Edizione	[1st ed. 2020.]
Pubbl/distr/stampa	Cham, Switzerland : , : Springer, , [2020]
Descrizione fisica	1 online resource (XIII, 410 p. 153 illus., 113 illus. in color.)
Disciplina	005.7
Collana	Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico	Big data
ISBN	3-030-59065-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Big data -- Knowledge Discovery -- Query Languages -- Artificial Intelligent.-Machine Learning -- Data Warehousing -- Distributed System -- Visualization -- Data Management -- Multimedia Data. .
Record Nr.	UNINA-9910427718503321

Edizione	[1st ed. 2019.]
Pubbl/distr/stampa	Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica	1 online resource (XIII, 321 p. 164 illus., 80 illus. in color.)
Disciplina	005.7
Collana	Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico	Database management Data mining Arithmetic and logic units, Computer Computer system failures Artificial intelligence Database Management Data Mining and Knowledge Discovery Arithmetic and Logic Structures System Performance and Evaluation Artificial Intelligence
ISBN	3-030-27520-5
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Applications -- Detecting the Onset of Machine Failure Using Anomaly Detection Methods -- A Hybrid Architecture for Tactical and Strategic Precision Agriculture -- Urban analytics of big transportation data for supporting smart cities -- Patterns -- Frequent Item Mining When Obtaining Support is Costly -- Mining Sequential Pattern of Historical Purchases for E-Commerce Recommendation -- Discovering and Visualizing Efficient Patterns in Cost/Utility Sequences -- Efficient Row Pattern Matching using Pattern Hierarchies for Sequence OLAP -- Statistically Significant Discriminative Patterns Searching -- RDF and Streams -- Multidimensional Integration of RDF datasets -- RDFPartSuite: Bridging Physical and Logical RDF Partitioning -- Mining quantitative temporal dependencies between interval-based streams -- Democratization of OLAP DSMS -- Big Data Systems -- Leveraging the Data Lake - Current State and Challenges -- SDWP: A New Data Placement Strategy for Distributed Big Data Warehouses in Hadoop -- Improved Programming-Language Independent MapReduce on Shared-Memory Systems -- Evaluating Redundancy and Partitioning of Geospatial Data in Document-Oriented Data Warehouses -- Graphs and Machine Learning -- Scalable Least Square Twin Support Vector Machine Learning -- Finding Strongly Correlated Trends in Dynamic Attributed Graphs -- Text-based Event Detection: Deciphering Date Information Using Graph Embeddings -- Efficiently Computing Homomorphic Matches of Hybrid Pattern Queries on Large Graphs -- Databases -- From Conceptual to Logical ETL Design using BPMN and Relational Algebra -- Accurate Aggregation Query-Result Estimation and Its Efficient Processing on Distributed Key-Value Store.
Record Nr.	UNINA-9910349305303321

Edizione	[1st ed. 2017.]
Pubbl/distr/stampa	Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Descrizione fisica	1 online resource (XIV, 488 p. 137 illus.)
Disciplina	005.7
Collana	Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico	Database management Data mining Application software Computer organization Artificial intelligence Database Management Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Systems Organization and Communication Networks Artificial Intelligence Computer Appl. in Social and Behavioral Sciences
ISBN	3-319-64283-9
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNINA-9910482967203321

Edizione	[1st ed. 2021.]
Pubbl/distr/stampa	Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica	1 online resource (XLII, 1830 p. 520 illus., 268 illus. in color.)
Disciplina	005.7
Collana	Advances in Intelligent Systems and Computing
Soggetto topico	Big data
ISBN	981-334-572-1
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNINA-9910484946003321

Pubbl/distr/stampa	Hoboken, New Jersey : , : Wiley, , [2021]
Descrizione fisica	1 online resource (xx, 376 pages) : illustrations
Disciplina	005.7
Soggetto topico	Big data
ISBN	1-119-74077-0 1-119-74078-9 1-119-74076-2
