日本データベース学会の皆様 東京工芸大学の宇田川と申します． 第14回Web Information Systems Engineering(WISE'13)国際会議と併設で WISE 2013 Workshop on data quality and trust in Big Data (QUAT'13)を 開催します。 http://permalink.gmane.org/gmane.comp.db.dbworld/39981 投稿締切は7月18日となっております．ご投稿をご検討いただければ幸いです． どうぞよろしくお願いいたします． ---------------------------------- 東京工芸大学工学部コンピュータ応用学科 宇田川 佳久 (Yoshihisa Udagawa) E-mail： udagawa@cs.t-kougei.ac.jp Call for Pagers (QUAT’13) WISE 2013 Workshop on data quality and trust in Big Data (QUAT’13) In conjunction with the 14th international conference on Web Information Systems Engineering (WISE’13), 13 Oct 2013 in Nanjing, China Focus The problem of data quality in data processing, data management, data analysis, and information systems largely and indistinctly affects every application domain, especially at the era of “Big Data”. “Big Data” has the characteristics of huge volume in data and a great variety of structures or no structure. “Big Data” is increased at a great velocity everyday and may be less trustable. The use of big data underpins critical activities in all sectors of our society. Many data processing tasks (such as data collection, data integration, data sharing, information extraction, and knowledge acquisition) require various forms of data preparation and consolidation with complex data processing and analysis techniques. Achieving the full transformative potential of “Big Data” requires both new data analysis algorithms and a new class of systems to handle the dramatic data growth, the demand to integrate structured and unstructured data analytics, and the increasing computing needs of massive scale analytics. The consensus is that the quality of data and the veracity of data have to span over the entire process of data collection, preparation, analysis, modelling, implementation, use, testing, and maintenance, including novel algorithms and usable systems. Topics of Interest The QUAT workshop is a qualified forum for presenting and discussing novel ideas and solutions related to the problems of exploring, assessing, monitoring, improving, and maintaining the quality of data and trust for “Big Data”. Topics include, but are not limited to, the following. - Conflict resolution and data fusion - Cleaning extremely large datasets - Privacy-preserving data quality - Data quality on novel data management architectures (cloud, streaming data, ...) - Data scrubbing, data standardization, data cleaning techniques - Quality-aware analytics solutions - Data quality mining - Quality of scientific, geographical, and biologic databases - Data quality assessment, measures and improvement methodologies - Trust in big data, - Trust in social networking data, - Trust distribution, propagation, and computation - Identity and Trust Management - Conceptual models and algebra for trust, - Data quality in big data, - Quality in data collection, processing, and storage - Data quality in environmental, transport, manufacture data - Data quality in the web data - Quality for data, information, and knowledge Important Dates Important Dates:
Deadline for submissions: July 18, 2013 Notification of acceptance: August 18, 2013 Camera-ready versions: September 10, 2013
Submission Instructions A full paper should not exceed 12 pages in LNCS (Lecture Notes in Computer Science) format A short paper should not exceed 6 pages still in LNCS format. http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 All papers must be submitted in PDF. Please ensure that any special fonts are embedded in the submitted documents. It is essential that the submitted papers print without difficulty on a variety of printers using Adobe Acrobat Reader. All submissions will be handled electronically. Each submission will be reviewed by at least three members of the program committee. ------ Prof. William Wei Song, PhD, MACM, MBSC Professor in Business Intelligence and Informatics Director of Postgraduate of MicroData Analysis Dalarna University (Högskolan Dalarna) P.O. Box 175, 791 88 Falun, Sweden Visit: Röda vägen 3, Borlänge Tel. +46(0)23-77 87 60 email:wso@du.se=