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Real Time Data Analytics For Large Scale Sensor Data

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Real Time Data Analytics for Large Scale Sensor Data

Real Time Data Analytics for Large Scale Sensor Data Book
Author : Himansu Das,Nilanjan Dey,Valentina Emilia Balas
Publisher : Academic Press
Release : 2019-08-31
ISBN : 0128182423
Language : En, Es, Fr & De

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Book Description :

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more. Examines IoT applications, the design of real-time intelligent systems, and how to manage the rapid growth of the large volume of sensor data Discusses intelligent management systems for applications such as healthcare, robotics and environment modeling Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale environments

Demand based Data Stream Gathering Processing and Transmission

Demand based Data Stream Gathering  Processing  and Transmission Book
Author : Jonas Traub
Publisher : BoD – Books on Demand
Release : 2021-04-09
ISBN : 3752671254
Language : En, Es, Fr & De

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Book Description :

This book presents an end-to-end architecture for demand-based data stream gathering, processing, and transmission. The Internet of Things (IoT) consists of billions of devices which form a cloud of network connected sensor nodes. These sensor nodes supply a vast number of data streams with massive amounts of sensor data. Real-time sensor data enables diverse applications including traffic-aware navigation, machine monitoring, and home automation. Current stream processing pipelines are demand-oblivious, which means that they gather, transmit, and process as much data as possible. In contrast, a demand-based processing pipeline uses requirement specifications of data consumers, such as failure tolerances and latency limitations, to save resources. Our solution unifies the way applications express their data demands, i.e., their requirements with respect to their input streams. This unification allows for multiplexing the data demands of all concurrently running applications. On sensor nodes, we schedule sensor reads based on the data demands of all applications, which saves up to 87% in sensor reads and data transfers in our experiments with real-world sensor data. Our demand-based control layer optimizes the data acquisition from thousands of sensors. We introduce time coherence as a fundamental data characteristic. Time coherence is the delay between the first and the last sensor read that contribute values to a tuple. A large scale parameter exploration shows that our solution scales to large numbers of sensors and operates reliably under varying latency and coherence constraints. On stream analysis systems, we tackle the problem of efficient window aggregation. We contribute a general aggregation technique, which adapts to four key workload characteristics: Stream (dis)order, aggregation types, window types, and window measures. Our experiments show that our solution outperforms alternative solutions by an order of magnitude in throughput, which prevents expensive system scale-out. We further derive data demands from visualization needs of applications and make these data demands available to streaming systems such as Apache Flink. This enables streaming systems to pre-process data with respect to changing visualization needs. Experiments show that our solution reliably prevents overloads when data rates increase.

Tributary

Tributary Book
Author : Yadid Ayzenberg
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

State of the art technology has made it possible to monitor various physiological signals for prolonged periods. Using wearable sensors, individuals can be monitored; sensor data can be collected and stored in digital format, transmitted to remote locations, and analyzed at later times. This technology may open the door to a multitude of exciting and innovative applications. We could learn the effects of the environment and of our day-to-day choices on our physiology. Does the number of hours we sleep affect our mood during the following day? Is our performance impacted by the times we schedule our recreational activities? Does physical activity affect our quality of sleep? Do these choices have an impact on chronic conditions? This proliferation of smart phones and wearable sensors is creating very large data sets that may contain useful information. Gartner claims that the Internet of Things Install Base Will Grow to 26 Billion Units By 2020. However, the magnitude of generated data creates new challenges as well. Processing and analyzing these large data sets in an efficient manner requires advanced computational tools. The challenge is that as more data are collected, it becomes more computationally expensive to process requiring novel algorithmic techniques and parallel architectures. Traditional analysis techniques do not scale adequately and in many cases researchers are required to create customized environments. This thesis explores and extends the affordances of warehouse scale computing for interactivity and pliability of large-scale time series data sets. In the first part of the thesis, I describe a theoretical framework for distributed processing of time-series data that is implementation invariant and may be implemented on an existing distributed computation infrastructure. Next, I present a detailed architecture and implementation of the theoretical framework, which was deployed on several clusters, as well as indepth analysis of the user-interface design considerations and the user experience design process. In the second part of the thesis, I present a system evaluation that consists of two parts. The first part is a quantitative characterization of the system performance in a variety of scenarios that included different dataset and cluster sizes. The second part contains the results of a qualitative user study: researchers were asked to use the system to analyze data that they had collected in their own studies and to participate in an ethnographic study on their experience. This study reveals that distributed computing holds great potential for accelerating scientific research utilizing large scale sensor data sets, providing new ways to see patterns in large sets of data, and much speedier analyses.

