A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources

From the Semantic Web’s inception, a number of concurrent initiatives have given rise to multiple segments: large semantic datasets, exposed by query endpoints; online Semantic Web documents, in the form of RDF files; and semantically annotated web content (e.g., using RDFa), semantic sources in their own right. In various mobile application scenarios, online semantic data has proven to be useful. While query endpoints are most commonly exploited, they are mainly useful to expose large semantic datasets. Alternatively, mobile RDF stores are utilized to query local semantic data, but this requires the design-time identification and replication of relevant data. Instead, we present a mobile query service that supports on-the-fly and integrated querying of semantic data, originating from a largely unused portion of the Semantic Web, comprising online RDF files and semantics embedded in annotated webpages. To that end, our solution performs dynamic identification, retrieval and caching of query-relevant semantic data. We explore several data identification and caching alternatives, and investigate the utility of source metadata in optimizing these tasks. Further, we introduce a novel cache replacement strategy, fine- tuned to the described query dataset, and include explicit support for the Open World Assumption. An extensive experimental validation evaluates the query service and its alternative components

Supporting the Mobile Querying of Existing Online Semantic Web Data for Context-Aware Applications

[EN] Mobile devices are increasingly multifunctional and personal, providing mobile applications with the necessary user information to achieve personalization. At the same time, detection technologies let such devices find nearby physical entities and thus map the user's environment. By exploiting existing online Semantic Web sources about these detected entities, mobile applications can further improve personalization. SCOUT is a mobile application framework that supports linking physical entities to online semantic data sources. It provides applications with an integrated, query-able view on these sources and the user's environment. The authors developed a tailored data management approach to efficiently access these distributed online semantic sources.Sven Casteleyn is supported by EC Marie Curie grant FP7- PEOPLE-2009-IEF, number 254383.Van Woensel, W.; Casteleyn, S.; Paret, E.; De Troyer, O. (2011). Supporting the Mobile Querying of Existing Online Semantic Web Data for Context-Aware Applications. IEEE Internet Computing. 15(6):32-39. https://doi.org/10.1109/MIC.2011.108323915

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

Optimizing Queries to Remote Resources

One key property of the Semantic Web is its support for interoperability. Recent research in this area focuses on the integration of multiple data sources to facilitate tasks such as ontology learning, user query expansion and context recognition. The growing popularity of such machups and the rising number of Web APIs supporting links between heterogeneous data providers asks for intelligent methods to spare remote resources and minimize delays imposed by queries to external data sources. This paper suggests a cost and utility model for optimizing such queries by leveraging optimal stopping theory from business economics: applications are modeled as decision makers that look for optimal answer sets. Queries to remote resources cause additional cost but retrieve valuable information which improves the estimation of the answer set's utility. Optimal stopping optimizes the trade-off between query cost and answer utility yielding optimal query strategies for remote resources. These strategies are compared to conventional approaches in an extensive evaluation based on real world response times taken from seven popular Web services

Applying Optimal Stopping for Optimizing Queries to External Semantic Web Resources

The rapid increase in the amount of available information from various online sources poses new challenges for programs that endeavor to process these sources automatically and identify the most relevant material for a given application. This paper introduces an approach for optimizing queries to Semantic Web resources based on ideas originally proposed by MacQueen for optimal stopping in business economics. Modeling applications as decision makers looking for optimal action/answer sets, facing search costs for acquiring information, test costs for checking these information, and receiving a reward depending on the usefulness of the proposed solution, yields strategies for optimizing queries to external services. An extensive evaluation compares these strategies to a conventional coverage based approach, based on real world response times taken from popular Web services

MapReduce-based Solutions for Scalable SPARQL Querying

The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and rconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results