Grossniklaus, Michael
0000-0003-1609-2221 Berufsbeschreibung
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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.
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.
Search Computing Systems (Extended Abstract)
Search Computing defines a new class of applications, which enable end users to perform exploratory search processes over multi-domain data sources available on the Web. These applications exploit suitable software frameworks and models that make it possible for expert users to configure the data sources to be searched and the interfaces for query submission and result visualization. We describe some usage scenarios and the reference architecture for Search Computing systems.
Panta Rhei : Optimized and Ranked Data Processing over Heterogeneous Sources
In the era of digital information, the value of data resides not only in its volume and quality, but also in the additional information that can be inferred from the combination (aggregation, comparison and join) of such data. There is a concrete need for data processing solutions that combine distributed and heterogeneous data sources, such as Web services, relational databases, and even search engines, that can all be modeled as services. In this demonstration, we show how our Panta Rhei model addresses the challenge of processing data over heterogeneous sources to provide feasible and ranked combinations of these services.
Efficient Computation of Search Computing Queries
This chapter gives a high-level overview of how query processing is carried out in SeCo. At the highest level of abstraction, queries are expressed in a conjunctive declarative query language over service interfaces, named SeCoQL, chosen to be a compact and readable formulation to serve both experts users and system developers. Queries are then expressed at a logical level in the form of acyclic invocation workflows, after a compile-time analysis that decides a cost-driven scheduling of service invocations. At a lower, physical level queries are then translated into executable specifications that distinguish between the data flow and the control flow, support parallelism, account for stateless and stateful computation tasks, and support backward and forward control. The query engine is implemented as an interpreter of these physical plans. A workbench and testing environment is also available in the form of a tool, to monitor the processing of complex queries by inspecting all phases of their analysis and execution, at all levels of abstraction.
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.
Chapter 10 : Join Methods and Query Optimization
Joins between data sources are an essential ingredient of multi-domain queries, as they exploit connection patterns defined between service marts or between service interfaces. This chapter moves from the definition of a query language over service interfaces, sketching how queries can be directly expressed over service marts and how these can be translated over service interfaces. The fundamental operation discussed in this chapter is the binary join between two sources, which is influenced by the type (search vs. exact) of services and by the management (parallel vs. sequential) of service calls. Then, this chapter presents an optimization framework for queries over several service interfaces, which considers several cost metrics for mapping queries into query plans, consisting of specific operations over services, and includes a branch and bound approach to the exploration of the combinatorial search space of all possible query plans.
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.
By 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.