Project Description 198:541. Query Processing Project 1. Exact query answering using standard indexes 2. Advanced query processing  Multidimensional.

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Presentation transcript:

Project Description 198:541

Query Processing Project 1. Exact query answering using standard indexes 2. Advanced query processing  Multidimensional  Text data  Top-k query model

Implementation Details  Choice of C++ or Java  Data storage You do not have to implement disk storage… but you can. You can use a DBMS for storage but you have to implement your own indexes. You can simulate disk rid by a hash table to access full tuples For the purpose of this project main-memory implementation is fine, but it might be easier for you to have something more persistent  Single or multiple tables You can have joins in the advanced query processing part of the project

Step 1: Finding Data  You should find a dataset Multi-attributes (3-4 minimum) At least 1000 data points  Domain Numeric values Some text fields if you want to look into IR techniques  Find data on which you can ask meaningful queries (exact and advanced)  Sources: Census data Weather statistics Bibliographical data Sales data (amazon)…

Step 2: Exact Query Processing  Deciding on meaningful indexes for your application  Bulk loading indexes (type is data and query dependent) B+ tree Hash tables  Answering exact queries Single-attributes Multi-attributes (merging single attributes results)

Step 3: Advanced Query Processing Numeric Data  Multidimensional Indexes Multidimensional range query processing  Skyline Queries Find the best undominated tuples in the data set Related: maximize a function of the attributes values  Top-k Query Processing, Nearest-Neighbor Queries Smart index accesses based on preferred results values  Join optimization using specific join indexes

Step 3: Advanced Query Processing Numeric and Text Data  IR techniques for text-only query Inverted lists Indexes  Exact Queries  Top-k queries (tf.idf scores)  Text and value queries Exact queries: find articles written in 2004 with “XML Path Indexes” in their abstract Top-k Queries  Exact matching on text, ranking on numeric value  Exact matching on numeric values, ranking on text  Ranking on both numeric values and text More research-oriented