Sanjay Agrawal Microsoft Research Surajit Chaudhuri Microsoft Research Gautam Das Microsoft Research DBXplorer: A System for Keyword Based Search over Relational Databases Presented by: DEEP PANCHOLI

Introduction The two most common types of search are Structured Search and Keyword Based Search Example of Structured Search A similar example is to search for books in booksellers 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

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

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

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) Table=Authors FnameLname JohnMarshall JohnShawn Archer JacquelynMarshall

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

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

Pub-Col Algorithm

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

Search Algorithm

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)

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

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

System Architecture for DBXplorer

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

Pub-Col and Pub-Cell symbol table size comparison

Symbol table publishing time comparison

Query performance

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

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

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