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Healthcare datasets are increasingly characterized by large volume, high rate of generation and need for real time analysis (velocity), and variety. These datasets are often termed biomedical big data and include multi-modal electrophysiological signals and electronic health records. In this talk, we focus on the computational challenges associated with signal data management and the role of semantic...
The emerging Internet of Things technologies enable enterprises to collect a variety of real-time data from the physical world, making a case for accessing, combining, interpreting, and distributing such data in real-time too. Enterprise Information Integration (EII) aims at providing tools for integrating data from multiple sources without having to first load all the data into a central warehouse,...
Crowd sourcing is an emerging paradigm to exploit the notion of human-computation for solving various computational problems, which cannot be accurately solved solely by the machine-based solutions. We use crowd sourcing for large-scale link management in the Semantic Web. More specifically, we develop Crowd Link, which utilizes crowd workers for verification and creation of triples in Linking Open...
Semantic models of data sources describe the meaning of the data in terms of the concepts and relationships defined by a domain ontology. Building such models is an important step toward integrating data from different sources, where we need to provide the user with a unified view of underlying sources. In this paper, we present a scalable approach to automatically learn semantic models of a structured...
The amount of data within the Linking Open Data (LOD) cloud is steadily increasing and resembles a rich source of information. Since Context-aware Services (CAS) can highly benefit from background information, e.g., about the environment of a user, it makes sense to leverage that enormous amount of data already present in the LOD cloud to enhance the quality of these services. Within this work, the...
Cultural heritage resources are huge and heterogeneous. They include highly structured, very unstructured, and semi-structured data or information obtained from both authorized and unauthorized sources and involving multimedia data including text, audio and video data. With the rapid development of the web, more and more cultural heritage organizations use digital methods to record, store and represent...
In many Semantic Web applications, having RDF predicates sorted by significance is of primarily importance to improve usability and performance. In this paper we focus on predicates available on DBpedia, the most important Semantic Web source of data counting 470 million english triples. Although there is plenty of work in literature dealing with ranking entities or RDF query results, none of them...
In recent years, developing needed software applications via the technique Web Service Composition (WSC) has been more and more popular. Moreover, benefit from the Semantic Web Services (SWSs) technology, it is possible to even automatically conduct WSC, i.e. the Automated Web Service composition (AWSC). Currently the AWSC is a well-studied research subject and which means the existence of a large...
Semantic web works on producing machine readable data. So semantic web aims to overcome the amount of data that is consisted. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that is consisted as a result of the existing pages on the web. Semantic search engines are extensions to traditional...
We present a simple approach to handle recursive SPARQL queries, that is, nested queries that may contain references to the query itself. This powerful feature is obtained by implementing a custom SPARQL function that takes a SPARQL query as a parameter and executes it over a specified endpoint. The behaviour is similar to the SPARQL 1.1 SERVICE clause, with a few fundamental differences: (1) the...
Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery...
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