In this demonstration, users will experience three engaging scenarios: In the first scenario, they can set up, configure, and deploy XGossip on Amazon Elastic Compute Cloud (EC2). It has a strong theoretical underpinning and provides provable guarantees on the accuracy of cardinality estimates, the number of messages exchanged, and the total bandwidth usage. It employs a novel divide-and-conquer strategy for load balancing and reducing the overall network bandwidth consumption.
XGossip relies on the principle of gossip: It is scalable, decentralized, and can cope with network churn and failures.
We present a novel tool called XGossip for Internetscale cardinality estimation of XPath queries over distributed XML data. We present a comprehensive performance evaluation of XGossip on Amazon EC2 using a heterogeneous collection of XML documents. We conduct theoretical analysis of XGossip in terms of accuracy of cardinality estimation, message complexity, and bandwidth consumption. XGossip employs a novel divide-and-conquer strategy for load balancing and reducing the bandwidth consumption. XGossip is designed to be scalable, decentralized, and robust to failures-properties that are desirable in a large-scale distributed system. We present a novel gossip algorithm called XGossip, which given an XPath query estimates the number of XML documents in the network that contain a match for the query. The cardinality estimate of XPath expressions is useful in XQuery optimization, designing IR-style relevance ranking schemes, and statistical hypothesis testing. In this paper, we address the problem of cardinality estimation of XPath queries over XML data stored in a distributed, Internet-scale environment such as a large-scale, data sharing system designed to foster innovations in biomedical and health informatics.
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