Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
ProPolyne: A Fast Wavelet-Based Algorithm for Progressive Evaluation of Polynomial Range-Sum Queries
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Approximate Query Processing Using Wavelets
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Dynamic Maintenance of Wavelet-Based Histograms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
STREAM: the stanford stream data manager (demonstration description)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Spatio-temporal data reduction with deterministic error bounds
The VLDB Journal — The International Journal on Very Large Data Bases
Trajectories for novel and detailed traffic information
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
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With resource-efficient summarization and accurate reconstruction of the historic traffic sensor data, one can effectively manage and optimize transportation systems (e.g., road networks) to become smarter (better mobility, less congestion, less travel time, and less travel cost) and greener (less waste of fuel and less greenhouse gas production). The existing data summarization (and archival) techniques are generic and are not designed to leverage the unique characteristics of the traffic data for effective data reduction. In this paper, we propose and explore a family of data summaries that take advantage of the high temporal and spatial redundancy/correlation among sensor readings from individual sensors and sensor groups, respectively, for effective data reduction. In particular, with these summaries we derive and maintain a "signature" as well as a series of "outliers" for the readings received from each individual sensor or group of co-located sensors. While signatures capture the typical readings that estimate the actual readings with bounded error, the outliers represent the actual readings where the error-bound is violated. With the combination of signatures and outliers, our proposed data summaries can effectively represent the actual data with much smaller storage footprint, while allowing for efficient querying of the sensor data with bounded error. Our experiments with a real traffic sensor dataset shows that our proposed data summaries use only 23% of the storage space otherwise required for storing the actual data, while allowing for highly accurate query results with guaranteed precision.