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Relaxing Queries. Presented by Ashwin Joshi Kapil Patil Sapan Shah. Motivation . Complex queries in terms of predicates against large databases. Right parameter values not known. Parameter adjustment becomes a trial-and-error method. More the predicates, difficult the adjustment.
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Relaxing Queries Presented by Ashwin Joshi KapilPatil Sapan Shah
Motivation • Complex queries in terms of predicates against large databases. • Right parameter values not known. • Parameter adjustment becomes a trial-and-error method. • More the predicates, difficult the adjustment
Example • How many conditions to adjust?? • How much to adjust?? • No. of choices exponential in number of conditions
In an information retrieval system, a direct, literal response is not always the best answer to a query. • This is particularly the case when the set of results produced by the query is empty. • A co-operative response is an indirect response that is more helpful to the user than the direct response would be. • Co-operative responses emerged in the context of natural-language question-answering.
Expected solution • For a failing query, this tool will provide a relaxed query. This relaxed query will have a small difference from the original query, but will return a few tuples when fired upon the target database. • While relaxing the original query, various queries resulting from the relaxation will be taken into consideration. The query which has a non-empty result will be chosen as the final result.
Steps required • Three steps • Exacting domain knowledge • Finding the most useful rule • Relaxing the failing query
Step 1: Extracting domain knowledge • In this step system uses the small, randomly-chosen subset of the target database to discover knowledge that can be used for query relaxation. The original query will be fired upon the target subset and for a relaxed constraint, results will be achieved. • Thus we will get a set of tuples that satisfy this relaxed query. From this subset, we can derive the knowledge as the intersection of all these attribute domains. Hence, we will have a heading towards the relaxed query. This knowledge is also dependant on the weighted impact of various attribute on the query.
Step 2: Finding the most useful rule • In this step we select the most useful rule among all the possible rules. • After deriving various rules that can lead to a relaxed query, a single rule needs to be defined for the final relaxation of the original query. • The nearest rule will be the rule that has the minimum distance from the original query. Again for this portion, the weighted impact of various attributes will play an important role. • The distance will be the aggregated sum of the distances of individual attributes.
Step 3: Relaxing the failing query • Here we implement an algorithm that will relax the failing query. • The algorithm will take the nearest rule from the step2 of the development and will derive relaxed query from the nearest rule. • The original query will be transformed into the relaxed query through this process and the relaxed query will return some results when fired upon the target database.
