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This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.
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
Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems
This book constitutes the refereed proceedings of the 11th International Conference on Database Systems for Advanced Applications, DASFAA 2006, held in Singapore in April 2006. 46 revised full papers and 16 revised short papers presented were carefully reviewed and selected from 188 submissions. Topics include sensor networks, subsequence matching and repeating patterns, spatial-temporal databases, data mining, XML compression and indexing, xpath query evaluation, uncertainty and streams, peer-to-peer and distributed networks and more.
This book constitutes the workshop proceedings of the 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014, held in Bali, Indonesia, in April 2014. The volume contains papers from 4 workshops, each focusing on hot topics related to database systems and applications: the Second International Workshop on Big Data Management and Analytics, BDMA 2014; the Third International Workshop on Data Management for Emerging Network Infrastructure, DaMEN 2014; the Third International Workshop on Spatial Information Modeling, Management and Mining, SIM3 2014, and the DASFAA Workshop on Uncertain and Crowdsourced Data, UnCrowd 2014.
This book constitutes the refereed proceedings of the 17th International Conference on Advanced Information Systems Engineering, CAiSE 2005, held in Porto, Portugal in June 2005. The 39 revised full papers presented were carefully reviewed and selected from 282 submissions. The papers are organized in topical sections on conceptual modeling, metamodeling, databases, query processing, process modeling and workflow systems, requirements engineering, model transformation, knowledge management and verification, Web services, Web engineering, software testing, and software quality.
This two volume set LNCS 9049 and LNCS 9050 constitutes the refereed proceedings of the 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015, held in Hanoi, Vietnam, in April 2015. The 63 full papers presented were carefully reviewed and selected from a total of 287 submissions. The papers cover the following topics: data mining; data streams and time series; database storage and index; spatio-temporal data; modern computing platform; social networks; information integration and data quality; information retrieval and summarization; security and privacy; outlier and imbalanced data analysis; probabilistic and uncertain data; query processing.
These two volumes set LNCS 8421 and LNCS 8422 constitutes the refereed proceedings of the 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014, held in Bali, Indonesia, in April 2014. The 62 revised full papers presented together with 1 extended abstract paper, 4 industrial papers, 6 demo presentations, 3 tutorials and 1 panel paper were carefully reviewed and selected from a total of 257 submissions. The papers cover the following topics: big data management, indexing and query processing, graph data management, spatio-temporal data management, database for emerging hardware, data mining, probabilistic and uncertain data management, web and social data management, security, privacy and trust, keyword search, data stream management and data quality.
This handbook covers a wide range of topics related to the collection, processing, analysis, and use of geospatial data in their various forms. This handbook provides an overview of how spatial computing technologies for big data can be organized and implemented to solve real-world problems. Diverse subdomains ranging from indoor mapping and navigation over trajectory computing to earth observation from space, are also present in this handbook. It combines fundamental contributions focusing on spatio-textual analysis, uncertain databases, and spatial statistics with application examples such as road network detection or colocation detection using GPUs. In summary, this handbook gives an essential introduction and overview of the rich field of spatial information science and big geospatial data. It introduces three different perspectives, which together define the field of big geospatial data: a societal, governmental, and governance perspective. It discusses questions of how the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary spatial data with contributions introducing into the exciting field of spatial statistics or into uncertain databases. A third perspective is taking a very practical perspective to big geospatial data, ranging from chapters that describe how big geospatial data infrastructures can be implemented and how specific applications can be implemented on top of big geospatial data. This would include for example, research in historic map data, road network extraction, damage estimation from remote sensing imagery, or the analysis of spatio-textual collections and social media. This multi-disciplinary approach makes the book unique. This handbook can be used as a reference for undergraduate students, graduate students and researchers focused on big geospatial data. Professionals can use this book, as well as practitioners facing big collections of geospatial data.