Machine Learning for Intelligent Decision Science

Machine Learning for Intelligent Decision Science Book
Author : Jitendra Kumar Rout,Minakhi Rout,Himansu Das
Publisher : Springer Nature
Release : 2020-04-02
ISBN : 9811536899
Language : En, Es, Fr & De

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Book Description :

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

Handbook of Large Scale Distributed Computing in Smart Healthcare

Handbook of Large Scale Distributed Computing in Smart Healthcare Book
Author : Samee U. Khan,Albert Y. Zomaya,Assad Abbas
Publisher : Springer
Release : 2017-08-07
ISBN : 3319582801
Language : En, Es, Fr & De

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Book Description :

This volume offers readers various perspectives and visions for cutting-edge research in ubiquitous healthcare. The topics emphasize large-scale architectures and high performance solutions for smart healthcare, healthcare monitoring using large-scale computing techniques, Internet of Things (IoT) and big data analytics for healthcare, Fog Computing, mobile health, large-scale medical data mining, advanced machine learning methods for mining multidimensional sensor data, smart homes, and resource allocation methods for the BANs. The book contains high quality chapters contributed by leading international researchers working in domains, such as e-Health, pervasive and context-aware computing, cloud, grid, cluster, and big-data computing. We are optimistic that the topics included in this book will provide a multidisciplinary research platform to the researchers, practitioners, and students from biomedical engineering, health informatics, computer science, and computer engineering.

Artificial Intelligence for the Internet of Health Things

Artificial Intelligence for the Internet of Health Things Book
Author : K. Shankar,Eswaran Perumal,Deepak Gupta
Publisher : CRC Press
Release : 2021-05-10
ISBN : 1000374297
Language : En, Es, Fr & De

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Book Description :

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

Smart Grid Using Big Data Analytics

Smart Grid Using Big Data Analytics Book
Author : Robert C. Qiu,Paul Antonik
Publisher : John Wiley & Sons
Release : 2017-04-17
ISBN : 1118494059
Language : En, Es, Fr & De

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Book Description :

This book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.

Service Oriented Computing

Service Oriented Computing Book
Author : Sami Yangui,Ismael Bouassida Rodriguez,Khalil Drira,Zahir Tari
Publisher : Springer Nature
Release : 2019-10-25
ISBN : 3030337022
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the 17th International Conference on Service-Oriented Computing, ICSOC 2019, held in Toulouse, France, in October 2019. The 28 full and 12 short papers presented together with 7 poster and 2 invited papers in this volume were carefully reviewed and selected from 181 submissions. The papers have been organized in the following topical sections: Service Engineering; Run-time Service Operations and Management; Services and Data; Services in the Cloud; Services on the Internet of Things; Services in Organizations, Business and Society; and Services at the Edge.

Big Data Management and Processing

Big Data Management and Processing Book
Author : Kuan-Ching Li,Hai Jiang,Albert Y. Zomaya
Publisher : CRC Press
Release : 2017-05-19
ISBN : 1498768083
Language : En, Es, Fr & De

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Book Description :

From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.

Computational Intelligence Applications in Business Intelligence and Big Data Analytics

Computational Intelligence Applications in Business Intelligence and Big Data Analytics Book
Author : Vijayan Sugumaran,Arun Kumar Sangaiah,Arunkumar Thangavelu
Publisher : CRC Press
Release : 2017-06-26
ISBN : 1351720252
Language : En, Es, Fr & De

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Book Description :

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry Book
Author : Patrick Bangert
Publisher : Gulf Professional Publishing
Release : 2021-03-04
ISBN : 0128209143
Language : En, Es, Fr & De

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Book Description :

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Online Engineering and Society 4 0

Online Engineering and Society 4 0 Book
Author : Michael E. Auer,Kalyan Ram Bhimavaram,Xiao-Guang Yue
Publisher : Springer Nature
Release : 2021-10-21
ISBN : 3030825299
Language : En, Es, Fr & De

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Book Description :

This book presents the general objective of the REV2021 conference which is to contribute and discuss fundamentals, applications, and experiences in the field of Online and Remote Engineering, Virtual Instrumentation, and other related new technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Nowadays, online technologies are the core of most fields of engineering and the whole society and are inseparably connected, for example, with Internet of Things, Industry 4.0 & Industrial Internet of Things, Cloud Technologies, Data Science, Cross & Mixed Reality, Remote Working Environments, Online & Biomedical Engineering, to name only a few. Since the first REV conference in 2004, we tried to focus on the upcoming use of the Internet for engineering tasks and the opportunities as well as challenges around it. In a globally connected world, the interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. Another objective of the conference is to discuss guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and Open Resources. REV2021 on "Online Engineering and Society 4.0" was the 17th in a series of annual events concerning the area of Remote Engineering and Virtual Instrumentation. It has been organized in cooperation with the International Engineering and Technology Institute (IETI) as an online event from February 24 to 26, 2021.

