SemLAV: Querying Deep Web and Linked Open Data with SPARQL

International audienceSemLAV allows to execute SPARQL queries against the Deep Web and Linked Open Data data sources. It implements the mediator-wrapper architecture based on view definitions over remote data sources. SPARQL queries are expressed using a mediator schema vocabulary, and SemLAV selects relevant data sources and rank them. The ranking strat-egy is designed to deliver results quickly based only on view definitions, i.e., no statistics, nor probing on sources are required. In this demonstra-tion, we validate the effectiveness of SemLAV approach with real data sources from social networks and Linked Open Data. We show in differ-ent setups that materializing only a subset of ranked relevant views is enough to deliver significant part of expected results

SemLAV: Local-As-View Mediation for SPARQL Queries

International audienceThe Local-As-View(LAV) integration approach aims at querying heterogeneous data in dynamic environments. In LAV, data sources are described as views over a global schema which is used to pose queries. Query processing requires to generate and execute query rewritings, but for SPARQL queries, the LAV query rewritings may not be generated or executed in a reasonable time. In this paper, we present SemLAV, an alternative technique to process SPARQL queries over a LAV integration system without generating rewritings. SemLAV executes the query against a partial instance of the global schema which is built on-the-fly with data from the relevant views. The paper presents an experimental study for SemLAV, and compares its performance with traditional LAV-based query processing techniques. The results suggest that SemLAV scales up to SPARQL queries even over a large number of views, while it significantly outperforms traditional solutions

Cyber threat intelligence sharing: Survey and research directions

Cyber Threat Intelligence (CTI) sharing has become a novel weapon in the arsenal of cyber defenders to proactively mitigate increasing cyber attacks. Automating the process of CTI sharing, and even the basic consumption, has raised new challenges for researchers and practitioners. This extensive literature survey explores the current state-of-the-art and approaches different problem areas of interest pertaining to the larger field of sharing cyber threat intelligence. The motivation for this research stems from the recent emergence of sharing cyber threat intelligence and the involved challenges of automating its processes. This work comprises a considerable amount of articles from academic and gray literature, and focuses on technical and non-technical challenges. Moreover, the findings reveal which topics were widely discussed, and hence considered relevant by the authors and cyber threat intelligence sharing communities

Proceedings of the First Karlsruhe Service Summit Workshop - Advances in Service Research, Karlsruhe, Germany, February 2015 (KIT Scientific Reports ; 7692)

Since April 2008 KSRI fosters interdisciplinary research in order to support and advance the progress in the service domain. KSRI brings together academia and industry while serving as a European research hub with respect to service science. For KSS2015 Research Workshop, we invited submissions of theoretical and empirical research dealing with the relevant topics in the context of services including energy, mobility, health care, social collaboration, and web technologies