0 $$aStudies in big data ;$$vv. 137.
001482684 852__ $$bebk
001482684 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-6034-7$$zOnline Access$$91397441.1
001482684 909CO $$ooai:library.usi.edu:1482684$$pGLOBAL_SET
001482684 980__ $$aBIB
001482684 980__ $$aEBOOK
001482684 982__ $$aEbook
001482684 983__ $$aOnline
001482684 994__ $$a92$$bISE