Machine Learning
Database Management Systems
A Framework for Generating Network-Based Moving Objects
Geoinformatica
CAPE: continuous query engine with heterogeneous-grained adaptivity
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Foundations and Trends in Databases
Ubiquitous City Technology & Applications
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Self-tuning query mesh for adaptive multi-route query processing
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Security Punctuation Framework for Enforcing Access Control on Streaming Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Parallel processing of continuous queries over data streams
Distributed and Parallel Databases
Sharing-aware horizontal partitioning for exploiting correlations during query processing
Proceedings of the VLDB Endowment
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We propose to demonstrate a practical alternative approach to the current state-of-the-art query processing techniques, called the "Query Mesh" (or QM, for short). The main idea of QM is to compute multiple routes (i.e., query plans), each designed for a particular subset of data with distinct statistical properties. Based on the execution routes and the data characteristics, a classifier model is induced and is used to partition new data tuples to assign the best routes for their processing. We propose to demonstrate the QM framework in the streaming context using our demo application, called the "Ubi-City". We will illustrate the innovative features of QM, including: the QM optimization with the integrated machine learning component, the QM execution using the efficient "Self-Routing Fabric" infrastructure, and finally, the QM adaptive component that performs the online adaptation of QM with near-zero runtime overhead.