Name
Abstract | ||
|---|---|---|
In modern applications, spatial objects are often annotated with textual descriptions, and users are offered the opportunity to formulate spatio-textual queries. The result set of such a query consists of spatio-textual objects ranked according to their distance from a desired location and to their textual relevance to the query. In this context, a challenging problem is how to select a set of at most b keywords to enhance the description of the facilities of a spatial object, in order to make the object appear in the top-k results of as many users as possible. In this paper, we formulate this problem, called Best-terms and we show that it is NP-hard. Hence, we present a baseline algorithm that provides an approximate solution to the problem. Then, we introduce a novel algorithm for keyword selection that greatly improves the efficiency of query processing. By means of a thorough experimental evaluation, we demonstrate the performance gains attained by our approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-22363-6_22 | ADVANCES IN SPATIAL AND TEMPORAL DATABASES (SSTD 2015) |
Field | DocType | Volume |
Inverted index,Data mining,Ranking,Result set,Computer science,Approximate solution | Conference | 9239 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Orestis Gkorgkas | 1 | 119 | 4.97 |
| Akrivi Vlachou | 2 | 751 | 39.95 |
| Christos Doulkeridis | 3 | 899 | 55.91 |
| Kjetil Nørvåg | 4 | 1311 | 79.26 |