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Review of CIKM 2009

Review of CIKM 2009. Da Zhou 2009-11-08. Outline. Flash-based Database Poster Demo Conclusion. Flash-based Database. RS-Wrapper: Random Write Optimization for Solid State Drive A Flexible Simulation Environment for Flash-aware Algorithms

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Review of CIKM 2009

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  1. Review of CIKM 2009 Da Zhou 2009-11-08

  2. Outline • Flash-based Database • Poster • Demo • Conclusion

  3. Flash-based Database • RS-Wrapper: Random Write Optimization for Solid State Drive • A Flexible Simulation Environment for Flash-aware Algorithms • Dynamic In-Page Logging for Flash-Aware B-Tree Index • Gap-Joo Na (Sungkyunkwan University)Sang-Won Lee (Sungkyunkwan University)Bongki Moon (University of Arizona) • Experiments • People

  4. Outline • Flash-based Database • Posters • Demo • Conclusion

  5. Information Retrieval: Citation Analysis, Social Networks for IR Domain-specific IR Filtering Foundation of Information Retrieval IR Architectures, Scalability and Efficiency IR Evaluation Language Specific IR Machine Learning for IR Multimedia IR Semi-Structured Information Retrieval User Modeling for IR, Search Personalization Web Search, Advertising, Adversarial Knowledge Management: Advertising and Optimization Classification and Clustering Data pre- and post-Processing Information Extraction Information Filtering and Recommender Systems Knowledge Synthesis and Visualization Large-scale statistical techniques Link and Graph Mining Semantic Techniques Temporal and Spatial data Mining Text Mining Posters

  6. Posters • Acronym Extraction • Training data: full name, context • Existing method: context-based • New idea: Context-based + Links between pages • Dataset: web pages in intranet • Review assignment • Motivation: According to research topic leads too much papers are assigned to a few persons • Greedy algorithm • Integer linear programming algorithm • Dataset: Gold standard data

  7. Posters • Construct Wikis • Time consuming, Laborious • Snippet extraction and selection • Distance and influence for snippet • What make categories difficult to classify? • Data itself • The characteristics of data

  8. Posters • A word clustering approach for language model-based sentence retrieval in question answering system • Word-based model • Class-based model • N-gram model • Add a variance into the formula

  9. Posters • A Collaborative Filtering Approach to Ad Recommendation using the Query-Ad Click Graph Discount shoes A.com Cheap shoes B.com Running shoes C.com

  10. Posters • URL Normalization for de-duplication of web page • URL 1: t1, t2, t3, t4 • URL 2: t1, t2, t3, t_title • T4= t_title

  11. Demo • OfCourse: Web Content Discovery, Classification and Information Extraction for Online Course Materials • XQGen - An Algebra-based XPath Query Generator for Micro-Benchmarking

  12. Conclusion • Limited improvement on old topics • More factors are considered • Minor improvement for existing method • Applying existing method in wider applications • DB is a small group • DB is closer to system than IR • IR is closer to daily life than DB

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