Introduction to the Knowledge Discovery from Sensor Data

Recent advances in distributed sensor network technology and increasing deployment of such networks to monitor wide variety of dynamic phenomena has opened new opportunities to the knowledge discovery community. Extracting knowledge and emerging patterns from sensor data is a nontrivial task. The challenges for the knowledge discovery community are expected to be immense. On one hand, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive insights, which in turn can facilitate real-time analysis. In addition, emerging societal problems require knowledge discovery solutions that are designed to investigate anomalies, changes, extremes and nonlinear processes, and departures from the normal. Keeping in view the requirements of the emerging field of knowledge discovery from sensor data, we took initiative to develop a community of researchers with common interests and scientific goals, which culminated into the organization of Sensor-KDD series of workshops in conjunction with the prestigious ACM SIGKDD International Conference of Knowledge Discovery and Data Mining. In this chapter we summarize the events of the Second ACM-SIGKDD International Workshop on Knowledge Discovery form Sensor Data (Sensor-KDD 2008).