Name
Abstract | ||
|---|---|---|
Nowadays, sensors are cheap, easy to deploy and immediate to integrate into applications. Since huge amounts of sensor data can be generated, selecting only relevant data to be saved for further usage, e.g. long-term query facilities, is still an issue. In this paper, we adapt the declarative approach developed in the seventies for database design and we apply it to sensor data streams. Given sensor data streams, the key idea is to consider both spatio-temporal dimensions and Spatio-Temporal Functional Dependencies as first class-citizens for designing sensor databases on top of any relational database management system. We propose an axiomatisation of these dependencies and the associated attribute closure algorithm, leading to a new normalization algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-64367-0_10 | ADVANCES IN SPATIAL AND TEMPORAL DATABASES, SSTD 2017 |
Field | DocType | Volume |
Data mining,Data stream mining,Normalization (statistics),Computer science,Database design,Functional dependency,Relational database management system | Conference | 10411 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manel Charfi | 1 | 0 | 0.68 |
| Yann Gripay | 2 | 29 | 6.88 |
| Jean-marc Petit | 3 | 820 | 156.09 |