Complex pattern detection and specification from multiscale environmental variables for biodiversity applications

• Jacqueline do Espírito Santo UNICAMP
• Claudia Medeiros UNICAMP

Resumo

Biodiversity scientists often need to define and detect scenarios of interest from data streams concern meteorological sensors. Such streams are characterized by their heterogeneity across spatial and temporal scales, which hampers construction of scenarios. To help them in this task, this paper proposes the use of the theory of Complex Event Processing (CEP) to detect complex event patterns in this context.

Referências

Agrawal, J., Diao, Y., Gyllstrom, D., and Immerman, N. (2008). Efficient pattern matching over event streams. In ACM SIGMOD, pages 147–160.

Barga, R. S. and Caituiro-Monge, H. (2006). Event correlation and pattern detection in cedr. In EBDT, pages 919–930.

Etzion, O. and Niblett, P. (2010). Event Processing in Action. Manning Publications Co.

Hardisty, A. and Roberts, D. (2013). A decadal view of biodiversity informatics: challenges and priorities. BMC Ecology, 13(1).

Koga, I. K. (2013). An Event-Based Approach to Process Environmental Data. PhD thesis, Instituto de Computação - Unicamp. Supervisor Claudia Bauzer Medeiros.

Motakis, I. and Zaniolo, C. (1995). Composite temporal events in active database rules: A logic-oriented approach. In DOOD, volume 1013 of LNCS, pages 19–37.

Obweger, H., Schiefer, J., Kepplinger, P., and Suntinger, M. (2010). Discovering hierarchical patterns in event-based systems. In SCC, pages 329–336.

Pietzuch, P., Shand, B., and Bacon, J. (2004). Composite event detection as a generic middleware extension. IEEE Network, 18(1):44–55.

Sen, S., Stojanovic, N., and Stojanovic, L. (2010). An approach for iterative event pattern recommendation. In DEBS, pages 196–205.