Next Generation Information Processing System

Next Generation Information Processing System Book
Author : Prachi Deshpande,Ajith Abraham,Brijesh Iyer,Kun Ma
Publisher : Springer Nature
Release : 2020-06-13
ISBN : 981154851X
Language : En, Es, Fr & De

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Book Description :

This book gathers high-quality research papers presented at the International Conference on Computing in Engineering and Technology (ICCET 2020) [formerly ICCASP], a flagship event in the area of engineering and emerging next-generation technologies jointly organized by the Dr. Babasaheb Ambedkar Technological University and MGM’s College of Engineering in Nanded, India, on 9-11 January 2020. Focusing on next-generation information processing systems, this second volume of the proceedings includes papers on cloud computing and information systems, artificial intelligence and the Internet of Things, hardware design and communication, and front-end design.

Maritime Technology and Engineering III

Maritime Technology and Engineering III Book
Author : Carlos Guedes Soares,T.A. Santos
Publisher : CRC Press
Release : 2016-12-01
ISBN : 1498795935
Language : En, Es, Fr & De

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Book Description :

Maritime Technology and Engineering 3 is a collection of papers presented at the 3rd International Conference on Maritime Technology and Engineering (MARTECH 2016, Lisbon, Portugal, 4-6 July 2016). The MARTECH Conferences series evolved from biannual national conferences in Portugal, thus reflecting the internationalization of the maritime sector. The keynote lectures and the papers, making up nearly 150 contributions, came from an international group of authors focused on different subjects in a variety of fields: Maritime Transportation, Energy Efficiency, Ships in Ports, Ship Hydrodynamics, Ship Structures, Ship Design, Ship Machinery, Shipyard Technology, afety & Reliability, Fisheries, Oil & Gas, Marine Environment, Renewable Energy and Coastal Structures. This book will appeal to academics, engineers and professionals interested or involved in these fields.

Data Analytics for Smart Cities

Data Analytics for Smart Cities Book
Author : Amir Alavi,William G. Buttlar
Publisher : CRC Press
Release : 2018-10-26
ISBN : 0429786638
Language : En, Es, Fr & De

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Book Description :

The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications.

Managing and Mining Sensor Data

Managing and Mining Sensor Data Book
Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
Release : 2013-01-15
ISBN : 1461463092
Language : En, Es, Fr & De

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Book Description :

Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.

Innovative Systems for Intelligent Health Informatics

Innovative Systems for Intelligent Health Informatics Book
Author : Faisal Saeed,Fathey Mohammed,Abdulaziz Al-Nahari
Publisher : Springer Nature
Release : 2021
ISBN : 303070713X
Language : En, Es, Fr & De

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Book Description :

This book presents the papers included in the proceedings of the 5th International Conference of Reliable Information and Communication Technology 2020 (IRICT 2020) that was held virtually on December 21-22, 2020. The main theme of the book is Innovative Systems for Intelligent Health Informatics. A total of 140 papers were submitted to the conference, but only 111 papers were published in this book. The book presents several hot research topics which include health informatics, bioinformatics, information retrieval, artificial intelligence, soft computing, data science, big data analytics, Internet of things (IoT), intelligent communication systems, information security, information systems, and software engineering. .

Big Data Analytics with Applications in Insider Threat Detection

Big Data Analytics with Applications in Insider Threat Detection Book
Author : Bhavani Thuraisingham,Pallabi Parveen,Mohammad Mehedy Masud,Latifur Khan
Publisher : CRC Press
Release : 2017-11-22
ISBN : 1498705480
Language : En, Es, Fr & De

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Book Description :

Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.

Data Mining and Big Data

Data Mining and Big Data Book
Author : Ying Tan,Yuhui Shi
Publisher : Springer
Release : 2016-07-04
ISBN : 3319409735
Language : En, Es, Fr & De

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Book Description :

The LNCS volume LNCS 9714 constitutes the refereed proceedings of the International Conference on Data Mining and Big Data, DMBD 2016, held in Bali, Indonesia, in June 2016. The 57 papers presented in this volume were carefully reviewed and selected from 115 submissions. The theme of DMBD 2016 is "Serving Life with Data Science". Data mining refers to the activity of going through big data sets to look for relevant or pertinent information.The papers are organized in 10 cohesive sections covering all major topics of the research and development of data mining and big data and one Workshop on Computational Aspects of Pattern Recognition and Computer Vision.

Control Applications in Modern Power System

Control Applications in Modern Power System Book
Author : Arun Kumar Singh,Manoj Tripathy
Publisher : Springer Nature
Release : 2020-11-26
ISBN : 9811588155
Language : En, Es, Fr & De

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Book Description :

This book presents select proceedings of the Electric Power and Renewable Energy Conference 2020 (EPREC 2020). This book provides rigorous discussions, case studies, and recent developments in emerging areas of control systems, especially, load frequency control, wide-area monitoring, control & instrumentation, optimization, intelligent control, energy management system, SCADA systems, etc. The contents of this book will be useful to researchers and professionals interested in control theory and its applications to power grids and systems. The book can also be used by policy makers and power engineers involved in power generation and distribution.