Managing and Mining Uncertain Data by Charu C. Aggarwal (auth.), Charu C. Aggarwal (eds.)

By Charu C. Aggarwal (auth.), Charu C. Aggarwal (eds.)

Managing and Mining doubtful Data comprises surveys via renowned researchers within the box of doubtful databases. The booklet provides the latest types, algorithms, and purposes within the doubtful info box in a dependent and concise method. This booklet is geared up on the way to conceal an important administration and mining subject matters within the box. the assumption is to make it available not just to researchers, but additionally to application-driven practitioners for fixing actual difficulties. Given the shortcoming of structurally prepared info at the new and rising zone of doubtful information, this publication presents insights which aren't simply obtainable elsewhere.

Managing and Mining doubtful Data is designed for a diversified viewers composed of professors, researchers and practitioners in undefined. This publication can be compatible as a reference publication for advanced-level database scholars in laptop technological know-how and engineering.

Editor Biography

Charu C. Aggarwal acquired his B.Tech in computing device technology from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a examine employees Member at IBM seeing that then, and has released over one hundred twenty papers in significant meetings and journals within the database and knowledge mining box. He has utilized for or been granted over sixty five US and overseas patents, and has 3 times been distinctive grasp Inventor at IBM for the economic worth of his patents. He has been granted 17 invention success awards by way of IBM for his patents. His paintings on actual time bio-terrorist chance detection in facts streams gained the IBM company award for Environmental Excellence in 2003. he's a recipient of the IBM notable Innovation Award in 2008 for his medical contributions to privateness expertise, and a recipient of the IBM examine department award for his contributions to movement mining for the procedure S venture. He has served at the software committee of so much significant database meetings, and was once software chair for the knowledge Mining and information Discovery Workshop, 2003, and application vice-chairs for the SIAM convention on facts Mining 2007, ICDM convention 2007, and the WWW convention, 2009. He served as an affiliate editor of the IEEE Transactions on information Engineering from 2004 to 2008. he's an affiliate editor of the ACM SIGKDD Explorations and an motion editor of the knowledge Mining and data Discovery magazine. he's a senior member of the IEEE and a life-member of the ACM.

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Example text

We shall give below comparisons between the models [41] and the tables with variables from [30]. Two criteria have been provided for comparisons among all these models: [30, 41] discuss closure under relational algebra operations, while [41] also emphasizes completeness, specifically the ability to represent all finite incomplete databases. We point out that the latter is not appropriate for tables with variables over an infinite domain, and we describe another criterion, RAcompleteness, that fully characterizes the expressive power of c-tables.

Everything we say can be easily reformulated for arbitrary relational schemas. , a subset I ⊆ N. The usual relational databases correspond to the cases when I = {I}. The no-information or zero-information database consists of all the relations: N. Conventional relational instances are finite. However, because D is infinite incomplete databases are in general infinite. Hence the interest in finite, syntactical, representations for incomplete information. 2 A representation system consists of a set (usually a syntactically defined “language”) whose elements we call tables, and a function Mod that associates to each table T an incomplete database Mod(T ).

All this gives us a p-database, namely the image of P under f . 1. Indeed, since f is a bijection, this probabilistic database is in fact isomorphic to P . In P the events that are in bijection with the Et ’s are the Cartesian product in which there is exactly one component {true} and the rest are {true, false}. 3. We define now another simple probabilistic representation system called probabilistic or-set-tables (p-or-set-tables for short). These are the probabilistic counterpart of or-set-tables where the attribute values are, instead of or-sets, finite probability spaces whose outcomes are the values in the or-set.

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