The Scalation Time Series Database Support For Big Data Analytics

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The Scalation Time Series Database: Support for Big Data Analytics

Author : Santosh Uttam Bobade
Publisher :
ISBN 13 :
Total Pages : 98 pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis The Scalation Time Series Database: Support for Big Data Analytics by : Santosh Uttam Bobade

Download or read book The Scalation Time Series Database: Support for Big Data Analytics written by Santosh Uttam Bobade and published by . This book was released on 2018 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: The need to support large-scale time series data is increasing rapidly. There are emerg- ing Time Series Databases built with conventional relational databases or newer NoSQL databases. The ScalaTion Time Series Database is built on top of its column-oriented in-memory database. ScalaTion is an open-source Scala based big data framework for simulation, optimization and analytics. This database provides support for large-scale stor- age, efficient query processing, pattern matching and a variety of forecasting techniques. Its design goals include the ability to scale up and scale out, and the ability to handle conven- tional multivariate time series. The database provides an easy way to transform a table into a matrix (or vector) which may be used as input for other data science/machine-learning models that are available in ScalaTion. The capabilities are illustrated via a case study of vehicle traffic forecasting. Multiple experiments are conducted to evaluate the performances of four databases: ScalaTion, MySQL, SQLite, and SparkSQL.

Time Series Databases

Author : Ted Dunning
Publisher : O'Reilly Media
ISBN 13 : 9781491914724
Total Pages : 0 pages
Book Rating : 4.9/5 (147 download)

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Book Synopsis Time Series Databases by : Ted Dunning

Download or read book Time Series Databases written by Ted Dunning and published by O'Reilly Media. This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You'll explore the theory behind time series databases and learn practical methods for implementing them. Authors Ted Dunning and Ellen Friedman provide a detailed examination of open source tools such as OpenTSDB and new modifications that greatly speed up data ingestion. You'll learn: A variety of time series use cases The advantages of NoSQL databases for large-scale time series data NoSQL table design for high-performance time series databases The benefits and limitations of OpenTSDB How to access data in OpenTSDB using R, Go, and Ruby How time series databases contribute to practical machine learning projects How to handle the added complexity of geo-temporal data For advice on analyzing time series data, check out Practical Machine Learning: A New Look at Anomaly Detection, also from Ted Dunning and Ellen Friedman.

Data Mining in Time Series Databases

Author : Mark Last
Publisher : World Scientific
ISBN 13 : 9812382909
Total Pages : 205 pages
Book Rating : 4.8/5 (123 download)

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Book Synopsis Data Mining in Time Series Databases by : Mark Last

Download or read book Data Mining in Time Series Databases written by Mark Last and published by World Scientific. This book was released on 2004 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed. Contents: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (H M Lie); Indexing of Compressed Time Series (E Fink & K Pratt); Boosting Interval-Based Literal: Variable Length and Early Classification (J J Rodriguez Diez); Segmenting Time Series: A Survey and Novel Approach (E Keogh et al.); Indexing Similar Time Series under Conditions of Noise (M Vlachos et al.); Classification of Events in Time Series of Graphs (H Bunke & M Kraetzl); Median Strings--A Review (X Jiang et al.); Change Detection in Classfication Models of Data Mining (G Zeira et al.). Readership: Graduate students, reseachers and practitioners in the fields of data mining, machine learning, databases and statistics.

Big Data

Author : Nathan Warren
Publisher :
ISBN 13 :
Total Pages : 328 pages
Book Rating : 4.:/5 (111 download)

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Book Synopsis Big Data by : Nathan Warren

Download or read book Big Data written by Nathan Warren and published by . This book was released on 2015 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing.

Practical Time Series Analysis

Author : Aileen Nielsen
Publisher : O'Reilly Media
ISBN 13 : 1492041629
Total Pages : 500 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Time Series Analysis by : Aileen Nielsen

Download or read book Practical Time Series Analysis written by Aileen Nielsen and published by O'Reilly Media. This book was released on 2019-09-20 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance

Data Mining In Time Series And Streaming Databases

Author : Mark Last
Publisher : World Scientific
ISBN 13 : 9813228059
Total Pages : 196 pages
Book Rating : 4.8/5 (132 download)

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Book Synopsis Data Mining In Time Series And Streaming Databases by : Mark Last

Download or read book Data Mining In Time Series And Streaming Databases written by Mark Last and published by World Scientific. This book was released on 2018-01-12 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining.The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic.

