
Time-aware collective spatial keyword query
Author(s) -
Zijun Chen,
Tingting Zhao,
Wenyuan Liu
Publication year - 2021
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis200131034c
Subject(s) - computer science , relevance (law) , information retrieval , query optimization , query expansion , pruning , scalability , sargable , query language , web query classification , spatial query , web search query , data mining , object (grammar) , query by example , search engine , database , artificial intelligence , agronomy , biology , political science , law
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.