Loading...
Please wait, while we are loading the content...
Similar Documents
On continuously monitoring the top-k moving objects with relational group and score functions
| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Donghui Tsotras, Vassilis J. Wen, Jian |
| Abstract | With the wide usage of location tracking system, continuously mining relationships among moving objects over their location changes is possible and also important to many real applications. This paper shows a novel continuous location-based query, called continuous relational top-k query, or CRTQ, which continuously monitors the k moving objects with the most significant relations with other objects by user-defined relational group and score functions. Although this kind of query can be implemented as a special case of the top-k join query using SQL, this straight-forward way is too expensive to be applicable widely. This paper also discusses the properties of this novel query, which leads to the flexibility of the query and also the difficulty for a generalized solution. An efficient algorithm is proposed for a special type of CRTQ over spatial data set with closeness grouping function and monotone increasing score function. Finally the main contributions of this showcase and also our future research plans are discussed. |
| Starting Page | 5 |
| Ending Page | 10 |
| Page Count | 6 |
| File Format | |
| ISSN | 19467729 |
| DOI | 10.1145/1645424.1645426 |
| Journal | SIGSPATIAL Special (SIGSPACIAL) |
| Volume Number | 1 |
| Issue Number | 3 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-06-21 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Top-k Query Relational Algorithm |
| Content Type | Text |
| Resource Type | Article |
