Provenance Data Model Research Articles

Knowledge-Driven Data Sharing Governance: Developing a data provenance model for constructing context-aware data sharing frameworks for linkage data within the All of Us Research Program.

Health data access and sharing in the U.S. have been hindered by complex privacy and security conditions, limiting research and improvement of health outcomes. This challenge is even greater in minority and sensitive populations - who would benefit most from integrative research. The All of Us Research Program is a National Institutes of Health funded initiative aiming to enhance health research through the analysis of lifestyle, environment, and genetics data from over one million participants, with a focus on communities historically underrepresented in biomedical research. The program is enhancing the utility of data by incorporating passive data streams, such as claims, electronic health records (EHRs), environmental, geocodes, mortality, and residential history. Further, through the use of national health information exchange standards, All of Us also aims to address technical challenges in linking EHRs from health information networks to deliver a more complete longitudinal participant record. However, such an initiative necessitates a thoughtful approach through data awareness, advancing data access in ways that preserve individual privacy while increasing scientific utility. Here, we describe our privacy-preserving record linkage (PPRL) and data privacy risk disclosure control strategy, emphasizing fit-for-purpose data provisioning. Our knowledge-driven data ecosystem architecture leverages a combination of PPRL, data provenance, and geocoding at multiple levels to support context-aware data sharing granular access control.

Prov-Dominoes: An approach for knowledge discovery from provenance data

Provenance has become increasingly relevant to understanding, auditing, and reproducing computational tasks. The provenance analysis processes can often be overwhelming to the user due to the large volume of data, the multiple relationships among data, and the implicit information buried in the data. Existing provenance analysis tools use either visual exploration (which is overwhelming for large provenance graphs) or do not support the exploration of implicit provenance data, such as the inferences of the PROV Data Model Constraints. To fill in this gap, we introduce Prov-Dominoes, a tool designed to interactively enable knowledge discovery on provenance data. Prov-Dominoes promotes the provenance relationships among entities, activities, and agents into first-class elements represented by domino tiles. It allows users to combine and compose such domino tiles visually and interactively, using GPU. The benefits of Prov-Dominoes are three-fold: first, it uses matrices to display provenance data, which is more compact than graphs; second, it allows users to easily explore implicit information; third, it is capable of efficiently processing large datasets using GPUs. We evaluated Prov-Dominoes over distinct case studies, allowing the observation of Prov-Dominoes in action. We also evaluated the performance of sequential combinations executed in Prov-Dominoes when dealing with provenance data with thousands of relations, contrasting their executions in GPU and CPU. The results showed that, for a large dataset, GPU was more than a hundred times faster than CPU.

Blockchain-based digital twin data provenance for predictive asset management in building facilities

Purpose The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.Design/methodology/approach Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.Findings Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.Practical implications The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.Originality/value The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

Framework for Data Provenance Assurance in Cloud Environment using Ethereum Blockchain

Ensuring secure data provenance is crucial for maintaining accountability and confidentiality in cloud environments. Cloud data provenance involves recording the history of creation and operations performed on cloud data objects. However, establishing trust between cloud customers and service providers remains a challenge, highlighting the need for assured data provenance models in cloud storage. Blockchain technology has emerged as a solution for designing data provenance assurance mechanisms. It provides a decentralized and distributed ledger to record the provenance of digital assets. In this context, we present a blockchain-based framework for ensuring data provenance in cloud storage. Initially, we develop a cloud storage application using OpenStack swift storage. This application caters to the storage needs of university students and faculty while providing data provenance capabilities. Subsequently, we design a data provenance assurance framework for confidential files of users using the Ethereum blockchain. To evaluate the scalability and performance of the proposed framework, we analyze various factors such as transaction throughput, latency, network size, and load on the blockchain network. The performance of the system is compared under two consensus algorithms: Proof of Work and Proof of Authority. By conducting this analysis, we aim to assess the effectiveness and efficiency of the blockchain-based solution in ensuring data provenance in cloud storage environments.

Provenance Data Management in Health Information Systems: A Systematic Literature Review.

This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.

Collaborative Metadata Definition using Controlled Vocabularies, and Ontologies

Collaborative Metadata Definition using Controlled Vocabularies, and Ontologies

Blockchain supported BIM data provenance for construction projects

The management of construction projects requires adequate techniques to support the continual exchange of information across disciplines. Recent advances in Building Information Modelling (BIM) have exposed new ways for process and data integration with open data formats, process mapping, and terminology. In construction projects where multiple disciplines produce and share BIM data, mechanisms for defining information priority, provenance and suitability become necessary in order to have consistent and traceable use of data. This includes objects or collection of attributes for data objects that are associated with a discipline or organisation including clear identification of the transaction that has introduced the information. Blockchain can be used to record metadata of BIM objects such as the issuing discipline, object version and responsibility/ liability associated with the data. Blockchain can offer the capability to apply levels of “trust” to individual BIM objects and a more secured framework of collaboration across stakeholders.This paper proposes a Blockchain-based BIM data provenance model to support information exchange in construction projects. By testing the solution in a real-world bridge construction scenario, it has been shown that the approach can recognise the levels of competence and can improve the process of BIM implementation. The proposed approach gives stakeholders more confidence when sharing their BIM data, reduces costs, and improves risk contingencies in construction projects. The paper provides a cost analysis to evidence the implications of using Blockchain for BIM data provenance through an experimental framework supported by an Ethereum public test network. A front-end web page has also been created to facilitate interaction with smart contracts and to monitor the BIM data provenance process.

