Name
Title | ||
|---|---|---|
Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases |
Abstract | ||
|---|---|---|
A geo-referenced time series database represents the data generated by a set of fixed locations (or items) observing a particular phenomenon over time. Useful information that can facilitate the users to achieve socio-economic development lies hidden in this data. This paper introduces a novel model of Fuzzy Geo-referenced Periodic-Frequent Patterns (FGPFPs) that may exist in these databases. An FGPFP represents a set of frequently occurring neighboring items observed at regular intervals in a database. For example, an FGPFP in a traffic congestion database represents a set of neighboring road segments where people have regularly faced congestion problems. A novel pruning technique has been presented to effectively reduce the search space and the computational cost of finding the desired patterns. We have also proposed an efficient depth-first search algorithm to find all the desired patterns. Experimental results demonstrate that the proposed algorithm is efficient. Finally, we demonstrate our model's usefulness by performing traffic congestion analytics. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/FUZZ-IEEE55066.2022.9882785 | 2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) |
Keywords | DocType | ISSN |
Big data, time series, pattern mining | Conference | 1544-5615 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pamalla Veena | 1 | 0 | 0.68 |
| Penugonda Ravikumar | 2 | 0 | 0.68 |
| Kundai Kwangwai | 3 | 0 | 0.34 |
| R. Uday Kiran | 4 | 0 | 0.68 |
| Kazuo Goda | 5 | 41 | 10.24 |
| Yutaka Watanobe | 6 | 0 | 0.68 |
| Koji Zettsu | 7 | 0 | 1.69 |