Scientific and Statistical Database Management (eBook, PDF)

21st International Conference, SSDBM 2009, New Orleans, LA, USA, June 2-4, 2009 Proceedings
Redaktion: Winslett, Marianne

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 21st International Conference on Scientific and Statistical Database Management, SSDBM 2009, held in New Orleans, LA, USA in June 2009.

The 29 revised full papers and 12 revised short papers including poster and demo papers presented together with three invited presentations were carefully reviewed and selected from 76 submissions. The papers are organized in topical sections on improving the end-user experience, indexing, physical design, and energy, application experience, workflow, query processing, similarity search, mining, as well as spatial data.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 648
• Erscheinungstermin: 21. Mai 2009
• Englisch
• ISBN-13: 9783642022791
• Artikelnr.: 44129941

Inhaltsangabe

Invited Presentation.- The Scientific Data Management Center: Providing Technologies for Large Scale Scientific Exploration.- Improving the End-User Experience.- Query Recommendations for Interactive Database Exploration.- Scientific Mashups: Runtime-Configurable Data Product Ensembles.- View Discovery in OLAP Databases through Statistical Combinatorial Optimization.- Designing a Geo-scientific Request Language - A Database Approach.- SEEDEEP: A System for Exploring and Querying Scientific Deep Web Data Sources.- Expressing OLAP Preferences.- Indexing, Physical Design, and Energy.- Energy Smart Management of Scientific Data.- Data Parallel Bin-Based Indexing for Answering Queries on Multi-core Architectures.- Finding Regions of Interest in Large Scientific Datasets.- Adaptive Physical Design for Curated Archives.- MLR-Index: An Index Structure for Fast and Scalable Similarity Search in High Dimensions.- Application Experience.- B-Fabric: An Open Source Life Sciences Data Management System.- Design and Implementation of Metadata System in PetaShare.- Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA.- Invited Presentation.- What Makes Scientific Workflows Scientific?.- Workflow.- Enabling Ad Hoc Queries over Low-Level Scientific Data Sets.- Exploring Scientific Workflow Provenance Using Hybrid Queries over Nested Data and Lineage Graphs.- Data Integration with the DaltOn Framework - A Case Study.- Experiment Line: Software Reuse in Scientific Workflows.- Tracking Files in the Kepler Provenance Framework.- BioBrowsing: Making the Most of the Data Available in Entrez.- Using Workflow Medleys to Streamline Exploratory Tasks.- Query Processing.- Experiences on Processing Spatial Data with MapReduce.-Optimization and Execution of Complex Scientific Queries over Uncorrelated Experimental Data.- Comprehensive Optimization of Declarative Sensor Network Queries.- Efficient Evaluation of Generalized Tree-Pattern Queries with Same-Path Constraints.- Mode Aware Stream Query Processing.- Evaluating Reachability Queries over Path Collections.- Similarity Search.- Easing the Dimensionality Curse by Stretching Metric Spaces.- Probabilistic Similarity Search for Uncertain Time Series.- Reverse k-Nearest Neighbor Search Based on Aggregate Point Access Methods.- Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation.- Keynote Address.- Cloud Computing for Science.- Mining.- Classification with Unknown Classes.- HSM: Heterogeneous Subspace Mining in High Dimensional Data.- Split-Order Distance for Clustering and Classification Hierarchies.- Combining Multiple Interrelated Streams for Incremental Clustering.- Improving Relation Extraction by Exploiting Properties of the Target Relation.- Cor-Split: Defending Privacy in Data Re-publication from Historical Correlations and Compromised Tuples.- A Bipartite Graph Framework for Summarizing High-Dimensional Binary, Categorical and Numeric Data.- Spatial Data.- Region Extraction and Verification for Spatial and Spatio-temporal Databases.- Identifying the Most Endangered Objects from Spatial Datasets.- Constraint-Based Learning of Distance Functions for Object Trajectories.