Proceedings of the Twenty-Fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems PDF Download
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Author:
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 394
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Book Description
Author:
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 394
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Book Description
Author:
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 316
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Book Description
Author: Jaideep Vaidya
Publisher: Springer Science & Business Media
ISBN: 0387294899
Category : Computers
Languages : en
Pages : 124
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Book Description
Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.
Author: Association for Computing Machinery. Special Interest Group on Management of Data
Publisher:
ISBN: 9781581138580
Category : Computer science
Languages : en
Pages : 364
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Book Description
Author: Dan Suciu
Publisher: Morgan & Claypool Publishers
ISBN: 1608456803
Category : Computers
Languages : en
Pages : 183
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Book Description
Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques
Author: Dinesh P. Mehta
Publisher: Taylor & Francis
ISBN: 1498701884
Category : Computers
Languages : en
Pages : 1121
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Book Description
The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.
Author: Lei Chen
Publisher: Springer Science & Business Media
ISBN: 364204204X
Category : Computers
Languages : en
Pages : 383
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Book Description
This book constitutes the workshop proceedings of the 14th International Conference on Database Systems for Advanced Applications, DASFAA 2009, held in Brisbane, Australia, in April 2009. The volume contains six workshops, each focusing on specific research issues that contribute to the main themes of the DASFAA conference: The First International Workshop on Benchmarking of XML and Semantic Web Applications (BenchmarkX'09); The Second International Workshop on Managing Data Quality in Collaborative Information Systems (MCIS'09); The 1st International Workshop on Data and Process Provenance (WDPP'09); The First International Workshop on Privacy-Preserving Data Analysis (PPDA'09); The First International Workshop on Mobile Business Collaboration (MBC'09); and the First Ph.D. Workshop.
Author: Ming Hua
Publisher: Springer Science & Business Media
ISBN: 1441993800
Category : Computers
Languages : en
Pages : 233
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Book Description
Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorithms of ranking queries on uncertain data. Theoretical and algorithmic results of ranking queries on uncertain data are presented in the last section of this book. Ranking Queries on Uncertain Data is the first book to systematically discuss the problem of ranking queries on uncertain data.
Author: Mykola Pechenizkiy
Publisher: Springer Science & Business Media
ISBN: 3642325181
Category : Technology & Engineering
Languages : en
Pages : 444
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Book Description
Database and information systems technologies have been rapidly evolving in several directions over the past years. New types and kinds of data, new types of applications and information systems to support them raise diverse challenges to be addressed. The so-called big data challenge, streaming data management and processing, social networks and other complex data analysis, including semantic reasoning into information systems supporting for instance trading, negotiations, and bidding mechanisms are just some of the emerging research topics. This volume contains papers contributed by six workshops: ADBIS Workshop on GPUs in Databases (GID 2012), Mining Complex and Stream Data (MCSD'12), International Workshop on Ontologies meet Advanced Information Systems (OAIS'2012), Second Workshop on Modeling Multi-commodity Trade: Data models and processing (MMT'12), 1st ADBIS Workshop on Social Data Processing (SDP'12), 1st ADBIS Workshop on Social and Algorithmic Issues in Business Support (SAIBS), and the Ph.D. Consortium associated with the ADBIS 2012 conference that report on the recent developments and an ongoing research in the aforementioned areas.
Author: Dennis Elliott Shasha
Publisher: Springer Science & Business Media
ISBN: 9780387008578
Category : Computers
Languages : en
Pages : 210
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Book Description
Time-series data—data arriving in time order, or a data stream—can be found in fields such as physics, finance, music, networking, and medical instrumentation. Designing fast, scalable algorithms for analyzing single or multiple time series can lead to scientific discoveries, medical diagnoses, and perhaps profits. High Performance Discovery in Time Series presents rapid-discovery techniques for finding portions of time series with many events (i.e., gamma-ray scatterings) and finding closely related time series (i.e., highly correlated price and return histories, or musical melodies). A typical time-series technique may compute a "consensus" time series—from a collection of time series—to use regression analysis for predicting future time points. By contrast, this book aims at efficient discovery in time series, rather than prediction, and its novelty lies in its algorithmic contributions and its simple, practical algorithms and case studies. It presumes familiarity with only basic calculus and some linear algebra. Topics and Features: *Presents efficient algorithms for discovering unusual bursts of activity in large time-series databases * Describes the mathematics and algorithms for finding correlation relationships between thousands or millions of time series across fixed or moving windows *Demonstrates strong, relevant applications built on a solid scientific basis *Outlines how readers can adapt the techniques for their own needs and goals *Describes algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection *Offers self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis This new monograph provides a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. It offers essential coverage of the topic for computer scientists, physicists, medical researchers, financial mathematicians, musicologists, and researchers and professionals who must analyze massive time series. In addition, it can serve as an ideal text/reference for graduate students in many data-rich disciplines.
