We show that relational algebra calculations for incomplete databases, probabilistic databases, bag semantics and whyprovenance are particular cases of the same general algorithms involving semirings. This further suggests a comprehensive provenance representation that uses semirings of polynomials. We extend these considerations to datalog and semirings of formal power series. We give algorithms for datalog provenance calculation as well as datalog evaluation for incomplete and probabilistic databases. Finally, we show that for some semirings containment of conjunctive queries is the same as for standard set semantics.

Summary.Although real-scale Semantic Web applications, such as Knowledge Portals and E-Marketplaces, require the management of voluminous resource metadata, sufficiently expressive declarative languages for metadata created according to the W3C RDF /S standard l are still missing. In answer to this need, we have designed a typed, functional query language, called RQL, whose novelty lies in its ability to smoothly combine schema and data querying. The purpose of this chapter is to present RQ L's formal data model and type system and illustrate its expressiveness by means of exemplary queries. RQL's formal foundations capture the RDF /S modeling primitives and provide a well-founded semantics for a declarative query language involving recursion and functional composition over complex description graphs. IntroductionIn the next evolutionary step of the Web, termed the Semantic Web [18.5], vast amounts of information resourees (data, doeuments, programs, ete.) will be made available along with various kinds of deseriptive information, i.e.,

Many advanced data management operations (e.g., incremental maintenance, trust assessment, debugging schema mappings, keyword search over databases, or query answering in probabilistic databases), involve computations that look at how a tuple was produced, e.g., to determine its score or existence. This requires answers to queries such as, "Is this data derivable from trusted tuples?"; "What tuples are derived from this relation?"; or "What score should this answer receive, given initial scores of the base tuples?". Such questions can be answered by consulting the provenance of query results.In recent years there has been significant progress on formal models for provenance. However, the issues of provenance storage, maintenance, and querying have not yet been addressed in an application-independent way. In this paper, we adopt the most general formalism for tuple-based provenance, semiring provenance. We develop a query language for provenance, which can express all of the aforementioned types of queries, as well as many more; we propose storage, processing and indexing schemes for data provenance in support of these queries; and we experimentally validate the feasibility of provenance querying and the benefits of our indexing techniques across a variety of application classes and queries.

Sharing structured data today requires standardizing upon a single schema, then mapping and cleaning all of the data. This results in a single queriable mediated data instance. However, for settings in which structured data is being collaboratively authored by a large community, e.g., in the sciences, there is often a lack of consensus about how it should be represented, what is correct, and which sources are authoritative. Moreover, such data is seldom static: it is frequently updated, cleaned, and annotated. The ORCHESTRA collaborative data sharing system develops a new architecture and consistency model for such settings, based on the needs of data sharing in the life sciences. In this paper we describe the basic architecture and implementation of the ORCHESTRA system, and summarize some of the open challenges that arise in this setting.

Abstract. In this paper we benchmark three popular database representations of RDF/S schemata and data: (a) a schema-aware (i.e., one table per RDF/S class or property) with explicit (ISA) or implicit (NOISA) storage of subsumption relationships, (b) a schema-oblivious (i.e., a single table with triples of the form subject-predicate-object ), using (ID) or not (URI) identifiers to represent resources and (c) a hybrid of the schema-aware and schema-oblivious representations (i.e., one table per RDF/S meta-class by distinguishing also the range type of properties). Furthermore, we benchmark two common approaches for evaluating taxonomic queries either on-the-fly (ISA, NOISA, Hybrid), or by precomputing the transitive closure of subsumption relationships (MatView, URI, ID). The main conclusion drawn from our experiments is that the evaluation of taxonomic queries is most efficient over RDF/S stores utilizing the Hybrid and MatView representations. Of the rest, schema-aware representations (ISA, NOISA) exhibit overall better performance than URI, which is superior to that of ID, which exhibits the overall worst performance.