Paper
10 September 2007 Dependence topology optimization in dynamic peer-to-peer database network
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Abstract
The scheme of dynamic coordination rules in peer-to-peer database uses rule caching and forwarding to successfully solve the dependence tree break problem in the situation that peers can join and leave freely. But there are still problems that weaken the performance of query processing in this scheme. Coordination rules in cache are merged in run time when bypassing break points. If dependence trees can be optimized into a form robust against peer absence beforehand, the query process will be more efficient. This paper proposes such mechanism by doing coordination rule combinations when new peer joins the dependence tree and new forwarded coordination rule arrives in cache. When some peers leave, queries take one existing bypass rule for reformulation, instead of concatenating cached ones from scratch. In effect, this mechanism optimizes dependence tree into a more robust topology whenever new peer joins. Even when there is no peer absence, bypass rules can make query processing more efficient without following through many mediating peers, especially when data are updated frequently and frequent queries are needed. At the same time, the original dependence tree are maintained for data cache query when the target peer is absent. Since dynamic coordination rules are expressed in XSLT, we try to find a way to form one XSLT whose function is equal to a chain of XSLTs, similar to the XML reasoning. The protocol also needs to be improved to inform to launch topology optimization when new peer join or rule changes.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhichao Zhao, Zheng Zhao, and Qingwei Shi "Dependence topology optimization in dynamic peer-to-peer database network", Proc. SPIE 6773, Next-Generation Communication and Sensor Networks 2007, 67730L (10 September 2007); https://doi.org/10.1117/12.733888
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Cited by 1 scholarly publication.
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KEYWORDS
Databases

Data modeling

Systems modeling

Associative arrays

Data integration

Data conversion

Artificial intelligence

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