Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Real Time Data Analytics For Large Scale Sensor Data
Download Real Time Data Analytics For Large Scale Sensor Data full books in PDF, epub, and Kindle. Read online Real Time Data Analytics For Large Scale Sensor Data ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Real-Time Data Analytics for Large Scale Sensor Data by : Himansu Das
Download or read book Real-Time Data Analytics for Large Scale Sensor Data written by Himansu Das and published by Academic Press. This book was released on 2019-08-31 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Demand-based Data Stream Gathering, Processing, and Transmission by : Jonas Traub
Download or read book Demand-based Data Stream Gathering, Processing, and Transmission written by Jonas Traub and published by BoD – Books on Demand. This book was released on 2021-04-09 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Big Data Analytics for Sensor-Network Collected Intelligence by : Hui-Huang Hsu
Download or read book Big Data Analytics for Sensor-Network Collected Intelligence written by Hui-Huang Hsu and published by Morgan Kaufmann. This book was released on 2017-02-02 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Contains contributions from noted scholars in computer science and electrical engineering from around the globe Provides a broad overview of recent developments in sensor collected intelligence Edited by a team comprised of leading thinkers in big data analytics
Download or read book Tributary written by Yadid Ayzenberg and published by . This book was released on 2016 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Improving Computational and Human Efficiency in Large-scale Data Analytics by : Kexin Rong
Download or read book Improving Computational and Human Efficiency in Large-scale Data Analytics written by Kexin Rong and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Network telemetry, sensor readings, and other machine-generated data are growing exponentially in volume. Meanwhile, the computational resources available for processing this data -- as well as analysts' ability to manually inspect it -- remain limited. As the gap continues to widen, keeping up with the data volumes is challenging for analytic systems and analysts alike. This dissertation introduces systems and algorithms that focus the limited computational resources and analysts' time in modern data analytics on a subset of relevant data. The dissertation comprises two parts that focus on improving the computational and human efficiency in data analytics, respectively. In the first part of this dissertation, we improve the computational efficiency of analytics by combining precomputation and sampling techniques to select a subset of data that contributes the most to query results. We demonstrate this concept with two approximate query processing systems. PS3 approximates aggregate SQL queries with weighted, partition-level samples based on precomputed summary statistics, whereas HBE approximates kernel density estimations using precomputed hash indexes as smart data samplers. Our evaluation shows that both systems outperform uniform sampling, the best-known result for these queries, with practical precomputation overheads. PS3 enables a 3 to 70x speedup under the same accuracy as uniform partition sampling, with less than 100 KB of storage overhead per partition; HBE offers up to a 10x improvements in query time compared to the second-best method with comparable precomputation time. In the second part of this dissertation, we improve the human efficiency of analytics by automatically identifying and summarizing unusual behaviors in large data streams to reduce the burden of manual inspections. We demonstrate this approach through two monitoring applications for machine-generated data. First, ASAP is a visualization operator that automatically smooths time series in monitoring dashboards to highlight large-scale trends and deviations. Compared to presenting the raw time series, ASAP decreases users' response time for identifying anomalies by up to 44.3% in our user study. We subsequently describe FASTer, an end-to-end earthquake detection system that we built in collaboration with seismologists at Stanford University. By pushing down domain-specific filtering and aggregation into the analytics workflows, FASTer significantly improves the speed and quality of earthquake candidate generation, scaling the analysis from three months of data from a single sensor to ten years of data over a network of sensors. The contributions of this dissertation have had real-world impact. ASAP has been incorporated into open-source tools such as Graphite, TimescaleDB Toolkit, and NPM module downsample. ASAP has also directly inspired an auto smoother for the real-time dashboards at the monitoring service Datadog. FASTer is open-source and has been used by researchers worldwide. Its improved scalability has enabled the discovery of hundreds of new earthquake events near the Diablo Canyon nuclear power plant in California.
Book Synopsis Computational Intelligence Applications in Business Intelligence and Big Data Analytics by : Vijayan Sugumaran
Download or read book Computational Intelligence Applications in Business Intelligence and Big Data Analytics written by Vijayan Sugumaran and published by CRC Press. This book was released on 2017-06-26 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Managing and Mining Sensor Data by : Charu C. Aggarwal
Download or read book Managing and Mining Sensor Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2013-01-15 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Large Scale Data Analytics by : Chung Yik Cho
Download or read book Large Scale Data Analytics written by Chung Yik Cho and published by Springer. This book was released on 2019-01-09 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.
