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XML keyword search is a popular topic in research field, and the Smallest Lowest Common Ancestor (SLCA) concept is fundamental for XML keyword search algorithms. With the rapid growth of XML data in internet, we are confronted with big data issues, it's becoming a new research direction for managing massive XML data
This paper proposed a structured p2p model called balance-peer, introduce a method to partition the data rang without any global information on the load to. Our Balance-peer model is used to realize a partition-by-keyword indexing service based on DHT with each node in the system responsible for part of the keywords
reduced. To reduce the communication overhead the server should send only the top-n files based on the keyword when the user asks for the data files. Since the owner need not maintain the copy of the files, it is all the more necessary to make check on the files available and also check the originality of the files stored in
Previously, we proposed efficient, scalable decentralized processing of SPARQL queries for an ad-hoc Semantic Web data sharing system and explored optimization techniques. However, it has proven to be difficult to measure the performance of the proposed query processing in a decentralized settings with existing tools. This is because assessments on SPARQL query performance were typically targeted...
. Finally, the experiments prove that this method can query the keyword of multi-dimensional space more efficiently with the massive data and has a good load balancing performance. And this method can be more effective to avoid the issue of the server cluster hotspot.
At present, there are vast amounts of information and applications available in distributed environment with heterogeneous databases, how to find them is a crucial task. An ontology-driven approach is put forward to overcome semantic heterogeneity caused by synonyms and homonyms during keyword-based search in
queries for given attributes or keywords, we leverage searchable symmetric encryption to design the encrypted local indexes that consider security, efficiency, and data locality simultaneously. Performance evaluation at Microsoft Azure is conducted in terms of Put/Get throughput and latency under different workloads. The
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