Abstract. We discuss, compare and relate some old and some new models for incomplete and probabilistic databases. We characterize the expressive power of c-tables over infinite domains and we introduce a new kind of result, algebraic completion, for studying less expressive models. By viewing probabilistic models as incompleteness models with additional probability information, we define completeness and closure under query languages of general probabilistic database models and we introduce a new such model, probabilistic c-tables, that is shown to be complete and closed under the relational algebra. 1

Provenance in the context of workflows, both for the data they derive and for their specification, is an essential component to allow for result reproducibility, sharing, and knowledge re-use in the scientific community. Several workshops have been held on the topic, and it has been the focus of many research projects and prototype systems. This tutorial provides an overview of research issues in provenance for scientific workflows, with a focus on recent literature and technology in this area. It is aimed at a general database research audience and at people who work with scientific data and workflows. We will (1) provide a general overview of scientific workflows, (2) describe research on provenance for scientific workflows and show in detail how provenance is supported in existing systems; (3) discuss emerging applications that are enabled by provenance; and (4) outline open problems and new directions for database-related research.

The Open Provenance Model (OPM) is a community-driven data model for Provenance that is designed to support inter-operability of provenance technology. Underpinning OPM, is a notion of directed acyclic graph, used to represent data products and processes involved in past computations, and causal dependencies between these. The Open Provenance Model was derived following two “Provenance Challenges”, international, multidisciplinary activities trying to investigate how to exchange information between multiple systems supporting provenance and how to query it. The OPM design was mostly driven by practical and pragmatic considerations, and is being tested in a third Provenance Challenge, which has just started. The purpose of this paper is to investigate the theoretical foundations of this data model. The formalisation consists of a set-theoretic definition of the data model, a definition of the inferences by transitive closure that are permitted, a formal description of how the model can be used to express dependencies in past computations, and finally, a description of the kind of time-based inferences that are supported. A novel element that OPM introduces is the concept of an account, by which multiple descriptions of a same execution are allowed to co-exist in a same graph. Our formalisation gives a precise meaning to such accounts and associated notions of alternate and refinement. Warning It was decided that this paper should be released as early as possible since it brings useful clarifications on the Open Provenance Model, and therefore can benefit the Provenance Challenge 3 community. The reader should recognise that this paper is however an early draft, and several sections are incomplete. Additionally, figures rely on colours but these may be difficult to read when printed in a black and white. It is advisable to print the paper in colour. 1 1

The need to understand and manage provenance arises in almost every scientific application. In many cases, information about provenance constitutes the proof of correctness of results that are generated by scientific applications. It also determines the quality and amount of trust one places on the results. For these reasons, the knowledge of provenance of a scientific result is typically regarded to be as important as the result itself. In this paper, we provide an overview of research in provenance in databases and discuss some future research directions. The content of this paper is largely based on the tutorial presented at SIGMOD 2007 [11].

Abstract. Provenance is information recording the source, derivation, or history of some information. Provenance tracking has been studied in a variety of settings; however, although many design points have been explored, the mathematical or semantic foundations of data provenance have received comparatively little attention. In this paper, we argue that dependency analysis techniques familiar from program analysis and program slicing provide a formal foundation for forms of provenance that are intended to show how (part of) the output of a query depends on (parts of) its input. We introduce a semantic characterization of such dependency provenance, show that this form of provenance is not computable, and provide dynamic and static approximation techniques. 1

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers dependence between data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources (e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracy of data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources. 1.

The automated tracking and storage of provenance information promises to be a major advantage of scientific workflow systems. We discuss issues related to data and workflow provenance, and present techniques for focusing user attention on meaningful provenance through “user views,” for managing the provenance of nested scientific data, and for using information about the evolution of a workflow specification to understand the difference in the provenance of similar data products.

Modern information management applications often require integrating data from a variety of data sources, some of which may copy or buy data from other sources. When these data sources model a dynamically changing world (e.g., people’s contact information changes over time, restaurants open and go out of business), sources often provide out-of-date data. Errors can also creep into data when sources are updated often. Given out-of-date and erroneous data provided by different, possibly dependent, sources, it is challenging for data integration systems to provide the true values. Straightforward ways to resolve such inconsistencies (e.g., voting) may lead to noisy results, often with detrimental consequences. In this paper, we study the problem of finding true values and determining the copying relationship between sources, when the update history of the sources is known. We model the quality of sources over time by their coverage, exactness and freshness. Based on these measures, we conduct a probabilistic analysis. First, we develop a Hidden Markov Model that decides whether a source is a copier of another source and identifies the specific moments at which it copies. Second, we develop a Bayesian model that aggregates information from the sources to decide the true value for a data item, and the evolution of the true values over time. Experimental results on both real-world and synthetic data show high accuracy and scalability of our techniques. 1.

We study containment and equivalence of (unions of) conjunctive queries on relations annotated with elements of a commutative semiring. Such relations and the semantics of positive relational queries on them were introduced in a recent paper as a generalization of set semantics, bag semantics, incomplete databases, and databases annotated with various kinds of provenance information. We obtain positive decidability results and complexity characterizations for databases with lineage, why-provenance, and provenance polynomial annotations, for both conjunctive queries and unions of conjunctive queries. At least one of these results is surprising given that provenance polynomial annotations seem “more expressive ” than bag semantics and under the latter, containment of unions of conjunctive queries is known to be undecidable. The decision procedures rely on interesting variations on the notion of containment mappings. We also show that for any positive semiring (a very large class) and conjunctive queries without self-joins, equivalence is the same as isomorphism. 1.

The Web has enabled the availability of a huge amount of useful information, but has also eased the ability to spread false information and rumors across multiple sources, making it hard to distinguish between what is true and what is not. Recent examples include the premature Steve Jobs obituary, the second bankruptcy of United airlines, the creation of Black Holes by the operation of the Large Hadron Collider, etc. Since it is important to permit the expression of dissenting and conflicting opinions, it would be a fallacy to try to ensure that the Web provides only consistent information. However, to help in separating the wheat from the chaff, it is essential to be able to determine dependence between sources. Given the huge number of data sources and the vast volume of conflicting data available on the Web, doing so in a scalable manner is extremely challenging and has not been addressed by existing work yet. In this paper, we present a set of research problems and propose some preliminary solutions on the issues involved in discovering dependence between sources. We also discuss how this knowledge can benefit a variety of technologies, such as data integration and Web 2.0, that help users manage and access the totality of the available information from various sources. 1.