Michael Grossniklaus

C-SPARQL: SPARQL for continuous querying

C-SPARQL is an extension of SPARQL to support continuous queries, registered and continuously executed over RDF data streams, considering windows of such streams. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with knowledge that evolves over time. We present C-SPARQL by means of examples in Urban Computing.

C-SPARQL: A CONTINUOUS QUERY LANGUAGE FOR RDF DATA STREAMS

This article defines C-SPARQL, an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time. C-SPARQL is presented by means of a full specification of the syntax, a formal semantics, and a comprehensive set of examples, relative to urban computing applications, that systematically cover the SPARQL extensions. The expression of meaningful queries over streaming data is strictly connected to the availability of aggregation primitives, thus C-SPARQL also includes extensions in this respect.

Querying RDF streams with C-SPARQL

Continuous SPARQL (C-SPARQL) is a new language for continuous queries over streams of RDF data. CSPARQL queries consider windows, i.e., the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with knowledge evolving over time. Examples of such application domains include real-time reasoning over sensors, urban computing, and social semantic data. In this paper, we present the C-SPARQL language extensions in terms of both syntax and examples. Finally, we discuss existing applications that already use C-SPARQL and give an outlook on future research opportunities.

An execution environment for C-SPARQL queries

Continuous SPARQL (C-SPARQL) is proposed as new language for continuous queries over streams of RDF data. It covers a gap in the Semantic Web abstractions which is needed for many emerging applications, including our focus on Urban Computing. In this domain, sensor-based information on roads must be processed to deduce localized traffic conditions and then produce traffic management strategies. Executing C-SPARQL queries requires the effective integration of SPARQL and streaming technologies, which capitalize over a decade of research and development; such integration poses several nontrivial challenges.n In this paper we (a) show the syntax and semantics of the C-SPARQL language together with some examples; (b) introduce a query graph model which is an intermediate representation of queries devoted to optimization; (c) discuss the features of an execution environment that leverages existing technologies; (d) introduce optimizations in terms of rewriting rules applied to the query graph model, so as to efficiently exploit the execution environment; and (e) show evidence of the effectiveness of our optimizations on a prototype of execution environment.

Incremental reasoning on streams and rich background knowledge

This article presents a technique for Stream Reasoning, consisting in incremental maintenance of materializations of ontological entailments in the presence of streaming information. Previous work, delivered in the context of deductive databases, describes the use of logic programming for the incremental maintenance of such entailments. Our contribution is a new technique that exploits the nature of streaming data in order to efficiently maintain materialized views of RDF triples, which can be used by a reasoner. n nBy adding expiration time information to each RDF triple, we show that it is possible to compute a new complete and correct materialization whenever a new window of streaming data arrives, by dropping explicit statements and entailments that are no longer valid, and then computing when the RDF triples inserted within the window will expire. We provide experimental evidence that our approach significantly reduces the time required to compute a new materialization at each window change, and opens up for several further optimizations.

Search computing challenges and directions

Search Computing (SeCo) is a project funded by the European Research Council (ERC). It focuses on building the answers to complex search queries like Where can I attend an interesting conference in my field close to a sunny beach? by interacting with a constellation of cooperating search services, using ranking and joining of results as the dominant factors for service composition. SeCo started on November 2008 and will last 5 years. This paper will give a general introduction to the Search Computing approach and then focus on its query optimization and execution engine, the aspect of the project which is most tightly related to objects and databases technologies.

Interplay of content and context

We examine the relationship between context engines and content management systems, showing by means of an example application how these should mutually interact with each other to ensure the timely delivery of relevant information. We show how a content management system can use context information to enrich its functionality and also how a general and abstract approach to content management can support context awareness. Information models of the general context engine and content management system that we have developed are presented, along with a description of how a symbiotic relationship of content and context can be achieved through the integration of these models.

Information concepts for content management

Content delivery is rapidly emerging as a complex systemsdomain concerned with multi-channel, multi-formatpublication of information across user and application domains.A variety of content management solutions havebeen developed in response to these challenges based on,not only differing technologies, but also heterogeneous approaches.However, none of these present a solution that isboth sufficient and consistent. Here we present an analysisof requirements leading to a general model of the informationconcepts central to content management. This model isthe basis for a web content management solution currentlyunder development.

Modelling context for information environments

Context-awareness has become an important consideration in the development of mobile and ubiquitous systems. While efforts have been made to develop general context models and application frameworks, it remains the case that often the notion of context supported is very restrictive and/or the representation of context is based on simple key-value pairs. As a result, most existing systems lack well-defined semantics and typing for context that would facilitate the general implementation, maintenance and adaption of context-aware systems. In this paper, we present a general context model that consolidates the models underlying many other approaches, while moving to a higher-level of abstraction in terms of semantic context description.

Context-aware platform for mobile data management

Interaction design is a major issue for mobile information systems in terms of not only the choice of input/output channels and presentation of information, but also the application of context-awareness. To support experimentation with these factors, we have developed platforms to support the rapid prototyping of multi-channel, multi-modal, context-aware applications. The Java-based platform presented here is based on an integration of a cross-media link server and an object-oriented framework for advanced content publishing, along with a Client Controller and Context Engine. We also describe how this platform was used to develop a mobile tourist information system for an international arts festival where interaction was based on a combination of interactive paper and speech output.