Open Provenance Model Research Articles

Enriching the Open Provenance Model for a Privacy-Aware Provenance Management

Today, the total amount of data that is generated, copied, and stored are increasing rapidly. Thereupon, the trustworthiness of the data source and the quality of data have significant importance for an effective data analysis. Therefore, it is critical to improve accountability for the quality of data. For this purpose, provenance information is used to provide the quality of data. Provenance information ensures the reliability and quality of data. Data provenance is a form of metadata to describe the life cycle of a data. Therefore, provenance information maintains the history of the data by describing how data are derived. The Open Provenance Model (OPM) aims to meet the requirements of a provenance model. For this purpose, OPM defines a core set of rules. Thus, OPM provides provenance interoperability. In this study, OPM is enhanced to provide a Privacy-Aware Provenance Management (PAPM) model. The goal of the PAPM model is to use provenance information in order to protect data from unwanted access and detect security violations. Therefore, PAPM uses provenance information to protect data privacy. Since the proposed PAPM model is domain-independent, it can be integrated into any interested domain to preserve privacy and ensure data security.

Improving privacy in health care with an ontology‐based provenance management system

AbstractProvenanc refers to the origin of information. Therefore, provenance is the metadata that record the history of data. As provenance is the derivation history of an object starting from its original source, the provenance information is used to analyse processes that are performed on an object and to track by whom these processes are performed. Thus, provenance shows the trustworthiness and quality of data. In a provenance management system in order to verify the trustworthy of provenance information, security needs must be also fulfilled. In this work, an ontology‐based privacy‐aware provenance management model is proposed. The proposed model is based on the Open Provenance Model, which is a common model for provenance. The proposed model aims to detect privacy violations, to reduce privacy risks by using permissions and prohibitions, and also to query the provenance data. The proposed model is implemented with Semantic Web technologies and demonstrated for the health care domain in order to preserve patients' privacy. Also, an infectious disease ontology and a vaccination ontology are integrated to the system in order to track the patients' vaccination history, to improve the quality of medical processes, the reliability of medical data, and the decision making in the health care domain.

A novel approach to provenance management for privacy preservation

Provenance determines the origin of the data by tracing and recording the actions that are performed on the data. Therefore, provenance is used in many fields to ensure the reliability and quality of data. In this work, provenance information is used to meet the security needs in information systems. For this purpose, a domain-independent provenance model is proposed. The proposed provenance model is based on the Open Provenance Model and Semantic Web technologies. The goal of the proposed provenance model is to integrate the provenance and security concepts in order to detect privacy violations by querying the provenance data. In order to evaluate the proposed provenance model, we illustrated our domain-independent model by integrating it with an infectious disease domain and implemented the Healthcare Provenance Information System.

Provenance data discovery through Semantic Web resources

SummaryProviding historical information to deal with knowledge loss about a scientific experiment has been the focus of some several researches. However, computational support for large‐scale scientific experiments is still incipient and is considered one of e‐science's greatest challenges. In this vein, providing provenance information to a scientist is part of these challenges. Provenance information helps to assure the reliability and reproducibility of experiments. Therefore, this work has as its main objective to present a new ontology—Open Provenance Model Ontology‐e (OPMO‐e)—which is part of an architecture, named SciProvMiner. Open Provenance Model Ontology‐e is a new ontology that encompasses both prospective and retrospective provenance. SciProvMiner captures the provenance and implements all inference and completeness rules defined by Open Provenance Model to provide provenance information beyond those already established. We hypothesize that the capture and management of provenance data will provide scientists with implicit strategical information about the experiment through OPMO‐e. Case studies were carried out to evaluate OPMO‐e use considering SciProvMiner. The obtained results revealed that the use of the proposed ontology and SciProvMiner can enhance scientist's knowledge about an experiment.

Flow-Based Provenance

Aim/Purpose: With information almost effortlessly created and spontaneously available, current progress in Information and Communication Technology (ICT) has led to the complication that information must be scrutinized for trustworthiness and provenance. Information systems must become provenance-aware to be satisfactory in accountability, reproducibility, and trustworthiness of data. Background: Multiple models for abstract representation of provenance have been proposed to describe entities, people, and activities involved in producing a piece of data, including the Open Provenance Model (OPM) and the World Wide Web Consortium. These models lack certain concepts necessary for specifying workflows and encoding the provenance of data products used and generated. Methodology: Without loss of generality, the focus of this paper is on OPM depiction of provenance in terms of a directed graph. We have redrawn several case studies in the framework of our proposed model in order to compare and evaluate it against OPM for representing these cases. Contribution: This paper offers an alternative flow-based diagrammatic language that can form a foundation for modeling of provenance. The model described here provides an (abstract) machine-like representation of provenance. Findings: The results suggest a viable alternative in the area of diagrammatic representation for provenance applications. Future Research: Future work will seek to achieve more accurate comparisons with current models in the field.

