Note: [1] This work was supported by the NSF-SEI grant, NSF-IGERT grant, Oak Ridge National Laboratory grant and US Army Corps of Engineers (Topographic Engineering Center) grant. The content does not necessarily reflect the position or policy of the government and no official endorsement should be inferred.
Abstract: 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. Since sensor data is spatio-temporal in nature, 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 paper, we propose Spatio-Temporal Sensor Graphs (STSG) to model sensor data at the conceptual. logical and physical levels. This model 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 evaluate the model using methods to find interesting patterns such as growing hotspots in sensor data and present analytical comparison of the algorithms with methods based on existing models.
