621 research outputs found
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
Constitute: The worldâs constitutions to read, search, and compare
Constitutional design and redesign is constant. Over the last 200 years, countries have replaced their constitutions an average of every 19 years and some have amended them almost yearly. A basic problem in the drafting of these documents is the search and analysis of model text deployed in other jurisdictions. Traditionally, this process has been ad hoc and the results suboptimal. As a result, drafters generally lack systematic information about the institutional options and choices available to them. In order to address this informational need, the investigators developed a web application, Constitute [online at http://www.constituteproject.org], with the use of semantic technologies. Constitute provides searchable access to the worldâs constitutions using the conceptualization, texts, and data developed by the Comparative Constitutions Project. An OWL ontology represents 330 ââtopicsââ â e.g. right to health â with which the investigators have tagged relevant provisions of nearly all constitutions in force as of September of 2013. The tagged texts were then converted to an RDF representation using R2RML mappings and Capsentaâs Ultrawrap. The portal implements semantic search features to allow constitutional drafters to read, search, and compare the worldâs constitutions. The goal of the project is to improve the efficiency and systemization of constitutional design and, thus, to support the independence and self-reliance of constitutional drafters.Governmen
EAGLEâA Scalable Query Processing Engine for Linked Sensor Data
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatioâtemporal correlations. Most semantic approaches do not have spatioâtemporal support. Some of them have attempted to provide full spatioâtemporal support, but have poor performance for complex spatioâtemporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatioâtemporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatioâtemporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE
BESDUI: A Benchmark for End-User Structured Data User Interfaces
The Semantic Web Community has invested significant research efforts in developing systems for Semantic Web search and exploration. But while it has been easy to assess the systemsâ computational efficiency, it has been much harder to assess how well different semantic systemsâ user interfaces help their users. In this article, we propose and demonstrate the use of a benchmark for evaluating such user interfaces, similar to the TREC benchmark for evaluating traditional search engines. Our benchmark includes a set of typical user tasks and a well-defined procedure for assigning a measure of performance on those tasks to a semantic system. We demonstrate its application to two such system, Virtuoso and Rhizomer. We intend for this work to initiate a community conversation that will lead to a generally accepted framework for comparing systems and for measuring, and thus encouraging, progress towards better semantic search and exploration tools
ClioPatria: A SWI-Prolog Infrastructure for the Semantic Web
ClioPatria is a comprehensive semantic web development framework based on SWI-Prolog. SWI-Prolog provides an efficient C-based main-memory RDF store that is designed to cooperate naturally and efficiently with Prolog, realizing a flexible RDF-based environment for rule based programming. ClioPatria extends this core with a SPARQL and LOD server, an extensible web frontend to manage the server, browse the data, query the data using SPARQL and Prolog and a Git-based plugin manager. The ability to query RDF using Prolog provides query composition and smooth integration with application logic. ClioPatria is primarily positioned as a prototyping platform for exploring novel ways of reasoning with RDF data. It has been used in several research projects in order to perform tasks such as data integration and enrichment and semantic search
Social Network Analysis on Educational Data Set in RDF Format
The increased usage of information technologies in educational tasks resulted in high volume of data, exploited to build analytical systems that can provide practical insight in the learning process. In this paper, we propose a method of running social network analysis on multiple data sources (academic years, communication tools). To achieve this, the collected data that describe social interactions were converted into a common format by employing a prior developed semantic web educational ontology. Using a mapping language the relational data set was linked to the appropriate concepts defined in the ontology and then it was exported in RDF format. The means for SPARQL access was also provided. Subsequently, query patterns were defined for different social interactions in the educational platform. To prove the feasibility of this approach, Gephi tool set was used to run SNA (Social Network Analysis) on data obtained with the SPARQL queries. The added value of this research lies in the potential of this method to simplify running social network analysis on multiple data sets, on a specific course or the entire academic year, by simply modifying the query pattern
Visualisation of Linked Data â Reprise
Linked Data promises to serve as a disruptor of traditional approaches to data management and use, promoting the push from the traditional Web of documents to a Web of data. The ability for data consumers to adopt a follow your nose approach, traversing links defined within a dataset or across independently-curated datasets, is an essential feature of this new Web of Data, enabling richer knowledge retrieval thanks to synthesis across multiple sources of, and views on, inter-related datasets. But for the Web of Data to be successful, we must design novel ways of interacting with the corresponding very large amounts of complex, interlinked, multi-dimensional data throughout its management cycle. The design of user interfaces for Linked Data, and more specifically interfaces that represent the data visually, play a central role in this respect. Contributions to this special issue on Linked Data visualisation investigate different approaches to harnessing visualisation as a tool for exploratory discovery and basic-to-advanced analysis. The papers in this volume illustrate the design and construction of intuitive means for end-users to obtain new insight and gather more knowledge, as they follow links defined across datasets over the Web of Data
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
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