Book Synopsis Data Science and Big Data Computing by : Zaigham Mahmood
Download or read book Data Science and Big Data Computing written by Zaigham Mahmood and published by Springer. This book was released on 2016-07-05 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.
Book Synopsis Real-Time Analytics by : Byron Ellis
Download or read book Real-Time Analytics written by Byron Ellis and published by John Wiley & Sons. This book was released on 2014-06-23 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.
Book Synopsis Smart Grid using Big Data Analytics by : Robert C. Qiu
Download or read book Smart Grid using Big Data Analytics written by Robert C. Qiu and published by John Wiley & Sons. This book was released on 2017-04-17 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Analytics for Smart Cities by : Amir Alavi
Download or read book Data Analytics for Smart Cities written by Amir Alavi and published by CRC Press. This book was released on 2018-10-26 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Smart Sensor Networks by : Umang Singh
Download or read book Smart Sensor Networks written by Umang Singh and published by Springer Nature. This book was released on 2021-09-01 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides IT professionals, educators, researchers, and students a compendium of knowledge on smart sensors and devices, types of sensors, data analysis and monitoring with the help of smart sensors, decision making, impact of machine learning algorithms, and artificial intelligence-related methodologies for data analysis and understanding of smart applications in networks. Smart sensor networks play an important role in the establishment of network devices which can easily interact with physical world through plethora of variety of sensors for collecting and monitoring the surrounding context and allowing environment information. Apart from military applications, smart sensor networks are used in many civilian applications nowadays and there is a need to manage high volume of demands in related applications. This book comprises of 9 chapters and presents a valuable insight on the original research and review articles on the latest achievements that contributes to the field of smart sensor networks and their usage in real-life applications like smart city, smart home, e-healthcare, smart social sensing networks, etc. Chapters illustrate technological advances and trends, examine research opportunities, highlight best practices and standards, and discuss applications and adoption. Some chapters also provide holistic and multiple perspectives while examining the impact of smart sensor networks and the role of data analytics, data sharing, and its control along with future prospects.
Book Synopsis Big Data Management and Processing by : Kuan-Ching Li
Download or read book Big Data Management and Processing written by Kuan-Ching Li and published by CRC Press. This book was released on 2017-05-19 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Cognitive Internet of Things by : Pethuru Raj
Download or read book Cognitive Internet of Things written by Pethuru Raj and published by CRC Press. This book was released on 2022-03-29 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Internet of Things (IoT) concept is defined as a flexible and futuristic network where all the different types of devices and smart objects can become seamlessly connected to each other and can actively participate in all types of processes which are happening around us. The grand objective of making physical, mechanical, electrical, and electronic devices to use the deeper and extreme connectivity and service-enablement techniques is to make them intelligent in their deeds, decisions, and deals. Cognitive IoT is the application of cognitive computing technologies to the data which is generated by the connected devices of the IoT ecosystem. Cognition means thinking; however, computers are not yet fully capable of mimicking human like thought. However, the present-day computer systems can perform some functions which are like the capability of human beings to think. Cognitive Internet of Things: Enabling Technologies, Platforms, and Use Cases explains the concepts surrounding Cognitive IoT. It also looks at the use cases and such supporting technologies as artificial intelligence and machine learning that act as key enablers of Cognitive IoT ecosystem. Different Cognitive IoT enabled platforms like IBM Watson and other product specific use cases like Amazon Alexa are covered in depth. Other highlights of the book include: Demystifying the cognitive computing paradigm Delineating the key capabilities of cognitive cloud environments Deep learning algorithms for cognitive IoT solutions Natural language processing (NLP) methods for cognitive IoT systems Designing a secure infrastructure for cognitive IoT platforms and applications
Book Synopsis Data Mining and Big Data by : Ying Tan
Download or read book Data Mining and Big Data written by Ying Tan and published by Springer. This book was released on 2016-07-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Transactions on Large-Scale Data- and Knowledge-Centered Systems LII by : Abdelkader Hameurlain
Download or read book Transactions on Large-Scale Data- and Knowledge-Centered Systems LII written by Abdelkader Hameurlain and published by Springer Nature. This book was released on 2022-09-27 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS journal Transactions on Large-Scale Data and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 52nd issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains 6 fully revised selected regular papers.
