Abstract
Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K -relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations.
Highlights
There is renewed interest in temporal databases fueled by the fact that abundant storage has made long term archival of historical data feasible
Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot
We demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference
Summary
There is renewed interest in temporal databases fueled by the fact that abundant storage has made long term archival of historical data feasible. Overlap between multiple periods associated with a tuple and unnecessary splits of periods complicate the interpretation of data and, should be avoided if possible Given these limitations and the lack of implementations for snapshot semantics queries over bag relations, users currently resort to manually implementing such queries in SQL which is time-consuming and error-prone [40]. We address the above limitations of previous approaches for snapshot semantics and develop a framework based on the following desiderata: (i) support for set and multiset relations, (ii) snapshot-reducibility for all operations, and (iii) a unique interval-based encoding of temporal relations. Previous works on sequenced semantics [7, 16, 18] aim to support snapshotreducibility These approaches focus on change preservation (i.e., preserve intervals from the input of a query), whereas we instead focus on a unique encoding. We demonstrate experimentally that we do not need to sacrifice performance to achieve correctness
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