The widespread use of XML in business and scientific databases has prompted the development of methodologies, techniques, and systems for effectively managing and analyzing XML data. This has increasingly attracted the attention of different research communities, including database, information retrieval, pattern recognition, and machine learning, from which several proposals have been offered to address problems in XML data management and knowledge discovery. XML Data Mining: Models, Methods, and Applications aims to collect knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods, and systems for XML data mining. This book addresses key issues and challenges in XML data mining, offering insights into the various existing solutions and best practices for modeling, processing, analyzing XML data, and for evaluating performance of XML data mining algorithms and systems.On the expressiveness of probabilistic XML models. ... F. Matthes, M. Hatzopoulos, K. BAphm, A. Kemper, T. Grust, aamp; C. BAphm (Eds.), Proceedings of the 10th International Conference on Extended Database Technology (EDBT), (pp. ... MYSTIQ: A system for finding more answers by using probabilities. ... An introduction to database systems (3rd 49 Modeling, Querying, and Mining Uncertain XML Data.
|Title||:||XML Data Mining: Models, Methods, and Applications|
|Publisher||:||IGI Global - 2011-11-30|