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.

Abstract. 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whenever a new window of streaming data arrives, by dropping explicit statements and entailments that are no longer valid, and then computing when the RDF triples inserted within the window will expire.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.

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.

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.

Where can I attend an interesting database workshop close to a sunny beach? Who are the strongest experts on service computing based upon their recent publication record and accepted European projects? Can I spend an April weekend in a city served by a low-cost direct flight from Milano offering a Mahler's symphony? We regard the above queries as multi-domain queries, i.e., queries that can be answered by combining knowledge from two or more domains (such as: seaside locations, flights, publications, accepted projects, conference offerings, and so on). This information is available on the Web, but no general-purpose software system can accept the above queries nor compute the answer. At the most, dedicated systems support specific multi-domain compositions (e.g., Google-local locates information such as restaurants and hotels upon geographic maps).This paper presents an overall framework for multi-domain queries on the Web. We address the following problems: (a) expressing multi-domain queries with an abstract formalism, (b) separating the treatment of "search" services within the model, by highlighting their differences from "exact" Web services, (c) explaining how the same query can be mapped to multiple "query plans", i.e., a well-defined scheduling of service invocations, possibly in parallel, which complies with their access limitations and preserves the ranking order in which search services return results; (d) introducing crossdomain joins as first-class operation within plans; (e) evaluating the query plans against several cost metrics so as to choose the most promising one for execution. This framework adapts to a variety of application contexts, ranging from end-user-oriented mash-up scenarios up to complex application integration scenarios.

No abstract

Abstract-Many data integration solutions in the market today include tools for schema mapping, to help users visually relate elements of different schemas. Schema elements are connected with lines, which are interpreted as mappings, i.e. high-level logical expressions capturing the relationship between source and target data-sets; these are compiled into queries and programs that convert source-side data instances into target-side instances. This paper describes Clip, an XML Schema mapping tool distinguished from existing tools in that mappings explicitly specify structural transformations in addition to value couplings. Since Clip maps hierarchical XML schemas, lines appear naturally nested. We describe the transformation semantics associated with our "lines" and how they combine to form mappings that are more expressive than those generated by Clio, a well-known mapping tool. Further, we extend Clio's mapping generation algorithms to generate Clip's mappings.

The recent success of XML as a standard to represent semi-structured data, and the increasing amount of available XML data, pose new challenges to the data mining community. In this paper we present the X MINE operator a tool we developed to extract XML association rules for XML documents. The operator; that is based on XPath and inspired by the syntax ofXQuery, allows us to express complex mining tasks, compactly and intuitively. X MINE can be used to specify indifferently ( and simultaneously) mining tasks both on the content and on the structure of the data, since the distinction in XML is slight. changed among data mining tools (e.g., PMML [8]); but there are no significant extensions of data mining research taking full advantage of the intrinsic properties of XML. However, it is easy to foresee that the spreading of XML will cause an increasing interest on this subject, going beyond a mere syntactic adaptation to XML of data mining artifacts and techniques.In this paper, we present the X MINE operator, a tool that can be used to extract association rules from native XML documents, shortly "XML association rules", which we first introduced in [6, 5]. The paper is organized as follows. In Section 2 we overview association rules in the context of relational databases. In Section 3 we shortly discuss the notion of association rules for XML while we refer the readers to [6,5] for additional details about the their theoretical foundations. In Section 4 we present the X MINE operator through a serie of intuitive examples. In Section 5 we introduce some basic concepts needed to discuss implementation details. In Section 6 we discuss how XML association rules are extracted from an XML document through XMINE by composing an execution environment for XPath expressions and an algorithm for discovering frequent itemsets. In Section 7 we give some implementation details discussing the current state of the prototype we developed and an outline of the planned future development. We conclude the paper with a discussion of future research directions.