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Author : David B. Lomet
Publisher :
ISBN 13 : 9783662192245
Total Pages : 432 pages
Book Rating : 4.1/5 (922 download)
Download or read book Foundations of Data Organization and Algorithms written by David B. Lomet and published by . This book was released on 2014-01-15 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Witold Litwin
Publisher :
ISBN 13 : 9783662178188
Total Pages : 544 pages
Book Rating : 4.1/5 (781 download)
Download or read book Foundations of Data Organization and Algorithms written by Witold Litwin and published by . This book was released on 2014-01-15 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Witold Litwin
Publisher : Springer Science & Business Media
ISBN 13 : 9783540512950
Total Pages : 552 pages
Book Rating : 4.5/5 (129 download)
Download or read book Foundations of Data Organization and Algorithms written by Witold Litwin and published by Springer Science & Business Media. This book was released on 1989-06-07 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Third International Conference on Foundations of Data Organization and Algorithms has been organized by INRIA in Paris from June 21 to 23, 1989. Previous FODO Conferences were held in Warsaw, 1981, and in Kyoto, 1985. The goal of this year's conference is to present advances in techniques of permanent and temporary data organization in different fields. New applications such as image processing, graphics, geographic data processing, robotics, office automation, information systems, language translation, and expert systems have developed various data organizations and algorithms specific to the application requirements. The growing importance of these applications has created a need for general studies on data organization and algorithms as well as for specific studies on new database management systems and on filing services. The articles submitted for the conference were subject to the usual rigorous reviewing process and selected on that basis. They offer an excellent snapshot of the state of the art in the field and should prove invaluable for computer scientists faced by the problems of data organization which are raised by these new applications.
Author : David B. Lomet
Publisher : Springer Science & Business Media
ISBN 13 : 9783540573012
Total Pages : 430 pages
Book Rating : 4.5/5 (73 download)
Download or read book Foundations of Data Organization and Algorithms written by David B. Lomet and published by Springer Science & Business Media. This book was released on 1993-09-29 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the proceedings of the Fourth International Conference on Data Organization and Algorithms, FODO '93, held in Evanston, Illinois. FODO '93 reflects the maturing of the database field which hasbeen driven by the enormous growth in the range of applications for databasesystems. The "non-standard" applications of the not-so-distant past, such ashypertext, multimedia, and scientific and engineering databases, now provide some of the central motivation for the advances in hardware technology and data organizations and algorithms. The volume contains 3 invited talks, 22 contributed papers, and 2 panel papers. The contributed papers are grouped into parts on multimedia, access methods, text processing, query processing, industrial applications, physical storage, andnew directions.
Author : Katsumi Tanaka
Publisher : Springer Science & Business Media
ISBN 13 : 1461513790
Total Pages : 377 pages
Book Rating : 4.4/5 (615 download)
Download or read book Information Organization and Databases written by Katsumi Tanaka and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information Organization and Databases: Foundations of Data Organization provides recent developments of information organization technologies that have become crucial not only for data mining applications and information visualization, but also for treatment of semistructured data, spatio-temporal data and multimedia data that are not necessarily stored in conventional DBMSs. Information Organization and Databases: Foundations of Data Organization presents: semistructured data addressing XML, query languages and integrity constraints, focusing on advanced technologies for organizing web data for effective retrieval; multimedia database organization emphasizing video data organization and data structures for similarity retrieval; technologies for data mining and data warehousing; index organization and efficient query processing issues; spatial data access and indexing; organizing and retrieval of WWW and hypermedia. Information Organization and Databases: Foundations of Data Organization is a resource for database practitioners, database researchers, designers and administrators of multimedia information systems, and graduate-level students in the area of information retrieval and/or databases wishing to keep abreast of advances in the information organization technologies.
Author :
Publisher :
ISBN 13 :
Total Pages : 444 pages
Book Rating : 4.3/5 (91 download)
Download or read book Foundations of Data Organization and Algorithms written by and published by . This book was released on 1993 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sakti P. Ghosh
Publisher : Springer Science & Business Media
ISBN 13 : 1461318815
Total Pages : 615 pages
Book Rating : 4.4/5 (613 download)
Download or read book Foundations of Data Organization written by Sakti P. Ghosh and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of data organization is a relatively new field of research in comparison to, other branches of science. It is close to twenty years old. In this short life span of this branch of computer science, it has spread to all corners of the world, which is reflected in this book. This book covers new database application areas (databases for advanced applications and CAD/VLSI databases), computational geometry, file allocation & distributed databases, database models (including non traditional database models), database machines, query processing & physical structures for relational databases, besides traditional file organization (hashing, index file organization, mathematical file organization and consecutive retrieval property), in order to identify new trends of database research. The papers in this book originally represent talks given at the International Conference on Foundations of Data Organization, which was held on May 21-24, 1985, in Kyoto, Japan. This conference was held at Kyoto University, and sponsored by the organizing committee of the International Conference on Foundations of Data Organization and the Japan Society for the Promotion of Science. The conference was in cooperation with: ACM SIGMOD, IEEE Computer Society, Information Processing Society of Japan, IBM Research, Kyushu University, Kobe University, IBM Japan, Kyoto Sangyo University and Polish Academy of Sciences. This Conference was the follow-up of the first conference, which was hosted by the Polish Academy of Sciences and held at Warsaw in 1981. The Warsaw conference focused mainly on consecutive retrieval property and it's applications.