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- List of Abbreviations -- Chapter 1 Big Data Analytics for the Internet of Things: An Overview -- Chapter 2 Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3) -- 2.1 Context -- 2.2 Models in the Background -- 2.3 Problem Space: Are We Asking the Correct Questions? -- 2.4 Solutions Approach: The Elusive Quest to Build Bridges Between Data and Decisions -- 2.5 Avoid This Space: The Deception Space -- 2.6 Explore the Solution Space: Necessary to Ask Questions That May Not Have Answers, Yet -- 2.7 Solution Economy: Will We Ever Get There? -- 2.8 Is This Faux Naïveté in Its Purest Distillate? -- 2.9 Reality Check: Data Fusion -- 2.10 "Double A" Perspective of Data and Tools vs. The Hypothetical Porous Pareto (80/20) Partition -- 2.11 Conundrums -- 2.12 Stigma of Partition vs. Astigmatism of Vision -- 2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI -- 2.14 In Service of Society -- 2.15 Data Science in Service of Society: Knowledge and Performance from PEAS -- 2.16 Temporary Conclusion -- Acknowledgements -- References -- Chapter 3 Machine Learning Techniques for IoT Data Analytics -- 3.1 Introduction -- 3.2 Taxonomy of Machine Learning Techniques -- 3.2.1 Supervised ML Algorithm -- 3.2.1.1 Classification -- 3.2.1.2 Regression Analysis -- 3.2.1.3 Classification and Regression Tasks -- 3.2.2 Unsupervised Machine Learning Algorithms -- 3.2.2.1 Clustering -- 3.2.2.2 Feature Extraction -- 3.2.3 Conclusion -- References -- Chapter 4 IoT Data Analytics Using Cloud Computing -- 4.1 Introduction -- 4.2 IoT Data Analytics -- 4.2.1 Process of IoT Analytics -- 4.2.2 Types of Analytics -- 4.3 Cloud Computing for IoT -- 4.3.1 Deployment Models for Cloud. 4.3.1.1 Private Cloud -- 4.3.1.2 Public Cloud -- 4.3.1.3 Hybrid Cloud -- 4.3.1.4 Community Cloud -- 4.3.2 Service Models for Cloud Computing -- 4.3.2.1 Software as a Service (SaaS) -- 4.3.2.2 Platform as a Service (PaaS) -- 4.3.2.3 Infrastructure as a Service (IaaS) -- 4.3.3 Data Analytics on Cloud -- 4.4 Cloud-Based IoT Data Analytics Platform -- 4.4.1 Atos Codex -- 4.4.2 AWS IoT -- 4.4.3 IBM Watson IoT -- 4.4.4 Hitachi Vantara Pentaho, Lumada -- 4.4.5 Microsoft Azure IoT -- 4.4.6 Oracle IoT Cloud Services -- 4.5 Machine Learning for IoT Analytics in Cloud -- 4.5.1 ML Algorithms for Data Analytics -- 4.5.2 Types of Predictions Supported by ML and Cloud -- 4.6 Challenges for Analytics Using Cloud -- 4.7 Conclusion -- References -- Chapter 5 Deep Learning Architectures for IoT Data Analytics -- 5.1 Introduction -- 5.1.1 Types of Learning Algorithms -- 5.1.1.1 Supervised Learning -- 5.1.1.2 Unsupervised Learning -- 5.1.1.3 Semi-Supervised Learning -- 5.1.1.4 Reinforcement Learning -- 5.1.2 Steps Involved in Solving a Problem -- 5.1.2.1 Basic Terminology -- 5.1.2.2 Training Process -- 5.1.3 Modeling in Data Science -- 5.1.3.1 Generative -- 5.1.3.2 Discriminative -- 5.1.4 Why DL and IoT? -- 5.2 DL Architectures -- 5.2.1 Restricted Boltzmann Machine -- 5.2.1.1 Training Boltzmann Machine -- 5.2.1.2 Applications of RBM -- 5.2.2 Deep Belief Networks (DBN) -- 5.2.2.1 Training DBN -- 5.2.2.2 Applications of DBN -- 5.2.3 Autoencoders -- 5.2.3.1 Training of AE -- 5.2.3.2 Applications of AE -- 5.2.4 Convolutional Neural Networks (CNN) -- 5.2.4.1 Layers of CNN -- 5.2.4.2 Activation Functions Used in CNN -- 5.2.5 Generative Adversarial Network (GANs) -- 5.2.5.1 Training of GANs -- 5.2.5.2 Variants of GANs -- 5.2.5.3 Applications of GANs -- 5.2.6 Recurrent Neural Networks (RNN) -- 5.2.6.1 Training of RNN -- 5.2.6.2 Applications of RNN. 5.2.7 Long Short-Term Memory (LSTM) -- 5.2.7.1 Training of LSTM -- 5.2.7.2 Applications of LSTM -- 5.3 Conclusion -- References -- Chapter 6 Adding Personal Touches to IoT: A User-Centric IoT Architecture -- 6.1 Introduction -- 6.2 Enabling Technologies for BDA of IoT Systems -- 6.3 Personalizing the IoT -- 6.3.1 Personalization for Business -- 6.3.2 Personalization for Marketing -- 6.3.3 Personalization for Product Improvement and Service Optimization -- 6.3.4 Personalization for Automated Recommendations -- 6.3.5 Personalization for Improved User Experience -- 6.4 Related Work -- 6.5 User Sensitized IoT Architecture -- 6.6 The Tweaked Data Layer -- 6.7 The Personalization Layer -- 6.7.1 The Characterization Engine -- 6.7.2 The Sentiment Analyzer -- 6.8 Concerns and Future Directions -- 6.9 Conclusions -- References -- Chapter 7 Smart Cities and the Internet of Things -- 7.1 Introduction -- 7.2 Development of Smart Cities and the IoT -- 7.3 The Combination of the IoT with Development of City Architecture to Form Smart Cities -- 7.3.1 Unification of the IoT -- 7.3.2 Security of Smart Cities -- 7.3.3 Management of Water and Related Amenities -- 7.3.4 Power