Spatiotemporal Data Analytics and Modeling

Author : John A
Publisher : Springer Nature
ISBN 13 : 9819996511
Total Pages : 253 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Spatiotemporal Data Analytics and Modeling by : John A

Download or read book Spatiotemporal Data Analytics and Modeling written by John A and published by Springer Nature. This book was released on with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Practical Time Series Analysis

Author : Dr. Avishek Pal
Publisher : Packt Publishing Ltd
ISBN 13 : 178829419X
Total Pages : 238 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Practical Time Series Analysis by : Dr. Avishek Pal

Download or read book Practical Time Series Analysis written by Dr. Avishek Pal and published by Packt Publishing Ltd. This book was released on 2017-09-28 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

Understanding Big Data Scalability

Author : Cory Isaacson
Publisher : Prentice Hall
ISBN 13 : 0133599094
Total Pages : 241 pages
Book Rating : 4.1/5 (335 download)

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Book Synopsis Understanding Big Data Scalability by : Cory Isaacson

Download or read book Understanding Big Data Scalability written by Cory Isaacson and published by Prentice Hall. This book was released on 2014-07-11 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get Started Scaling Your Database Infrastructure for High-Volume Big Data Applications “Understanding Big Data Scalability presents the fundamentals of scaling databases from a single node to large clusters. It provides a practical explanation of what ‘Big Data’ systems are, and fundamental issues to consider when optimizing for performance and scalability. Cory draws on many years of experience to explain issues involved in working with data sets that can no longer be handled with single, monolithic relational databases.... His approach is particularly relevant now that relational data models are making a comeback via SQL interfaces to popular NoSQL databases and Hadoop distributions.... This book should be especially useful to database practitioners new to scaling databases beyond traditional single node deployments.” —Brian O’Krafka, software architect Understanding Big Data Scalability presents a solid foundation for scaling Big Data infrastructure and helps you address each crucial factor associated with optimizing performance in scalable and dynamic Big Data clusters. Database expert Cory Isaacson offers practical, actionable insights for every technical professional who must scale a database tier for high-volume applications. Focusing on today’s most common Big Data applications, he introduces proven ways to manage unprecedented data growth from widely diverse sources and to deliver real-time processing at levels that were inconceivable until recently. Isaacson explains why databases slow down, reviews each major technique for scaling database applications, and identifies the key rules of database scalability that every architect should follow. You’ll find insights and techniques proven with all types of database engines and environments, including SQL, NoSQL, and Hadoop. Two start-to-finish case studies walk you through planning and implementation, offering specific lessons for formulating your own scalability strategy. Coverage includes Understanding the true causes of database performance degradation in today’s Big Data environments Scaling smoothly to petabyte-class databases and beyond Defining database clusters for maximum scalability and performance Integrating NoSQL or columnar databases that aren’t “drop-in” replacements for RDBMSes Scaling application components: solutions and options for each tier Recognizing when to scale your data tier—a decision with enormous consequences for your application environment Why data relationships may be even more important in non-relational databases Why virtually every database scalability implementation still relies on sharding, and how to choose the best approach How to set clear objectives for architecting high-performance Big Data implementations The Big Data Scalability Series is a comprehensive, four-part series, containing information on many facets of database performance and scalability. Understanding Big Data Scalability is the first book in the series. Learn more and join the conversation about Big Data scalability at bigdatascalability.com.

Big Data Analytics

Author : Satish Narayana Srirama
Publisher : Springer Nature
ISBN 13 : 3030936201
Total Pages : 360 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Big Data Analytics by : Satish Narayana Srirama

Download or read book Big Data Analytics written by Satish Narayana Srirama and published by Springer Nature. This book was released on 2022-01-01 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 8th International Conference on Big Data Analytics, BDA 2021, which took place during December 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 full and 3 short papers included in this volume were carefully reviewed and selected from 41 submissions. The contributions were organized in topical sections named as follows: medical and health applications; machine/deep learning; IoTs, sensors, and networks; fundamentation; pattern mining and data analytics.

HPI Future SOC Lab

Author : Meinel, Christoph
Publisher : Universitätsverlag Potsdam
ISBN 13 : 386956282X
Total Pages : 183 pages
Book Rating : 4.8/5 (695 download)

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Book Synopsis HPI Future SOC Lab by : Meinel, Christoph

Download or read book HPI Future SOC Lab written by Meinel, Christoph and published by Universitätsverlag Potsdam. This book was released on 2015-06-03 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: The “HPI Future SOC Lab” is a cooperation of the Hasso-Plattner-Institut (HPI) and industrial partners. Its mission is to enable and promote exchange and interaction between the research community and the industrial partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard- and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2013. Selected projects have presented their results on April 10th and September 24th 2013 at the Future SOC Lab Day events.

