xingquan hill zhu fvenocemsuvmedu apologies crosspostings please send interested colleagues research groups area call book chapters knowledge discovery data mining challenges realities real world data httpwwwcsuvmeduxqzhubookigi_kddhtm submission deadline proposal november 1 2005 book edited dr xingquan hill zhu dr ian davidson introduction knowledge discovery data mining kdd dedicated exploring useful information large volume data however practice underlying data quality data modality often impose severe challenges mining tasks mining large data sets require mining algorithms scale well applications getting instances expensive mining algorithms must handle small data sets data suffers corruptions erroneous missing values desirable enhance underlying data mined situations privacy preserving data mining trustworthy data sharing desirable explicitly intentionally add perturbations original data order mask sensitive values protect privacy addition traditional data sources multimedia image audio videos mining algorithms severely challenged unique modality data huge continuous volume unrelational data items data characterized realworld complexities traditional data mining efforts often work ineffectively input algorithms often assumed confirm strict assumptions reasonable data volume specific data distributions missing inconsistent incorrect values creates challenges realworld data available effective mining mechanisms building realworld data mining applications general consensus among data mining practitioners ignoring underlying data quality modality data volume data dimensionality information quality often leads inferior results computational efficiency data mining increasingly recognized key technology analyzing understanding data need knowledge discovery realworld data sets becomes compelling alleviate challenges realworld data quality modality research endeavors proposed many solutions using progressive sampling handle large datasets generating artificial instances using unlabeled examples learning small labeled data sets adopting noise cleaning dealing erroneous data entries many challenges still exist objective proposed book intended provide focal point research data mining practitioners advance knowledge discovery realworld data sets indepth experiences methodologies discussed book provide theoretical empirical guidance users suffered underlying data quality data modality book focus interdisciplinary collaborations among various areas data quality control data processing data mining data privacy data sharing end solicit highquality chapters address challenges reality issues data mining including limited following topics data mining challenges mining lowquality information sources mining large data sets continuous streaming media mining multimedia data text graphic audio image video case studies realworld data mining projects new challenges applications data mining information quality modality data information quality assessment data quality models theoretical foundations domainspecific data quality evaluation metrics quality characterization data quality monitoring data preprocessing enhancement data cleansing noise detection data correction resolving data redundancy inconsistency duplicates conflicts datafeature reduction instancefeature ranking data editing imputation data acquisition filling incomplete values data transformations reconciliation consolidation intelligent tools data enhancement data mining algorithms data quality data modality concerns progressive sampling subspace mining generating artificial instances using unlabeled examples integrating data quality measures effective mining data mining data intentional perturbations quality awareness data mining noise tolerant data mining mining raw data sources industrial data quality enhancement improved data mining results submission procedure researchers practitioners invited submit november 1 2005 23 page chapter proposal clearly explaining mission concerns proposed chapter authors accepted proposals notified november 15 2005 status proposals sent chapter organizational guidelines full chapters expected submitted february 1 2006 submitted chapters reviewed doubleblind review basis book scheduled published idea group inc wwwideagroupcom publisher idea group publishing information science publishing irm press cybertech publishing idea group reference imprints 2007 important dates send abstract editors november 1 2005 authors notified november 15 2005 full chapters due february 1 2006 review authors march 15 2006 revised chapters due april 15 2006 inquiries forwarded xingquan hill zhu department computer science university vermont 33 colchester ave votey 377 burlington vt 05405 phone 802 6563305 fax 802 6560696 email fvenocemsuvmedu httpwwwcsuvmeduxqzhu ian davidson department computer science state university new york 1400 washington avenue albany ny 12222 phone 518 442 5173 email ltuesssicsalbanyedu httpwwwcsalbanyedudavidson details please refer following website httpwwwcsuvmeduxqzhubookigi_kddhtm wed 06 aug 2008 132535 0400 uai call chapters knowledge disc data miningchallenges realities real world data