A Formal Account of the Open Provenance Model

On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The open provenance model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present article extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The article concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs.

Komadu: A Capture and Visualization System for Scientific Data Provenance

Data provenance captured from scientific applications is a critical precursor to data sharing and reuse. For researchers wanting to repurpose data, it is a source of information about the lineage and attribution of the data and this is needed in order to establish trust in a data set. Komadu is a standalone provenance capture and visualization system for capturing, representing, and manipulating provenance coming from scientific tools, infrastructures, and repositories. It uses the W3C PROV standard [1] in representing data, and it is the successor of the Karma [2] provenance capture system which was based on Open Provenance Model (OPM) [3]. Komadu comes with two different interfaces: a Web Services interface based on Apache Axis2 [4] and a messaging interface based on RabbitMQ [5]. Komadu is completely open source and the source code is publicly available on GitHub [6]. Even though Komadu has been used most extensively in relation to scientific research, its interfaces are designed to collect and visualize provenance of any kind of application needing provenance.

A workflow modeling system for capturing data provenance

A workflow is an abstraction of the steps associated with the underlying work process and is typically modeled as a directed graph. The workflow concept under its various manifestations has been used to model applications in diverse areas, including project planning, manufacturing, scientific experiments, execution of computer software, and publishing. While the Open Provenance Model Core Specification had laid the foundation for defining the key concepts in a workflow, a simplified high level graphical representation of a workflow that is widely applicable is not available. In this paper we describe a novel general framework for building workflows and implementing the associated actions, which will facilitate understanding of work processes across multiple disciplines. As such, most work processes are organized hierarchically with well defined control and management responsibilities. This framework will facilitate integration and coordination of activities across associated domains. Additionally, it will act as a template to refer to the associated metadata as well as reference to access the instance data from archives of completed workflow cases. When a specific case is in progress, a finite state machine will guide the user through the steps and provide up to date information about the current state. We describe the main building blocks in the framework, their functionalities and illustrate the integration of workflows between an experimental and a scientific process.

A provenance-based approach to evaluate data quality in eScience

Data quality is growing in relevance as a research topic. Quality assessment has been progressively incorporated in many business environments, and in software engineering practices. eScience environments, however, because of the multiplicity and heterogeneity of data sources and scientific experts involved in a given problem, complicate data quality assessment. This paper deals with the evaluation of the quality of data managed by eScience applications. Our approach is based on data provenance, i.e. the history of the origins and transformations applied to a given data product. Our contributions include a the specification of a framework to track data provenance and use it to derive quality information, b a model for data provenance based on the Open Provenance Model, and c a methodology to evaluate the quality of data based on its provenance. Our proposal is validated experimentally by a prototype that takes advantage of the Taverna workflow system.

An Extensible Framework for Provenance in Human Terrain Visual Analytics

We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework.

Mapping Geospatial Metadata to Open Provenance Model

This paper maps the data lineage entities in ISO19115 and ISO19115-2, the metadata standards of the International Organization for Standardization for geographic information and for imagery and gridded data [ISO geospatial metadata (GMD)], to the entities in open provenance model (OPM). The term “map” refers to establishing a correspondence between the said entities in ISO GMD and OPM. Presently, many geospatial data available in spatial data infrastructures (SDI) are described using ISO GMD. Its structure, however, makes tracing the provenance of these data a challenging task. OPM prioritizes causal relationships between things for capturing the workflow applied to particular data, making it easier to trace the data provenance. The mapping in this paper provides a convenient means to trace the provenance of data through the OPM causal relations and evaluate the fitness for use of these data, a necessary step toward data integration. This paper uses the notion of process to identify various data processing activities encoded in ISO GMD, the resource and the agent types involved in these activities, and state changes. A software prototype to carry out the mapping is developed. The mapping result is encoded in the resource description framework format to permit integral use of geospatial data in SDI and the data from the open data world. An exemplar metadata in ISO GMD from the National Oceanographic Data Center of the National Oceanic and Atmospheric Administration is used to demonstrate the feasibility to convert from the ISO GMD data lineage entities to the OPM entities.

