A Greedy Approach to Concurrent Processing of Frequent Itemset Queries

We consider the problem of concurrent execution of multiple frequent itemset queries. If such data mining queries operate on overlapping parts of the database, then their overall I/O cost can be reduced by integrating their dataset scans. The integration requires that data structures of many data mining queries are present in memory at the same time. If the memory size is not sufficient to hold all the data mining queries, then the queries must be scheduled into multiple phases of loading and processing. Since finding the optimal assignment of queries to phases is infeasible for large batches of queries due to the size of the search space, heuristic algorithms have to be applied. In this paper we formulate the problem of assigning the queries to phases as a particular case of hypergraph partitioning. To solve the problem, we propose and experimentally evaluate two greedy optimization algorithms.