Abstract
Mining data streams for mobile and embedded applications faces a major problem represented in the high rate of the streaming input with regard to the available computational resources. Adapting the data mining algorithms to the availability of resources is an essential step towards realizing the potential applications in this area. In this paper, we review our Algorithm Output Granularity (AOG) for data stream mining adaptation. The generalization of AOG based on Probably Approximately Correct (PAC) learning model is presented. This generalization is of paramount importance to establish a theoretical framework for adaptation and resource-awareness in data stream mining.
| Original language | English |
|---|---|
| Title of host publication | Biomedical Engineering Conference, 2008. CIBEC 2008. Cairo International |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1 -6 |
| Number of pages | 6 |
| ISBN (Print) | 9781424426942 |
| Publication status | Published - Dec 2008 |
| Event | Biomedical Engineering Conference - Cairo, Egypt Duration: 18 Dec 2008 → 20 Dec 2008 |
Conference
| Conference | Biomedical Engineering Conference |
|---|---|
| Abbreviated title | CIBEC 2008 |
| Country/Territory | Egypt |
| City | Cairo |
| Period | 18/12/08 → 20/12/08 |
Keywords
- adaptation
- adaptive data stream mining
- algorithm output granularity
- embedded application
- mobile application
- probably approximately correct learning model
- resource awareness
- adaptive systems
- data mining
- embedded systems
- medical information systems
- mobile computing