A model of process documentation to determine provenance in mash-ups

Future Generation Computer Systems

Clifford

Freire³

et al. 2011

Self Cite

584

470

The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenance model. (3) To define provenance in a precise, technologyagnostic manner. (4) To support a digital representation of provenance for any "thing", whether produced by computer systems or not. (5) To allow multiple levels of description to coexist. (6) To define a core set of rules that identify the valid inferences that can be made on provenance representation. This document contains the specification of the Open Provenance Model (v1.1) resulting from a community-effort to achieve inter-operability in the Third Provenance Challenge.

Section: Discussion Related and Future Workmentioning

confidence: 99%

The Open Provenance Model core specification (v1.1)

Future Generation Computer Systems

Clifford

Freire³

et al. 2011

Self Cite

584

470

“…Its intellectual base consists of a vast majority of workflow papers [250,174,201,97,256,294,419,39], a provenance-aware operating sytem [301], and a survey [36]; they are accompanied by database papers, which are also [1,216,75]. The research front also consists of papers that, by and large, are system-oriented.…”

Section: Analysis Of Research Trendsmentioning

confidence: 99%

The Foundations for Provenance on the Web

2010

FNT in Web Science

Self Cite

168

Provenance, i.e., the origin or source of something, is becoming an important concern, since it offers the means to verify data products, to infer their quality, to analyse the processes that led to them, and to decide whether they can be trusted. For instance, provenance enables the reproducibility of scientific results; provenance is necessary to track attribution and credit in curated databases; and, it is essential for reasoners to make trust judgements about the information they use over the Semantic Web.As the Web allows information sharing, discovery, aggregation, filtering and flow in an unprecedented manner, it also becomes very difficult to identify, reliably, the original source that produced an information item on the Web. Since the emerging use of provenance in niche applications is undoubtedly demonstrating the benefits of provenance, we contend that provenance can and should reliably be tracked and exploited on the Web, and we survey the necessary foundations to achieve such a vision.Using multiple data sources, we have compiled the largest bibliographical database on provenance so far. This large corpus allows us to analyse emerging trends in the research community. Specifically, using the CiteSpace tool, we identify clusters of papers that constitute research fronts, from which we derive characteristics that we use to structure our foundational framework for provenance on the Web. We note that such an endeavour requires a multi-disciplinary approach, since it requires contributions from many computer science sub-disciplines, but also other non-technical fields given the human challenge that is anticipated.To develop our vision, it is necessary to provide a definition of provenance that applies to the Web context. Our conceptual definition of provenance is expressed in terms of processes, and is shown to generalise various definitions of provenance commonly encountered. Furthermore, by bringing realistic distributed systems assumptions, we refine our definition as a query over assertions made by processes.Given that the majority of work on provenance has been undertaken by the database, workflow and e-science communities, we review some of their work, contrasting approaches, and focusing on important topics we believe to be crucial for bringing provenance to the Web, such as abstraction, collections, storage, queries, workflow evolution, semantics and activities involving human interactions.However, provenance approaches developed in the context of databases and workflows essentially deal with closed systems. By that, we mean that workflow or database management systems are in full control of the data they manage, and track their provenance within their own scope, but not beyond. In the context of the Web, a broader approach is required by which chunks of provenance representation can be brought together to describe the provenance of information flowing across multiple systems. This is the specific purpose of the Open Provenance Vision, which is an approach that consists of controlled voc...

“…We also obtain the list of ancestors' identifiers by the accessor ancestor G (id), which is lexicographically ordered. The hash-value contained in each node is calculated according to Equation (4).…”

Section: Secured Provenance Graphmentioning

confidence: 99%

Securing Provenance-Based Audits

Aldeco-Pérez

Lecture Notes in Computer Science

2010

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Abstract. Given the significant increase of on-line services that require personal information from users, the risk that such information is misused has become an important concern. In such a context, information accountability is desirable since it allows users (and society in general) to decide, by means of audits, whether information is used appropriately. To ensure information accountability, information flow should be made transparent. It has been argued that data provenance can be used as the mechanism to underpin such a transparency. Under these conditions, an audit's quality depends on the quality of the captured provenance information. Thereby, the integrity of provenance information emerges as a decisive issue in the quality of a provenance-based audit. The aim of this paper is to secure provenance-based audits by the inclusion of cryptographic elements in the communication between the involved entities as well as in the provenance representation. This paper also presents a formalisation and an automatic verification of a set of security properties that increase the level of trust in provenance-based audit results.