New Horizons for a Data-Driven Economy

Author : José María Cavanillas
Publisher : Springer
ISBN 13 : 3319215698
Total Pages : 312 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis New Horizons for a Data-Driven Economy by : José María Cavanillas

Download or read book New Horizons for a Data-Driven Economy written by José María Cavanillas and published by Springer. This book was released on 2016-04-04 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

The Future Internet

Author : Federico Alvarez
Publisher : Springer
ISBN 13 : 3642302416
Total Pages : 273 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis The Future Internet by : Federico Alvarez

Download or read book The Future Internet written by Federico Alvarez and published by Springer. This book was released on 2012-04-29 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Irrespective of whether we use economic or societal metrics, the Internet is one of the most important technical infrastructures in existence today. It will serve as a catalyst for much of our innovation and prosperity in the future. A competitive Europe will require Internet connectivity and services beyond the capabilities offered by current technologies. Future Internet research is therefore a must. The Future Internet Assembly (FIA) is a successful and unique bi-annual conference that brings together participants of over 150 projects from several distinct but interrelated areas in the EU Framework Programme 7. The 20 full papers included in this volume were selected from 40 submissions, and are preceded by a vision paper describing the FIA Roadmap. The papers have been organized into topical sections on the foundations of Future Internet, the applications of Future Internet, Smart Cities, and Future Internet infrastructures.

New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence

Author : Daniel H. de la Iglesia
Publisher : Springer Nature
ISBN 13 : 3031383443
Total Pages : 371 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence by : Daniel H. de la Iglesia

Download or read book New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence written by Daniel H. de la Iglesia and published by Springer Nature. This book was released on 2023-07-21 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the evidence-based insights into the ethical considerations surrounding disruptive technologies. In the rapidly evolving landscape of technology, where breakthroughs in artificial intelligence, big data, the Internet of Things, and bioinformatics have revolutionized our world, a critical need arises to reassess our ethical frameworks. This need has given birth to the thriving field of technology ethics, or tech ethics, which has grown exponentially in recent years. Once a niche area of research, it now encompasses a multitude of technology experts dedicated to understanding the societal impact of these advancements and striving for the development of more ethically grounded technology. At the forefront of this movement stands the International Conference on Disruptive Technologies, Tech Ethics, and Artificial Intelligence (DITTET 2023). Serving as a paramount platform for scholars, professionals, and experts, this conference presents an unparalleled opportunity to explore the latest scientific and technical progress and its profound ethical implications. DITTET facilitates the exchange of cutting-edge research on disruptive technologies, fostering knowledge transfer and collaboration among interdisciplinary fields. DITTET 2023 aspires to bring together a diverse range of industry leaders, humanists, and academics, providing a comprehensive overview of the scientific advancements and applications of artificial intelligence while examining their ethical dimensions in areas such as climate change, politics, economy, and security. By delving into these crucial topics, the conference aims to unravel the intricate relationship between technology and ethics, paving the way for responsible and conscientious innovation in today's world.

Encyclopedia of Data Science and Machine Learning

Author : Wang, John
Publisher : IGI Global
ISBN 13 : 1799892212
Total Pages : 3296 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Encyclopedia of Data Science and Machine Learning by : Wang, John

Download or read book Encyclopedia of Data Science and Machine Learning written by Wang, John and published by IGI Global. This book was released on 2023-01-20 with total page 3296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

Artificial Intelligence for COVID-19

Author : Diego Oliva
Publisher : Springer Nature
ISBN 13 : 3030697444
Total Pages : 594 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Artificial Intelligence for COVID-19 by : Diego Oliva

Download or read book Artificial Intelligence for COVID-19 written by Diego Oliva and published by Springer Nature. This book was released on 2021-07-19 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations.

Dynamic Factor Models

Author : Siem Jan Koopman
Publisher : Emerald Group Publishing
ISBN 13 : 1785603523
Total Pages : 685 pages
Book Rating : 4.7/5 (856 download)

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Book Synopsis Dynamic Factor Models by : Siem Jan Koopman

Download or read book Dynamic Factor Models written by Siem Jan Koopman and published by Emerald Group Publishing. This book was released on 2016-01-08 with total page 685 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.