Distribution and Management -- 7.3.5 Revenue Collection and Administration -- 7.3.6 Management of City Assets and Human Resources -- 7.3.7 Environmental Pollution Management -- 7.4 How Future Smart Cities Can Improve Their Utilization of the Internet of All Things, with Examples -- 7.5 Conclusion -- References -- Chapter 8 A Roadmap for Application of IoT-Generated Big Data in Environmental Sustainability -- 8.1 Background and Motivation -- 8.2 Execution of the Study -- 8.2.1 Role of Big Data in Sustainability -- 8.2.2 Present Status and Future Possibilities of IoT in Environmental Sustainability -- 8.3 Proposed Roadmap -- 8.4 Identification and Prioritizing the Barriers in the Process. 8.4.1 Internet Infrastructure -- 8.4.2 High Hardware and Software Cost -- 8.4.3 Less Qualified Workforce -- 8.5 Conclusion and Discussion -- References -- Chapter 9 Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids -- 9.1 Introduction -- 9.2 Applications of Synchrophasor Data -- 9.2.1 Voltage Stability Analysis -- 9.2.2 Transient Stability -- 9.2.3 Out of Step Splitting Protection -- 9.2.4 Multiple Event Detection -- 9.2.5 State Estimation -- 9.2.6 Fault Detection -- 9.2.7 Loss of Main (LOM) Detection -- 9.2.8 Topology Update Detection -- 9.2.9 Oscillation Detection -- 9.3 Utility Big Data Issues Related to PMU-Driven Applications -- 9.3.1 Heterogeneous Measurement Integration -- 9.3.2 Variety and Interoperability -- 9.3.3 Volume and Velocity -- 9.3.4 Data Quality and Security -- 9.3.5 Utilization and Analytics -- 9.3.6 Visualization of Data -- 9.4 Big Data Analytics Platforms for PMU Data Processing -- 9.4.1 Hadoop -- 9.4.2 Apache Spark -- 9.4.3 Apache HBase -- 9.4.4 Apache Storm -- 9.4.5 Cloud-Based Platforms -- 9.5 Conclusions -- References -- Chapter 10 Intelligent Enterprise-Level Big Data Analytics for Modeling and Management in Smart Internet of Roads -- 10.1 Introduction -- 10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification -- 10.2.1 Detection of the Bounding Box of the License Plate -- 10.2.2 Segmentation Objective -- 10.2.3 Spatial Invariances -- 10.2.4 Model Framework -- 10.2.4.1 Increasing the Layer of Transformation -- 10.2.4.2 Data Format of Sample Images -- 10.2.4.3 Applying Batch Normalization -- 10.2.4.4 Network Architecture -- 10.2.5 Role of Data -- 10.2.6 Synthesizing Samples -- 10.2.7 Invariances -- 10.2.8 Reducing Number of Features -- 10.2.9 Choosing Number of Classes -- 10.3 Experimental Setup and Results -- 10.3.1 Sparse Softmax Loss. 10.3.2 Mean Intersection Over Union -- 10.4 Practical Implementation of Enterprise-Level Big Data Analytics for Smart City -- 10.5 Conclusion -- References -- Chapter 11 Predictive Analysis of Intelligent Sensing and Cloud-Based Integrated Water Management System -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed Six-Tier Data Framework -- 11.3.1 Primary Components -- 11.3.2 Contact Unit (FC-37) -- 11.3.3 Internet of Things Communicator (ESP8266) -- 11.3.4 GSM-Based ARM and Control System -- 11.3.5 Methodology -- 11.3.6 Proposed Algorithm -- 11.4 Implementation and Result Analysis -- 11.4.1 Water Report for Home 1 and Home 2 Modules -- 11.5 Conclusion -- References -- Chapter 12 Data Security in the Internet of Things: Challenges and Opportunities -- 12.1 Introduction -- 12.2 IoT: Brief Introduction -- 12.2.1 Challenges in a Secure IoT -- 12.2.2 Security Requirements in IoT Architecture -- 12.2.2.1 Sensing Layer -- 12.2.2.2 Network Layer -- 12.2.2.3 Interface Layer -- 12.2.3 Common Attacks in IoT -- 12.3 IoT Security Classification -- 12.3.1 Application Domain -- 12.3.1.1 Authentication -- 12.3.1.2 Authorization -- 12.3.1.3 Depletion of Resources -- 12.3.1.4 Establishment of Trust -- 12.3.2 Architectural Domain -- 12.3.2.1 Authentication in IoT Architecture -- 12.3.2.2 Authorization in IoT Architecture -- 12.3.3 Communication Channel -- 12.4 Security in IoT Data -- 12.4.1 IoT Data Security: Requirements -- 12.4.1.1 Data: Confidentiality, Integrity, and Authentication -- 12.4.1.2 Data Privacy -- 12.4.2 IoT Data Security: Research Directions -- 12.5 Conclusion -- References -- Chapter 13 DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment -- 13.1 Introduction -- 13.1.1 State of the Art -- 13.1.2 Contribution -- 13.1.3 Organization -- 13.2 Cloud and DDoS Attack -- 13.2.1 Cloud Deployment Models. 13.2.1.1 Differences Between Private Cloud and Public Cloud.
Record Nr.	UNINA-9910554829703321