In content-based publish-subscribe (pub/sub) systems, users express their interests as queries over a stream of publications. Scaling up content-based pub/sub to very large numbers of subscriptions is challenging: users are interested in low latency, that is, getting subscription results fast, while the pub/sub system provider is mostly interested in scaling, i.e., being able to serve large numbers of subscribers, with low computational resources utilization. We present a novel approach for scalable content-based pub/sub in the presence of constraints on the available CPU and network resources, implemented within our pub/sub system Delta. We achieve scalability by off-loading some subscriptions from the pub/sub server, and leveraging view-based query rewriting to feed these subscriptions from the data accumulated in others1. Our main contribution is a novel algorithm for organizing views in a multi-level dissemination network, exploiting view-based rewriting and powerful linear programming capabilities to scale to many views, respect capacity constraints, and minimize latency. The efficiency and effectiveness of our algorithm are confirmed through extensive experiments and a large deployment in a WAN.@en

We consider the problem of efficiently sharing large volumes of XML data based on distributed hash table overlay networks. Over the last three years, we have built ViP2P (standing for Views in Peer-to-Peer), a platform for the distributed, parallel dissemination of XML data among peers. At the core of ViP2P stand distributed materialized XML views, defined as XML queries, filled in with data published anywhere in the network, and exploited to efficiently answer queries issued by any network peer. ViP2P is one of the very few fully implemented P2P platforms for XML sharing, deployed on hundreds of peers in a WAN. This paper describes the system architecture and modules, and the engineering lessons learned. We show experimental results, showing that our choices, outperf related systems by orders of magnitude in terms of data volumes, network size and data dissemination throughput.@en

The efficient processing of XQuery still poses significant challenges. A particularly effective technique to improve XQuery processing performance consists of using materialized views to answer queries. In this work, we consider the problem of choosing the best views to materialize within a given space budget in order to improve the performance of a query workload. The paper is the first to address the view selection problem for queries and views with value joins and multiple return nodes. The challenges we face stem from the expressive power and features of both the query and view languages and from the size of the search space of candidate views to materialize. While the general problem has prohibitive complexity, we propose and study a heuristic algorithm and demonstrate its superior performance compared to the state of the art.

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We propose to demonstrate LiquidXML, a platform for managing large corpora of XML documents in large-scale P2P networks. All LiquidXML peers may publish XML documents to be shared with all the network peers. The challenge then is to efficiently (re-)distribute the published content in the network, possibly in overlapping, redundant fragments, to support efficient processing of queries at each peer. The novelty of LiquidXML relies in its adaptive method of choosing which data fragments are stored where, to improve performance. The "liquid" aspect of XML management is twofold: XML data flows from many sources towards many consumers, and its distribution in the network continuously adapts to improve query performance.@en

Liquid queries are a flexible tool for information seeking, based on the progressive exploration of the search space; they produce "fluid" results which dynamically adapt to the shape of the query, as a liquid adapts to its container. The liquid query paradigm relies on the SeCo service mart and multi-domain query execution concepts: an expert user selects a priori the service marts relevant to the information seeking task at hand and the connections necessary to join them, and publishes such a definition in the SeCo back-end. The Liquid Query client-side interface consumes the application definition created by the expert and dynamically builds a query interface for the end-user. Such interface allows one to supply keywords to query the pre-configured service marts and offers controls for exploring the combinations computed by the SeCo execution engine. The interaction commands are based on a tabular representation of results and comprise: reordering, clustering, addition or deletion of attributes, addition of extra service marts to the query for specific items in the result set or for the entire result set, request of more results from all services or from selected ones, expansion of details on selected items, and more. The Liquid Query is equipped with multiple data visualization options suited to render multi-domain results and can be instrumented with indicators showing the quality of the result set.

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