Author : Graham Cormode
Publisher : Now Publishers
Page : 308 pages
File Size : 25,18 MB
Release : 2012
Category : Computers
ISBN : 9781601985163
Describes basic principles and recent developments in approximate query processing. It focuses on four key synopses: random samples, histograms, wavelets, and sketches. It considers issues such as accuracy, space and time efficiency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance.
Author : National Research Council
Publisher : National Academies Press
Page : 191 pages
File Size : 46,85 MB
Release : 2013-09-03
Category : Mathematics
ISBN : 0309287812
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Author : James Abello
Publisher : Springer
Page : 1209 pages
File Size : 20,70 MB
Release : 2013-12-21
Category : Computers
ISBN : 1461500052
The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board on Mathe matical Seiences has stated: "My interest in this problern (Massive Data Sets) isthat I see it as the rnost irnportant cross-cutting problern for the rnathernatical sciences in practical problern solving for the next decade, because it is so pervasive. " The Handbook of Massive Data Sets is comprised of articles writ ten by experts on selected topics that deal with some major aspect of massive data sets. It contains chapters on information retrieval both in the internet and in the traditional sense, web crawlers, massive graphs, string processing, data compression, dustering methods, wavelets, op timization, external memory algorithms and data structures, the US national duster project, high performance computing, data warehouses, data cubes, semi-structured data, data squashing, data quality, billing in the large, fraud detection, and data processing in astrophysics, air pollution, biomolecular data, earth observation and the environment.
Author : Jure Leskovec
Publisher : Cambridge University Press
Page : 480 pages
File Size : 45,37 MB
Release : 2014-11-13
Category : Computers
ISBN : 1107077230
Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Author : Torsten Grust
Publisher : Springer Science & Business Media
Page : 961 pages
File Size : 26,15 MB
Release : 2006-10-10
Category : Computers
ISBN : 3540467882
This book constitutes the thoroughly refereed joint post-proceedings of nine workshops held as part of the 10th International Conference on Extending Database Technology, EDBT 2006, held in Munich, Germany in March 2006. The 70 revised full papers presented were selected from numerous submissions during two rounds of reviewing and revision.
Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
Page : 365 pages
File Size : 36,33 MB
Release : 2007-04-03
Category : Computers
ISBN : 0387475346
This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
Author : David S. Wilson
Publisher :
Page : 70 pages
File Size : 46,71 MB
Release : 2018
Category : Big data
ISBN :
Correlated Sample Synopsis (or CS2) has been proven to be a valuable option concerning centralized databases but has yet to be tested on big data. With the overall accumulation of data growing at an alarming rate, scalable query estimation and approximate query processing are becoming necessary for large databases. Query estimations based on the Simple Random Sample Without Replacement (or SRSWOR) return results with extremely high relative errors for join queries. Existing methods, such as Join Synopses, only work well with foreign key joins, and the sample size can grow dramatically as the dataset gets larger. This research aims to provide that CS2 can speed up search query length results, give precise join query estimations, and minimize storage costs when presented with big data. In addition, this research extends the correlated sampling techniques and estimation methods of CS2 to the big data environment with no index present. Extensive experiments with large TPC-H datasets in Apache Hive show that CS2 produces fast and accurate query estimations on big data.
Author : Anirban Mondal
Publisher : Springer
Page : 429 pages
File Size : 11,26 MB
Release : 2018-12-11
Category : Computers
ISBN : 3030047806
This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining.
Author : Alan Anderson
Publisher : John Wiley & Sons
Page : 390 pages
File Size : 31,7 MB
Release : 2015-08-31
Category : Computers
ISBN : 1118940016
The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word "statistics" alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in data Provides guidance on extracting previously unknown information from large databases Shows you how to discover patterns available in big data Gives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.
Author : Graham Cormode
Publisher : Cambridge University Press
Page : 279 pages
File Size : 11,54 MB
Release : 2020-11-12
Category : Computers
ISBN : 1108477445
A comprehensive introduction to flexible, efficient tools for describing massive data sets to improve the scalability of data analysis.
