Developing a model that facilitates the representation and knowledge discovery on sensor data presents many challenges. With sensors reporting data at a very high frequency, resulting in large volumes of data, there is a need for a model that is memory efficient. Sensor networks have spatial characterstics which include the location of the sensors. In addition, sensor data incorporates temporal nature, and hence the model must also support the time dependence of the data. Balancing the conflicting requirements of simplicity, expressiveness, and storage efficiency is challenging. The model should also provide adequate support for the formulation of efficient algorithms for knowledge discovery. Though spatio-temporal data can be modeled using time expanded graphs, this model replicates the entire graph across time instants, resulting in high storage overhead and computationally expensive algorithms. In this chapter, we discuss a data model called Spatio-Temporal Sensor Graphs (STSG) to model sensor data, which allows the properties of edges and nodes to be modeled as a time series of measurement data. Data at each instant would consist of the measured value and the expected error. Also, we present several case studies illustrating how the proposed STSG model facilitates methods to find interesting patterns (e.g., growing hotspots) in sensor data.
|Original language||English (US)|
|Title of host publication||Knowledge Discovery from Sensor Data|
|Number of pages||20|
|State||Published - Jan 1 2008|