Along with the proliferation of online digital map and locationbased service, very large POI (point of interest) databases have been constructed where a record corresponds to a POI with information including name, category, address, geographical location and other features. A basic spatial query in POI database is POI retrieval. In many scenarios, a user cannot provide enough information to pinpoint the POI except some clue. For example, a user wants to identify a café in a city visited many years ago. SHe cannot remember the name and address but she still recalls that "the café is about 200 meters away from a restaurant; and turning left at the restaurant there is a bakery 500 meters away, etc.". Intuitively, the clue, even partial and approximate, describes the spatiotextual context around the targeted POI. Motivated by this observation, this work investigates clue-based spatio-textual query which allows user providing clue, i.e., some nearby POIs and the spatial relationships between them, in POI retrieval. The objective is to retrieve k POIs from a POI database with the highest spatio-textual context similarities against the clue. This work has deliberately designed data-quality-tolerant spatio-textual context similarity metric to cope with various data quality problems in both the clue and the POI database. Through crossing valuation, the query accuracy is further enhanced by ensemble method. Also, this work has developed an index called roll-out-star R-tree (RSR-tree) to dramatically improve the query processing efficiency. The extensive tests on data sets from the real world have verified the superiority of our methods in all aspects.
Liu et al. proposed the clue-based spatio-textual query REF which takes a set of keywords and a clue as inputs, and returns k objects with highest similarities against the clue.
Clue-based Spatio-textual Query
PVLDB
PVLDB
Computer Science