On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Rate-based query optimization for streaming information sources
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
Temporal and spatio-temporal aggregations over data streams using multiple time granularities
Information Systems - Special issue: Best papers from EDBT 2002
The Tangram Stream Query Processing System
Proceedings of the Fifth International Conference on Data Engineering
A Probabilistic Room Location Service for Wireless Networked Environments
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
STREAM: the stanford stream data manager (demonstration description)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Taxonomy of XML schema languages using formal language theory
ACM Transactions on Internet Technology (TOIT)
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Energy efficient strategies for object tracking in sensor networks: A data mining approach
Journal of Systems and Software
Journal of Systems and Software
Expert Systems with Applications: An International Journal
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Our system architecture to manage sensor data is described. Our data mining applications require past history of the sensor data. Therefore, unlike most present systems that focus on streaming data, and cache a small window of historic data, we store the entire historic data. Several interesting problems arise in these scenarios. We study two of them: (a) Given that a sensor can send data corresponding to its current configuration at any particular instant, how do we define the data that should be stored in the database? (b) Sensors try to minimize the amount of data transmitted. Also there could be data loss in the network. So the data stored will have lots of "holes". In this case, how can an application make sense of the stored data? In this paper, we describe our approach to solve these problems that enables an application to recreate the environment that generated the data as precisely as possible.