Applied multivariate statistical analysis
Applied multivariate statistical analysis
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Synopsis data structures for massive data sets
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Middleware challenges for wireless sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review
Time-Expanded Graphs for Flow-Dependent Transit Times
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Sensor Networks for Emergency Response: Challenges and Opportunities
IEEE Pervasive Computing
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Linear road: a stream data management benchmark
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Spatio-temporal network databases and routing algorithms: a summary of results
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Time-Aggregated graphs for modeling spatio-temporal networks
CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
A survey on position-based routing in mobile ad hoc networks
IEEE Network: The Magazine of Global Internetworking
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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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.