By Divyakant Agrawal, Amr El Abbadi, Shyam Antony, Sudipto Das (auth.), Shinji Kikuchi, Shelly Sachdeva, Subhash Bhalla (eds.)
This e-book constitutes the refereed lawsuits of the sixth overseas Workshop on Databases in Networked details structures, DNIS 2010, held in Aizu-Wakamatsu, Japan in October 2010. The thirteen revised complete papers offered including nine invited talks and 1 keynote lecture have been conscientiously reviewed and chosen for inclusion within the publication. The workshop typically places the main target on information semantics and infrastructure for info administration and interchange. The papers are geared up in topical sections on networked info structures: infrastructure, entry to details assets, info and information administration structures, info extraction from information assets, and geo-spatial determination making.
Read or Download Databases in Networked Information Systems: 6th International Workshop, DNIS 2010, Aizu-Wakamatsu, Japan, March 29-31, 2010. Proceedings PDF
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Additional info for Databases in Networked Information Systems: 6th International Workshop, DNIS 2010, Aizu-Wakamatsu, Japan, March 29-31, 2010. Proceedings
On-line applications had no appreciable amount of historical data because they jettisoned their historical data as quickly as possible in the name of high performance. Thus, corporations had lots of data and very little information. For 15 years, people built different manifestations of data warehouses where the focus was on structured data extracted from OLTP databases. Increasingly, enterprises have come to realize that for business intelligence applications supporting decision making, information derived from unstructured and semi-structured data sources is also needed.
Second, we needed to define and compute a similarity measure between each pair of query feature vectors and between each pair of performance feature vectors. KCCA uses a kernel function to compute this similarity measure and we use the common “Gaussian” kernel, as described in . 4E 80E 3E KCCA Predicted Performance Vector 128 25792 Performance Projection 8E Fig. 7. Prediction: KCCA projects a new query’s feature vector, then looks up its neighbors from the performance projection and uses their performance vectors to derive the new query’s predicted performance vector Given these features matrices and the similarity measures, KCCA then projects the feature vectors into new high-dimensional subspaces — a query projection and a performance projection — where nearness indicates similarity.
Of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 575– 586 (2005) 6. : Estimating Progress of Execution for SQL Queries. In: Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 803–814 (2004) 7. : Dynamic Resource Brokering for Multi-User Query Execution. In: Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 281–292 (1995) 8. : Managing operational business intelligence workloads. SIGOPS Oper. Syst. Rev. 43(1), 92–98 (2009) 9. : How Input Data Sets Change Program Behaviour.
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