A Provenance Tracking Model for Data Updates

Egyptian Computer Science Journal, 2000

This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies.

Lecture Notes in Computer Science, 2012

In this paper, we propose a provenance model able to represent the provenance of any data object captured at any abstraction layer (workflow/process/OS) and present an abstract schema of the model. The expressive nature of the model makes it potential to be utilized in real world data processing systems.

Future Generation Computer Systems, 2011

From the World Wide Web to supply chains and scientific simulations, distributed systems are a widely used and important approach to building computational systems. Tracking provenance within these systems is crucial for determining the trustworthiness of data they produce, troubleshooting problems, assigning responsibility for decisions, and improving performance. To facilitate such tracking, the Open Provenance Model (OPM) has been created to enable the interchange of provenance between a distributed system's components. However, to date, the ability of OPM to represent distributed systems has not been verified. In this work, we show how OPM can be used to represent a set of distributed systems' patterns. We present a profile that shows that these patterns are a specialisation of OPM. Finally, we define a contract that enables participants in a distributed system to ensure that their provenance can be integrated cohesively.

Provenance is an increasing concern due to the ongoing revolution in sharing and processing scientific data on the Web and in other computer systems. It is proposed that many computer systems will need to become provenanceaware in order to provide satisfactory accountability, reproducibility, and trust for scientific or other high-value data. To date, there is not a consensus concerning appropriate formal models or security properties for provenance. In previous work, we introduced a formal framework for provenance security and proposed formal definitions of properties called disclosure and obfuscation. In this article, we study refined notions of positive and negative disclosure and obfuscation in a concrete setting, that of a general-purpose programing language. Previous models of provenance have focused on special-purpose languages such as workflows and database queries. We consider a higher-order, functional language with sums, products, and recursive types and functions, and equip it with a tracing semantics in which traces themselves can be replayed as computations. We present an annotation-propagation framework that supports many provenance views over traces, including standard forms of provenance studied previously. We investigate some relationships among provenance views and develop some partial solutions to the disclosure and obfuscation problems, including correct algorithms for disclosure and positive obfuscation based on trace slicing.

2010

Provenance has been studied extensively in both database and workflow management systems, so far with little convergence of definitions or models. Provenance in databases has generally been defined for relational or complex object data, by propagating fine-grained annotations or algebraic expressions from the input to the output. This kind of provenance has been found useful in other areas of computer science: annotation databases, probabilistic databases, schema and data integration, etc. In contrast, workflow provenance aims to capture a complete description of evaluation-or enactment-of a workflow, and this is crucial to verification in scientific computation. Workflows and their provenance are often presented using graphical notation, making them easy to visualize but complicating the formal semantics that relates their run-time behavior with their provenance records. We bridge this gap by extending a previously-developed dataflow language which supports both database-style querying and workflow-style batch processing steps to produce a workflow-style provenance graph that can be explicitly queried. We define and describe the model through examples, present queries that extract other forms of provenance, and give an executable definition of the graph semantics of dataflow expressions.

Concurrency and …, 2008

Proceedings of the 10th ACM SIGPLAN International Conference on Certified Programs and Proofs

In multiple domains, large amounts of data are daily generated and combined to be analyzed. The interpretation of these analyses requires to track back the provenance of combined data with respect to initial, raw data. The correctness of the provenance is crucial in many critical domains, such as medicine to prescribe treatments. In this article, we propose the first provenance-aware extended relational algebra formalized in a proof assistant (Coq), for a non trivial subset of database queries: queries containing aggregates, null values, and correlated sub-queries. The formalization is validated by an adequacy proof with respect to standard evaluation of queries. This development is a first step towards a posteriori certification of provenance for data manipulation, with strong guaranties.

2008

The concept of provenance is already well understood in the study of fine art where it refers to the trusted, documented history of some work of art. Given that documented history, the object attains an authority that allows scholars to understand and appreciate its importance and context relative to other works of art. This same concept of provenance may also be applied to data and information generated within a computer system; particularly when the information is subject to regulatory control over an extended period of time.

2010

CHAPTER 1. INTRODUCTION Chapter 2 Contribution Semantics In this chapter we formally define Perm-Influence-Contribution-Semantics (PI-CS) the contribution semantics developed for the Perm system and prove several important properties of the provenance generated by this CS type. PI-CS is a type of I-CS based on Lineage-CS. We decided to develop our own type of CS, because the representation used by Lineage-CS (and also other CS types) is not suited for our approach to implement a "purely relational" provenance management system. Furthermore, as we will demonstrate in this chapter, Lineage-CS, in contrast to PI-CS, does not extend to queries with nested sub-queries (sublinks) and queries with negation. We define transformation provenance CS for the use in TRAMP as extensions of PI-CS. First, we introduce an extended relational algebra which allows for a natural algebraic representation of SQL queries. Afterwards, we introduce PI-CS, demonstrate its applicability to the operators of the algebra, and study the relationship between this CS type and Lineage-CS. Finally, we present transformation provenance. Note that in this chapter we are only discussing the semantics of provenance and do not develop algorithms for generating provenance according to this semantics. Provenance computation is discussed in chapter 3.

Proceedings of the UK OST e-Science Third …, 2004