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Self-Tuning Query Mesh for Adaptive Multi-Route Query Processing (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bertino, Elisa Nehme, Rimma V. Rundensteiner, Elke A. |
| Abstract | In real-life applications, different subsets of data may have distinct statistical properties, e.g., various websites may have diverse visitation rates, different categories of stocks may have dissimilar price fluctuation patterns. For such applications, it can be fruitful to eliminate the commonly made single execution plan assumption and instead execute a query using several plans, each optimally serving a subset of data with particular statistical properties. Furthermore, in dynamic environments, data properties may change continuously, thus calling for adaptivity. The intriguing question is: can we have an execution strategy that (1) is plan-based to leverage on all the benefits of traditional plan-based systems, (2) supports multiple plans each customized for different subset of data, and yet (3) is as adaptive as “plan-less ” systems like Eddies? While the recently proposed Query Mesh (QM) approach provides a foundation for such an execution paradigm, it does not address the question of adaptivity required for highly dynamic environments. In this work, we fill this gap by proposing a Self-Tuning Query Mesh (ST-QM) – an adaptive solution for content-based multi-plan execution engines. ST-QM addresses adaptive query processing by abstracting it as a concept drift problem – a wellknown subject in machine learning. Such abstraction allows to discard adaptivity candidates (i.e., the cases indicating a change in the environment) early in the process if they are insignificant or not “worthwhile ” to adapt to, and thus minimize the adaptivity overhead. A unique feature of our approach is that all logical transformations to the execution strategy get translated into a single inexpensive physical operation – the classifier change. Our experimental evaluation using a continuous query engine shows the performance benefits of ST-QM approach over the alternatives, namely the non-adaptive and the Eddies-based solutions. |
| File Format | |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Data Property Single Execution Plan Assumption St-qm Approach Query Mesh Dissimilar Price Fluctuation Pattern Continuous Query Engine Distinct Statistical Property Machine Learning Content-based Multi-plan Execution Engine Adaptivity Overhead Real-life Application Different Category Different Subset Execution Strategy Single Inexpensive Physical Operation Intriguing Question Adaptive Solution Traditional Plan-based System Various Website Concept Drift Problem Adaptive Multi-route Query Processing Self-tuning Query Mesh Plan-less System Particular Statistical Property St-qm Address Adaptive Query Processing Multiple Plan Eddies-based Solution Experimental Evaluation Several Plan Logical Transformation Diverse Visitation Rate Execution Paradigm Adaptivity Candidate Performance Benefit Dynamic Environment Unique Feature Wellknown Subject |
| Content Type | Text |