Toward the modeling of data provenance in scientific publications

In this paper, we implement a provenance-aware system for documenting publications, called PADS. It employs a three-layered provenance hierarchy, which can output diverse types of provenance data related to the research life cycle. From this, we generate different profiles for research ventures, reviewers, and authors. PADS employs the standard Open Provenance Model (OPM) specification for capturing provenance data, and stores this data as ontological instances. We show that data is retrieved without any apparent delay in the execution time of the queries. We also demonstrate how this data can be used to make useful recommendations to the organizers, in order to manage upcoming research ventures.

SWfPS: PROPOSIÇÃO DE UM SISTEMA DE PROVENIÊNCIA DE DADOS E PROCESSOS NO DOMÍNIO DE WORKFLOWS CIENTÍFICOS

This paper describes SWfPS, an architecture that aims to interact with Scientific Workflow Management Systems in order to capture and manipulate provenance metadata. For this purpose, SWfPS adopts an approach based on an abstract model for representing the lineage. This model, called Open Provenance Model, allows SWfPS to set up a homogeneous and interoperable infrastructure for handling provenance metadata. As a result, SWfPS is able to provide a framework for query metadata provenance generated in an e-Science scenario. Moreover, the architecture uses semantic web technology in order to process provenance queries. In this context, using ontologies and inference engines, SWfPS can make inferences about lineage and, based on those inferences, obtain important results from the extraction of information from the managed data, beyond what is explicitly registered.

Enhancing workflow with a semantic description of scientific intent

Scientists are becoming increasingly dependent upon resources available through the Internet including, for example, datasets and computational modelling services, which are changing the way they conduct their research activities. This paper investigates the use of workflow tools enhanced with semantics to facilitate the design, execution, analysis and interpretation of workflow experiments and exploratory studies. Current workflow technologies do not incorporate any representation of experimental constraints and goals, which we refer to in this paper as scientist’s intent. This paper proposes an abstract model of intent based on the Open Provenance Model (OPM) specification. To realise this model a framework based upon a number of Semantic Web technologies has been developed, including the OWL ontology language and the Semantic Web Rule Language (SWRL). Through the use of social simulation case studies the paper illustrates the benefits of using this framework in terms of workflow monitoring, workflow provenance and annotation of experimental results.

Modelling provenance of DBpedia resources using Wikipedia contributions

DBpedia is one of the largest datasets in the linked Open Data cloud. Its centrality and its cross-domain nature makes it one of the most important and most referred to knowledge bases on the Web of Data, generally used as a reference for data interlinking. Yet, in spite of its authoritative aspect, there is no work so far tackling the provenance aspect of DBpedia statements. By being extracted from Wikipedia, an open and collaborative encyclopedia, delivering provenance information about it would help to ensure trustworthiness of its data, a major need for people using DBpedia data for building applications. To overcome this problem, we propose an approach for modelling and managing provenance on DBpedia using Wikipedia edits, and making this information available on the Web of Data. In this paper, we describe the framework that we implemented to do so, consisting in (1) a lightweight modelling solution to semantically represent provenance of both DBpedia resources and Wikipedia content, along with mappings to popular ontologies such as the W7 – what, when, where, how, who, which, and why – and OPM – open provenance model – models, (2) an information extraction process and a provenance-computation system combining Wikipedia articles’ history with DBpedia information, (3) a set of scripts to make provenance information about DBpedia statements directly available when browsing this source, as well as being publicly exposed in RDF for letting software agents consume it.

