Distributed Query Engine for Multiple-Query Optimization over Data Stream
| Autor: | Yong Zhang, Chunxiao Xing, Jin Wang, Junye Yang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Data stream
050101 languages & linguistics Distributed Computing Environment Data stream mining Computer science Distributed computing 05 social sciences 02 engineering and technology Reuse Query optimization Query plan Resource (project management) 0202 electrical engineering electronic engineering information engineering Overhead (computing) 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences |
| Zdroj: | Database Systems for Advanced Applications ISBN: 9783030185893 DASFAA (3) |
| DOI: | 10.1007/978-3-030-18590-9_79 |
| Popis: | Query processing over data stream has attracted much attention in real-time applications. While many efforts have been paid for query processing of data streams in distributed environment, no previous study focused on multiple-query optimization. To address this problem, we propose EsperDist, a distributed query engine for multiple-query optimization over data stream. EsperDist can significant reduce the overhead of network transmission and memory usage by reusing operators in the query plan. Moreover, EsperDist also makes best effort to minimize the query cost so as to avoid resource bottle neck in a single machine. In this demo, we will present the architecture and work-flow of EsperDist using datasets collected from real world applications. We also propose a user-friendly to monitor query results and interact with the system in real time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|