Interactive join query inference with JIM

Interactive Join Query Inference with JIM

Specifying join predicates may become a cumbersome task in many situations e.g., when the relations to be joined come from disparate data sources, when the values of the attributes carry little or no knowledge of metadata, or simply when the user is unfamiliar with querying formalisms. Such task is recurrent in many traditional data management applications, such as data integration, constraint ...

Interactive Inference of Join Queries

We investigate the problem of inferring join queries from user interactions. The user is presented with a set of candidate tuples and is asked to label them as positive or negative depending on whether or not she would like the tuples as part of the join result. The goal is to quickly infer an arbitrary n-ary join predicate across two relations by keeping the number of user interactions as mini...

New bucket join algorithm for faster join query results

Join is the most expensive and the frequent operation in database. Significant numbers of join queries are executed in the interactive applications. In interactive applications the first few thousand results need to be produced without any delay. The current join algorithms are mainly based on hash join or sort merge join which is less suitable for interactive applications because some pre-work...

JOIN(X): Constraint-Based Type Inference for the Join-Calculus

Query join ordering optimization with evolutionary multi-agent systems

This work presents an evolutionary multi-agent system applied to the query optimization phase of Relational Database Management Systems (RDBMS) in a non-distributed environment. The query optimization phase deals with a known problem called query join ordering, which has a direct impact on the performance of such systems. The proposed optimizer was programmed in the optimization core of the H2 ...