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Efficient Multi-parametric Query Execution with the Prefer System

This presentation explores the Prefer system, which enhances the efficient execution of multi-parametric ranked queries in databases. It highlights the importance and varying weights of different attributes in determining query results, particularly focusing on how attributes like house price and bedrooms carry more significance than others, such as age. We discuss the solution involving ranked materialized views and a greedy view selection algorithm to cover the query space, ultimately improving response time and accuracy. This system facilitates better database decisions through advanced query algorithms.

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Efficient Multi-parametric Query Execution with the Prefer System

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  1. Prefer: A System for the Efficient Execution of Multi-parametric Ranked Queries Vagelis Hristidis Nick Koudas Uannis Papakonstantinou Presented By: Amrita Tamrakar 09-Mar-2006

  2. Contents • Introduction • Importance/Weights of the attributes • The Efficiency and response time ?? • Solution • View Selection • Pipelining Algorithm • Conclusion

  3. Prefer • Prefer is a layer on top of commercial database which allows efficient evaluation of multi-parametric databases. • Different attributes may have diff weight • House price and bedrooms have more weight than the age. • Cheap houses may be old and small (so search more)

  4. Importance of Attributes • Preference is the keyword Weights Preference function a1A1+a2A2 +….+Anan Attributes tuples

  5. Prefer Interface

  6. Example Preference = (0.1,0.6,0.3) , Score 10*0.1+17*0.6+20*0.3

  7. Efficiency and Response Time • Retrieve the whole Table • Apply the preference function on each tuple • Sort the tuples Prf fn Relation Score Sort Top k tuples ……….……….

  8. Solution Proposed • Ranked Materialized view • 10-20 views can have most possible queries Queries with linear preference function ∑ Preference vector v = {v1,....vn} Attribute preference

  9. Prefer Architechture Query Preprocessing Stage Select Materialized View Best View for the query Materialized Views Execute Pipelining Algorithm Output Results Finding the top k and sorting View Selection

  10. View Selection • The union of views will cover the whole space • Outputs view sequence V such that for every query q there is at least one view that covers q • Greedy view selection algorithm for space constraints • Given a set of views { Rv1….Rvn }that covers [0,1]k space,select C views that maximize the number of points in [0,1]k covered.

  11. Pipelining Ranked query using ranked view • T = DeteremineWatermark() • Scan Rv for tuples greater than T • Sort these tuples by fq and mark as processed • Continue for unprocessed tuples till top –k is retrieved v R v Rv1 Ranked tuples Query q tqi tq1 R q

  12. Example Q=(0.1,0.6,0.3) v=(0.2,0.4,0.4) Rv1 The first watermark= 14.26 Sort by fq Till k tuples are retrieved

  13. Conclusion • Introduction of database selection queries with user preferences • Use of multiple DB views • Algorithms to select the best view • Practical application via PREFER

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