The collective spatial keyword query is a hot research topic in the database
community in recent years, which considers both the positional relevance to
the query location and textual relevance to the query keywords. However, in
real life, the temporal information of object is not always valid. Based on
this, we define a new query, namely time-aware collective spatial keyword
query (TCoSKQ), which considers the positional relevance, textual relevance,
and temporal relevance between objects and query at the same time. Two
evaluation functions are defined to meet different needs of users, for each
of which we propose an algorithm. Effective pruning strategies are proposed
to improve query efficiency based on the two algorithms. Finally, the
experimental results show that the proposed algorithms are efficient and
scalable.
Time-aware collective spatial keyword query
