Query Suggestion for Geo-Textual Data


Grant Data
Project Title
Query Suggestion for Geo-Textual Data
Principal Investigator
Professor Mamoulis, Nikolaos   (Principal investigator)
Co-Investigator(s)
Dr Cheng Chun Kong   (Co-Investigator)
Duration
36
Start Date
2016-05-31
Completion Date
2019-05-30
Amount
496028
Conference Title
Presentation Title
Keywords
query suggestion, location-based search, geo-textual data
Discipline
Database and data science
Panel
Engineering (E)
Sponsor
RGC General Research Fund (GRF)
HKU Project Code
17205015
Grant Type
General Research Fund (GRF)
Funding Year
2015/2016
Status
On-going
Objectives
1) [Design of effective query suggestion models for geo-textual data] We will design novel LAKQS models that consider both the relevance of the suggested queries to the original query and the proximity of their results to the query issuer's location; 2) [Evaluation of query suggestion models] We will evaluate the effectiveness of the designed LAKQS models with the help of open-source real data (e.g., AOL query logs, tweets). We will also develop an evaluation platform; using it we will collect feedback from real users about the suitability of the suggested queries; 3) [Development of access methods and algorithms for efficient location-aware query suggestion] We plan to develop scalable techniques for real-time LAKQS, based on the most effective models from those that have been designed.