Provenance-based reproducibility in the Semantic Web

Reproducibility is a crucial property of data since it allows users to understand and verify how data were derived, and therefore allows them to put their trust in such data. Reproducibility is essential for science, because the reproducibility of experimental results is a tenet of the scientific method, but reproducibility is also beneficial in many other fields, including automated decision making, visualization, and automated data feeds. To achieve the vision of reproducibility, the workflow-based community has strongly advocated the use of provenance as an underpinning mechanism for reproducibility, since a rich representation of provenance allows steps to be reproduced and all intermediary and final results checked and validated. Concurrently, multiple ontology-based representations of provenance have been devised, to be able to describe past computations, uniformly across a variety of technologies. However, such Semantic Web representations of provenance do not have any formal link with execution. Even assuming a faithful and non-malicious environment, how can we claim that an ontology-based representation of provenance enables reproducibility, since it has not been given any execution semantics, and therefore has no formal way of expressing the reproduction of computations? This is the problem that this paper tackles by defining a denotational semantics for the Open Provenance Model, which is referred to as the reproducibility semantics. This semantics is used to implement a reproducibility service, leveraging multiple Semantic Web technologies, and offering a variety of reproducibility approaches, found in the literature. A series of empirical experiments were designed to exhibit the range of reproducibility capabilities of our approach; in particular, we demonstrate the ability to reproduce computations involving multiple technologies, as is commonly found on the Web.

Enhancing Workflow with a Semantic Description of Scientific Intent

Scientists are becoming increasingly dependent upon resources available through the Internet including, for example, datasets and computational modelling services, which are changing the way they conduct their research activities. This paper investigates the use of workflow tools enhanced with semantics to facilitate the design, execution, analysis and interpretation of workflow experiments and exploratory studies. Current workflow technologies do not incorporate any representation of experimental constraints and goals, which we refer to in this paper as scientist's intent. This paper proposes an abstract model of intent based on the Open Provenance Model (OPM) specification. To realise this model a framework based upon a number of Semantic Web technologies has been developed, including the OWL ontology language and the Semantic Web Rule Language (SWRL). Through the use of social simulation case studies the paper illustrates the benefits of using this framework in terms of workflow monitoring, workflow provenance and annotation of experimental results.

Provenance-Based Reproducibility in the Semantic Web

Reproducibility is a crucial property of data since it allows users to understand and verify how data was derived, and therefore allows them to put their trust in such data. Reproducibility is essential for science, because the reproducibility of experimental results is a tenet of the scientific method, but reproducibility is also beneficial in many other fields, including automated decision making, visualization, and automated data feeds. To achieve the vision of reproducibility, the workflow-based community has strongly advocated the use of provenance as an underpinning mechanism for reproducibility, since a rich representation of provenance allows steps to be reproduced and all intermediary and final results checked and validated. Concurrently, multiple ontology-based representations of provenance have been devised, to be able to describe past computations, uniformly across a variety of technologies. However, such Semantic Web representations of provenance do not have any formal link with execution. Even assuming a faithful and non-malicious environment, how can we claim that an ontology-based representation of provenance enables reproducibility, since it has not been given any execution semantics, and therefore has no formal way of expressing the reproduction of computations? This is the problem that this paper tackles by defining a denotational semantics for the Open Provenance Model, which is referred to as the reproducibility semantics. This semantics is used to implement a reproducibility service, leveraging multiple Semantic Web technologies, and offering a variety of reproducibility approaches, found in the literature. A series of empirical experiments were designed to exhibit the range of reproducibility capabilities of our approach; in particular, we demonstrate the ability to reproduce computations involving multiple technologies, as is commonly found on the Web.

Special Section: The third provenance challenge on using the open provenance model for interoperability

The third provenance challenge was organized to evaluate the efficacy of the Open Provenance Model (OPM) in representing and sharing provenance with the goal of improving the specification. A data loading scientific workflow that ingests data files into a relational database for the Pan-STARRS sky survey project was selected as a candidate for collecting provenance. Challenge participants record provenance, run queries over it, and export/import provenance as OPM documents with other teams to verify interoperability. Fourteen teams participated in the challenge that concluded at a workshop in June 2009 in Amsterdam. The experiences of several participating teams are included in this special issue. In this editorial, we describe the challenge in detail, review its outcome, and introduce articles included in this special issue.

Analysis of approaches for supporting the Open Provenance Model: A case study of the Trident workflow workbench

The Trident workbench is a platform for composing, executing and managing scientific workflows. While Trident collects provenance in its native provenance model, the third provenance challenge was an opportunity to build support for the Open Provenance Model into Trident. There are several possible approaches to harmonize our native model with OPM, and such choices are also available to other existing provenance and workflow systems working towards OPM compatibility. We identify and analyze the relative merits of these approaches in an effort to inform practitioners planning to support OPM in their existing provenance/workflow systems. Further, we describe our experience with using the integration approach we choose to interoperate with other teams as part of the challenge.