Scalable keyword search over relational data streams by aggressive candidate network consolidation

Keyword search over relational streams is useful when allowing users to query on streams without understanding the details about the streams and query language as well. There have been several research works on this direction, and the state-of-the-art approaches exploit Candidate Networks (CNs), which are schema-level descriptions of possible joining networks of tuples, and generate query plans based on CNs. However, in fact, the performance of these approaches seriously degrades in particular when the maximum size of CNs (T-max) and/or the number of query keywords are large due to the explosive increase in the number of CNs. To cope with this problem, we propose a novel query plan called MX-structure to consolidate CNs as much as possible. We suppress explosive blowup of nodes in query plans by consolidating all common edges among CNs. The experimental results prove that the proposed algorithm performs much better than the state-of-the-art approaches. (C) 2018 Elsevier Ltd. All rights reserved.