Tree pattern aggregation for scalable XML data disseminationTree pattern aggregation for scalable XML data dissemination Δημοσίευση σε Συνέδριο Conference Publication 2015-12-012002enWith the rapid growth of XML-document traffic on the Internet, scalable content-based dissemination of XML documents to a large, dynamic group of consumers has become an important research challenge. To indicate the type of content that they are interested in, data consumers typically specify their subscriptions using some XML pattern specification language (e.g., XPath). Given the large volume of subscribers, system scalability and efficiency mandate the ability to aggregate the set of consumer subscriptions to a smaller set of content specifications, so as to both reduce their storagespace requirements as well as speed up the documentsubscription matching process. In this paper, we provide the first systematic study of subscription aggregation where subscriptions are specified with tree patterns (an important subclass of XPath expressions). The main challenge is to aggregate an input set of tree patterns into a smaller set of generalized tree patterns such that: (1) a given space constraint on the total size of the subscriptions is met, and (2) the loss in precision (due to aggregation) during document filtering is minimized. We propose an efficient tree-pattern aggregation algorithm that makes effective use of document-distribution statistics in order to compute a precise set of aggregate tree patterns within the allotted space budget. As part of our solution, we also develop several novel algorithms for tree-pattern containment and minimization, as well as “least-upper-bound” computation for a set of tree patterns. These results are of interest in their own right, and can prove useful in other domains, such as XML query optimization. Extensive results from a prototype implementation validate our approach.http://creativecommons.org/licenses/by/4.0/826-83728th International Conference on Very Large Data BasesProceedings of the 28th international conference on Very Large Data Bases Chan Chee-Yong Wenfei Fan Felber Pascal Garofalakis Minos Γαροφαλακης Μινως Rastogi Rajeev Databases