International audience

Topic evolution networks are widely used to represent the evolution of research topics in scientific document archives. These networks might contain thousands of topics and alignment edges which are computed by comparing millions of topic pairs with some similarity function. In this work, we are addressing the problem of computing a very large number cosine-based topic alignments on top of Apache Spark. We present the native map-reduce implementation proposed by Spark and a more efficient implementation which is tuned for alignment computation. Both implementations are evaluated on three real-world datasets.

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of inferences is one them. For instance, to complete the answer set of SPARQL queries, RDF database systems evaluate semantic RDFS relationships (subPropertyOf, subClassOf) through time-consuming query rewriting algorithms or space-consuming data materialization solutions. To reduce the memory footprint and ease the exchange of large datasets, these systems generally apply a dictionary approach for compressing triple data sizes by replacing resource identifiers (IRIs), blank nodes and literals with integer values. In this article, we present a structured resource identification scheme using a clever encoding of concepts and property hierarchies for efficiently evaluating the main common RDFS entailment rules while minimizing triple materialization and query rewriting. We will show how this encoding can be computed by a scalable parallel algorithm and directly be implemented over the Apache Spark framework. The efficiency of our encoding scheme is emphasized by an evaluation conducted over both synthetic and real world datasets.

This demonstration presents MeowsReader, a real-time news ranking and filtering prototype. MeowsReader illustrates how a general class of continuous top-k queries offers a suitable abstraction for modeling and implementing real-time search services over highly dynamic information streams combining keyword search and realtime web signals about information items. Users express their interest by simple text queries and continuously receive the best matching results in an alert-like environment. The main innovative feature are dynamic item scores which take account of information decay, real-time web attention and other online user feedback. Additionally, a trends detection mechanism automatically generates trending entities from the input streams, which can smoothly be added to user profiles in form of keyword queries.

International audience

In this work we are interested in the scalable processing of content filtering queries over text item streams. In particular, we are aiming to generalize state of the art solutions with non-homogeneous scoring functions combining query-independent item importance with query-dependent content relevance. While such complex ranking functions are widely used in web search engines this is to our knowledge the first scientific work studying their usage in a continuous query scenario. Our main contribution consists in the definition and the evaluation of new efficient in-memory data structures for indexing continuous top-k queries based on an original two-dimensional representation of text queries. We are exploring locally-optimal score bounds and heuristics that efficiently prune the search space of candidate top-k query results which have to be updated at the arrival of new stream items. Finally, we experimentally evaluate memory/matching time trade-offs of these index structures. In particular we experimentally illustrate their linear scaling behavior with respect to the number of indexed queries.

We present RoSeS, a running system for large-scale content-based RSS feed filtering and aggregation. The implementation of RoSeS is based on standard database concepts like declarative query languages, views and multi-query optimization. Users create personalized feeds by defining and composing content-based filtering and aggregation queries on collections of RSS feeds. These queries are translated into continuous multi-query execution plans which are optimized using a new cost-based multi-query optimization strategy.

XML is becoming the universal format for data exchange between applications. Recently, the emergence of Web services as standard means of publishing and accessing data on the Web introduced a new class of XML documents, which we call intensional documents. These are XML documents where some of the data is given explicitly while other parts are defined only intensionally by means of embedded calls to Web services.When such documents are exchanged between applications, one has the choice of whether or not to materialize the intensional data (i.e., to invoke the embedded calls) before the document is sent. This choice may be influenced by various parameters, such as performance and security considerations. This article addresses the problem of guiding this materialization process.We argue that---like for regular XML data---schemas (à la DTD and XML Schema) can be used to control the exchange of intensional data and, in particular, to determine which data should be materialized before sending a document, and which should not. We formalize the problem and provide algorithms to solve it. We also present an implementation that complies with real-life standards for XML data, schemas, and Web services, and is used in the Active XML system. We illustrate the usefulness of this approach through a real-life application for peer-to-peer news exchange.