ODE-SWS: A Semantic Web Service Development Environment

Web Services (WS) are software modules that perform operations that are network-accessible through XML messaging. Web Services in the Semantic Web, that is, Semantic Web Services (SWS), should describe semantically their structure and capabilities to enable its automatic discovery, invocation and composition. In this work we present a development environment to design SWS in a language-independent manner. This environment is based on a framework that defines an ontology set to characterize how a SWS should be specified. The core ontology of this framework describes the SWS problem-solving behaviour and enables the SWS design at a conceptual level. Considering this framework, the SWS development environment is composed of (1) a graphical interface, in which the conceptual design of SWSs is performed, and (2) a tool set, which instantiates the framework ontologies according to the graphical model created by the user, verifies the completeness and consistency of the SWS through instance evaluation, and translates the SWS conceptual model description into SWS (and WS) languages, such as DAML-S, WSDL or UDDI. This tool set is integrated in the WebODE ontology engineering workbench in order to take advantage of its reasoning and ontology translation capabilities

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

GraphX: Unifying Data-Parallel and Graph-Parallel Analytics

From social networks to language modeling, the growing scale and importance of graph data has driven the development of numerous new graph-parallel systems (e.g., Pregel, GraphLab). By restricting the computation that can be expressed and introducing new techniques to partition and distribute the graph, these systems can efficiently execute iterative graph algorithms orders of magnitude faster than more general data-parallel systems. However, the same restrictions that enable the performance gains also make it difficult to express many of the important stages in a typical graph-analytics pipeline: constructing the graph, modifying its structure, or expressing computation that spans multiple graphs. As a consequence, existing graph analytics pipelines compose graph-parallel and data-parallel systems using external storage systems, leading to extensive data movement and complicated programming model. To address these challenges we introduce GraphX, a distributed graph computation framework that unifies graph-parallel and data-parallel computation. GraphX provides a small, core set of graph-parallel operators expressive enough to implement the Pregel and PowerGraph abstractions, yet simple enough to be cast in relational algebra. GraphX uses a collection of query optimization techniques such as automatic join rewrites to efficiently implement these graph-parallel operators. We evaluate GraphX on real-world graphs and workloads and demonstrate that GraphX achieves comparable performance as specialized graph computation systems, while outperforming them in end-to-end graph pipelines. Moreover, GraphX achieves a balance between expressiveness, performance, and ease of use