DBXplorer: A System for Keyword B ased Search over Relational Databases

1. DBXplorer: A System for Keyword Based Search over Relational Databases Sanjay Agrawal Microsoft Research SurajitChaudhuri Microsoft Research Gautam Das Microsoft Research Presented by: DEEP PANCHOLI 1000556121

2. Introduction • The two most common types of search are Structured Search and Keyword Based Search • Example of Structured Search http://autos.yahoo.com/ • A similar example is to search for books in bookseller’s database e.g. Books->Travel->Maps->Asia->Russia • We all already know what is keyword based search and one example can be searching for ‘Jim Gray’ on Microsoft Intranet to obtain matched rows

3. Introduction (cont) • Problems faced with Keyword based search implementation • Need to know schema • Normalized databases • Availability of indexes • Built on the concept laid by BANKS paper explained in the last lecture. • Symbol tables • Compacting the symbol tables • Search requirements

4. Overview of DBXplorer • DBXplorer returns all rows either from single table or from multiple tables, using FK-joins, such that each row has all the keywords • Publish • 1. Identify a database and tables and columns within it that are to be enabled for search • 2. Create auxiliary tables (Symbol Tables) • Search • 1. Look up the Symbol table • 2. Searching in possible subsets of tables • 3. Construct and execute SQL statement and rank the results before displaying to user

5. Different Symbol Table Designs • We will only consider exact match problem • Two important levels of granularities • Column level granularity (Pub-Col) • Cell level granularity (Pub-Cell)

6. Factors that affect granularity • Space and time requirements • Pub-Col is faster and occupies less space • Keyword search performance • Pub-Col if there is an index on the column • Ease of Symbol table maintenance • Pub-Col is easier to maintain as it contains updates only if there is addition of a new distinct values • Hence, the Pub-Col alternative is almost always better than Pub-Cell unless if certain columns contain no indexes • If an index is available for column, we should use Pub-Col granularity

7. Pub-Col representation • Store simply as Keyword-ColId • Alternative is to use Hashvalue-ColId since storing keywords is wasteful as strings can be long and of varying lengths • Compression Algorithms • FK-Comp: If column c1 is a subset of values in another column c2, we retain only values in c1 • CP-Comp: It is used when pairs of columns share common keywords but are not tied by FK

8. Pub-Col Algorithm

9. Search Component • Common step for all kinds of granularities • It makes use of join trees • Hence, if we join tables that occur in the join tree the resulting relation will contain all potential rows containing all keywords specified in the query • Example of graph tree • Finally SQL query is generated and run • The result is then ranked before outputting. The basic approach is to rank them based on the number of joins involved which is similar to Banks approach

10. Search Algorithm

11. Case of Token matches • Token matches are matches in which keyword match with a token or a substring of attribute value • Pub-Prefix method efficiently enables token match capabilities by exploiting available B+ tree indexes • Symbol table has entry (hash(k),T.C, P)

12. Case of Token matches (cont) • Pub-Prefix method result is comparable to Pub-Cell method when the column width is small (i.e. less than 100 characters) • For columns where strings are greater than hundreds of characters, Pub-Cell outperforms Pub-Prefix significantly • Important issue is to determine the appropriate prefix length stored in symbol table. However, Pub-Prefix method is still being researched upon • Other research is going on in field of stemming of query keywords

13. Experimental Results • The experiments were carried out on a 450MHz 256 MB Intel P-3 machine. There were 4 databases used for evaluation: • TPC-H data of sizes from 100 to 500 MB • USR is Microsoft employee address DB of 130 MB with 19 tables • ML is a 375 MB mailing list DB with 38 tables • KB is a 365 MB DB with 84 tables containing information on articles and help manuals on various shipped products

14. System Architecture for DBXplorer

15. Experimental Results (cont) • In particular the authors show the following: • Pub-Col is compact compared to Pub-Cell • Pub-Col scales linearly with data size and is independent of data distribution • Pub-Prefix is compact compared to Pub-Cell and has a significantly better performance when full text indexes are not present

16. Pub-Col and Pub-Cell symbol table size comparison

17. Symbol table publishing time comparison

18. Query performance

19. Other Observations • It was also noticed that search scales with number of query keywords. The query was varied with 2 to 10 keywords and still the average query time was between 1 to 1.3 seconds • Also, it was noticed that FK-Comp and CP-Comp reduce the size of Pub-Col by a factor of 0.45 to 0.90 depending on size of original table • However, it was noticed that compression added a negligible overhead on search performance

20. Effectiveness of Pub-Prefix method • The Pub-prefix method was tested on workload consisting of 100 random keywords from character column of width 64 bytes in the KB database. • It was noticed that the performance of Pub-Prefix increased with increase in Pub-Prefix length and gave the optimum performance at prefix-length of 8 • This is because as the length increases, beyond a certain limit the optimizer decides to scan the original table compared to index search

21. Conclusion • Although, we discussed only about a single database query, this technique can be applied to search multiple databases also • DBXplorer is easy to use with any Database Management system • As mentioned before, the Pub-Col alternative is the best when columns have indexes on them. A hybrid table can be created so that if there is an index for a column, we use Pub-Col granularity and if there is no index, we use Pub-Cell granularity