Author : Cynthia Dwork
Publisher :
ISBN 13 : 9781601988188
Total Pages : 286 pages
Book Rating : 4.9/5 (881 download)
Download or read book The Algorithmic Foundations of Differential Privacy written by Cynthia Dwork and published by . This book was released on 2014 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.
Author : Wenfei Fan
Publisher : Morgan & Claypool Publishers
ISBN 13 : 160845777X
Total Pages : 220 pages
Book Rating : 4.6/5 (84 download)
Download or read book Foundations of Data Quality Management written by Wenfei Fan and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an overview of fundamental issues underlying central aspects of data quality - data consistency, data deduplication, data accuracy, data currency, and information completeness. The book promotes a uniform logical framework for dealing with these issues, based on data quality rules.
Author : Fabrizio Luccio
Publisher : CRC Press
ISBN 13 : 1439831386
Total Pages : 224 pages
Book Rating : 4.4/5 (398 download)
Download or read book Mathematical and Algorithmic Foundations of the Internet written by Fabrizio Luccio and published by CRC Press. This book was released on 2011-07-06 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: To truly understand how the Internet and Web are organized and function requires knowledge of mathematics and computation theory. Mathematical and Algorithmic Foundations of the Internet introduces the concepts and methods upon which computer networks rely and explores their applications to the Internet and Web. The book offers a unique approach to mathematical and algorithmic concepts, demonstrating their universality by presenting ideas and examples from various fields, including literature, history, and art. Progressing from fundamental concepts to more specific topics and applications, the text covers computational complexity and randomness, networks and graphs, parallel and distributed computing, and search engines. While the mathematical treatment is rigorous, it is presented at a level that can be grasped by readers with an elementary mathematical background. The authors also present a lighter side to this complex subject by illustrating how many of the mathematical concepts have counterparts in everyday life. The book provides in-depth coverage of the mathematical prerequisites and assembles a complete presentation of how computer networks function. It is a useful resource for anyone interested in the inner functioning, design, and organization of the Internet.
Author : Avrim Blum
Publisher : Cambridge University Press
ISBN 13 : 1108617360
Total Pages : 433 pages
Book Rating : 4.1/5 (86 download)
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Author : Christopher G. Healey
Publisher : CRC Press
ISBN 13 : 1315302853
Total Pages : 203 pages
Book Rating : 4.3/5 (153 download)
Download or read book Disk-Based Algorithms for Big Data written by Christopher G. Healey and published by CRC Press. This book was released on 2016-11-17 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk-Based Algorithms for Big Data is a product of recent advances in the areas of big data, data analytics, and the underlying file systems and data management algorithms used to support the storage and analysis of massive data collections. The book discusses hard disks and their impact on data management, since Hard Disk Drives continue to be common in large data clusters. It also explores ways to store and retrieve data though primary and secondary indices. This includes a review of different in-memory sorting and searching algorithms that build a foundation for more sophisticated on-disk approaches like mergesort, B-trees, and extendible hashing. Following this introduction, the book transitions to more recent topics, including advanced storage technologies like solid-state drives and holographic storage; peer-to-peer (P2P) communication; large file systems and query languages like Hadoop/HDFS, Hive, Cassandra, and Presto; and NoSQL databases like Neo4j for graph structures and MongoDB for unstructured document data. Designed for senior undergraduate and graduate students, as well as professionals, this book is useful for anyone interested in understanding the foundations and advances in big data storage and management, and big data analytics. About the Author Dr. Christopher G. Healey is a tenured Professor in the Department of Computer Science and the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics, both at North Carolina State University in Raleigh, North Carolina. He has published over 50 articles in major journals and conferences in the areas of visualization, visual and data analytics, computer graphics, and artificial intelligence. He is a recipient of the National Science Foundation’s CAREER Early Faculty Development Award and the North Carolina State University Outstanding Instructor Award. He is a Senior Member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and an Associate Editor of ACM Transaction on Applied Perception, the leading worldwide journal on the application of human perception to issues in computer science.
Author : Avrim Blum
Publisher : Cambridge University Press
ISBN 13 : 1108485065
Total Pages : 433 pages
Book Rating : 4.1/5 (84 download)
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Author : S. Muthukrishnan
Publisher : Now Publishers Inc
ISBN 13 : 193301914X
Total Pages : 136 pages
Book Rating : 4.9/5 (33 download)
Download or read book Data Streams written by S. Muthukrishnan and published by Now Publishers Inc. This book was released on 2005 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges.
Author : Wenfei Fan
Publisher : Springer Nature
ISBN 13 : 3031018923
Total Pages : 201 pages
Book Rating : 4.0/5 (31 download)
Download or read book Foundations of Data Quality Management written by Wenfei Fan and published by Springer Nature. This book was released on 2022-05-31 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues
Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 1466510854
Total Pages : 752 pages
Book Rating : 4.4/5 (665 download)
Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Author : Parikshit Narendra Mahalle
Publisher : Springer Nature
ISBN 13 : 9811651604
Total Pages : 125 pages
Book Rating : 4.8/5 (116 download)
Download or read book Foundations of Data Science for Engineering Problem Solving written by Parikshit Narendra Mahalle and published by Springer Nature. This book was released on 2021-08-21 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
