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Database Theory and Application: International Conference, DTA 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009, Proceedings / Edition 1
by Dominik Slezak
As future generation information technology (FGIT) becomes specialized and fr- mented, it is easy to lose sight that many topics in FGIT have common threads and, because of this, advances in one discipline may be transmitted to others. Presentation of recent results obtained in different disciplines encourages this interchange for the advancement of FGIT as a whole
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As future generation information technology (FGIT) becomes specialized and fr- mented, it is easy to lose sight that many topics in FGIT have common threads and, because of this, advances in one discipline may be transmitted to others. Presentation of recent results obtained in different disciplines encourages this interchange for the advancement of FGIT as a whole. Of particular interest are hybrid solutions that c- bine ideas taken from multiple disciplines in order to achieve something more signi- cant than the sum of the individual parts. Through such hybrid philosophy, a new principle can be discovered, which has the propensity to propagate throughout mul- faceted disciplines. FGIT 2009 was the first mega-conference that attempted to follow the above idea of hybridization in FGIT in a form of multiple events related to particular disciplines of IT, conducted by separate scientific committees, but coordinated in order to expose the most important contributions. It included the following international conferences: Advanced Software Engineering and Its Applications (ASEA), Bio-Science and Bio-Technology (BSBT), Control and Automation (CA), Database Theory and Application (DTA), D- aster Recovery and Business Continuity (DRBC; published independently), Future G- eration Communication and Networking (FGCN) that was combined with Advanced Communication and Networking (ACN), Grid and Distributed Computing (GDC), M- timedia, Computer Graphics and Broadcasting (MulGraB), Security Technology (SecTech), Signal Processing, Image Processing and Pattern Recognition (SIP), and- and e-Service, Science and Technology (UNESST).
Product Details
- ISBN-13:
- 9783642105821
- Publisher:
- Springer Berlin Heidelberg
- Publication date:
- 12/01/2009
- Series:
- Communications in Computer and Information Science Series, #64
- Edition description:
- 2009
- Pages:
- 185
- Product dimensions:
- 6.10(w) x 9.10(h) x 0.50(d)
Table of Contents
Steganalysis for Reversible Data Hiding.- An Incremental View Maintenance Approach Using Version Store in Warehousing Environment.- The Study of Synchronization Framework among Multi-datasets.- Clustering News Articles in NewsPage.com Using NTSO.- Categorizing News Articles Using NTC without Decomposition.- A Comparative Analysis of XML Schema Languages.- Mining Approximate Frequent Itemsets over Data Streams Using Window Sliding Techniques.- Preserving Referential Integrity Constraints in XML Data Transformation.- Know-Ont: Engineering a Knowledge Ontology for an Enterprise.- Transformation of Data with Constraints for Integration: An Information System Approach.- Comparative Analysis of XLMiner and Weka for Association Rule Mining and Clustering.- Infobright for Analyzing Social Sciences Data.- Enhanced Statistics for Element-Centered XML Summaries.- Algorithm for Enumerating All Maximal Frequent Tree Patterns among Words in Tree-Structured Documents and Its Application.- A Method for Learning Bayesian Networks by Using Immune Binary Particle Swarm Optimization.- A Semantics-Preserving Approach for Extracting OWL Ontologies from UML Class Diagrams.- Data Warehousing and Business Intelligence: Benchmark Project for the Platform Selection.- Automatic Extraction of Decision Rules from Non-deterministic Data Systems: Theoretical Foundations and SQL-Based Implementation.- Soft Set Approach for Maximal Association Rules Mining.- Soft Set Theoretic Approach for Dimensionality Reduction.- Rough Set Approach for Categorical Data Clustering.
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