Mining Swarm Patterns in Sliding Windows over Moving Object Data Streams
| Autor: | Kuldeep Sharma, Srijay Deshpande, Alka Bhushan, Nandlal L. Sarda, Umesh Bellur |
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| Rok vydání: | 2017 |
| Předmět: |
Data stream mining
Computer science 020204 information systems Sliding window protocol Real-time computing 0202 electrical engineering electronic engineering information engineering Key (cryptography) Swarm behaviour Overhead (computing) Window (computing) 02 engineering and technology Object (computer science) Cluster analysis |
| Zdroj: | SIGSPATIAL/GIS |
| Popis: | Several emerging applications such as traffic management and urban emergency response systems often need to identify groups from recent window of the moving object data. These requirements mandate algorithmic solutions that are time and memory efficient for adding new data incrementally and removing stale data. In this paper, we consider the problem of finding closed Swarms over a sliding window. The key challenges in computing closed Swarms over a sliding window are: (i) Search space for computing closed Swarms from new data is large (ii) Removal of old data leaves many non-closed Swarms which need to be identified and deleted. None of the existing methods are efficient in adding new data and removing old data for large datasets. This paper presents an efficient incremental graph based method for computing Swarms over sliding windows. We use a real dataset to show the performance of our method. The complexity analysis as well as experimental results demonstrate that our method is significantly faster than the existing incremental method over sliding windows with increased memory requirement. In particular, our method is shown to be 7-13 times faster with 3-5 times memory overhead in all the experiments. |
| Databáze: | OpenAIRE |
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