The Common Provenance Model: Capturing Distributed Provenance in Life Sciences Processes.

The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.

Relating simulation studies by provenance—Developing a family of Wnt signaling models

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

An FPGA Implementation of Privacy Preserving Data Provenance Model Based on PUF for Secure Internet of Things

Data provenance to maintain data integrity and authenticity is a significant challenge in the Internet of Things (IoT) environments. Data provenance protocols must communicate provenance metadata while preserving its privacy. This enables trust in IoT systems expanding its acceptance within society. In this paper, we present a scheme to combine data provenance and privacy-preserving solutions. Our scheme utilizes Physical Unclonable Functions (PUFs) with non-interactive zero knowledge proof to provide trustworthy and dependable IoT systems. An IoT device can anonymously send data to the corresponding server associated with the proof of ownership. We propose a privacy-preserving data provenance protocol. This protocol was synthesized with Altera Quartus. It was implemented on an Altera Cyclone IV FPGA to demonstrate its practicality and feasibility. Most of the protocol steps take time of the order of 40 $$\upmu$$ s establishing its practicality.

Usage of Data Provenance Models in Collaborative Multidisciplinary Aero-Engine Design

AbstractThe collaborative multidisciplinary design of aircraft engines is a complex and highly iterative process. An essential characteristic of this design process is the involvement of a large number of experts from different disciplines, as well as the usage of numerous tools and workflows. Large amounts of data are produced and need to be exchanged via a multitude of interfaces. Furthermore, the data undergo various transformations in the course of the design process. Understanding where a certain piece of data originates from and how it is connected to other datasets becomes therefore progressively essential. The purpose of this paper is to present a methodology to apply data provenance models in collaborative multidisciplinary aero-engine design, supported by an approach for data standardization and identification. Besides the methodology, the software implementation to support this approach is presented in detail, including automated capturing and storage of provenance data, as well as methods for data investigation. In addition, the presented methodology is evaluated by means of practical examples from the field of preliminary aero-engine design.

A framework for evaluating ontology meta-matching approaches

Ontology matching has become a key issue to solve problems of semantic heterogeneity. Several researchers propose diverse techniques that can be used in distinct scenarios. Ontology meta-matching approaches are a specialization of ontology matching and have achieved good results in pairs of ontologies with different types of heterogeneities. However, developing a new ontology meta-matcher can be a costly process and a lot of experiments are often carried out to analyze the behavior of the matcher. This article presents a modularized framework that covers the main stages of the ontology meta-matching evaluation process. This framework aims to aid researchers to develop and analyze algorithms for ontology meta-matching, mainly metaheuristic-based supervised and unsupervised approaches. As the main contribution of the research, the framework proposed will facilitate the evaluation of ontology meta-matching approaches and, as the secondary contribution, a data provenance model that captures the main information generated and consumed throughout experiments is presented in the framework.

A Proposed Approach for Provenance Data Gathering

Data provenance focuses on the source of the data and on the identification of data sources and their transformations undergone over time. This paper proposes a generic method for collecting provenance data, and is a follow-up of a study carried out by the same authors in a Brazilian hemotherapy center. This method is based on the W3C’s Provenance Data Model (PROV-DM), and proposes a way to capture, store and analyze anemia-index provenance data by applying a scientific workflow, together with the management of provenance of knowledge. This is an exploratory, practical and deductive study carried out with real data from 197,551 candidates for blood donors, extracted from reports ranging from 2000 to 2018 provided by a Brazilian hemotherapy center. People identified with high anemia rates were quantified and tagged as not-suitable for blood donations. The inadequate candidates were quantified with the highest rate of anemia, and out of 1011 male candidates and 4039 female candidates, women had the highest levels of inadequate blood donations. At the end of this study, it can be concluded that the generic method for collecting data provenance proposed here can be applied in several areas of knowledge.

Abstracting PROV provenance graphs: A validity-preserving approach

Data provenance is a structured form of metadata designed to record the activities and datasets involved in data production, as well as their dependency relationships. The PROV data model, released by the W3C in 2013, defines a schema and constraints that together provide a structural and semantic foundation for provenance. This enables the interoperable exchange of provenance between data producers and consumers. When the provenance content is sensitive and subject to disclosure restrictions, however, a way of hiding parts of the provenance in a principled way before communicating it to certain parties is required. In this paper we present a provenance abstraction operator that achieves this goal. It maps a graphical representation of a PROV document PG1 to a new abstract version PG2, ensuring that (i) PG2 is a valid PROV graph, and (ii) the dependencies that appear in PG2 are justified by those that appear in PG1. These two properties ensure that further abstraction of abstract PROV graphs is possible. A guiding principle of the work is that of minimum damage: the resultant graph is altered as little as possible, while ensuring that the two properties are maintained. The operator developed is implemented as part of a user tool, described in a separate paper, that lets owners of sensitive provenance information control the abstraction by specifying an abstraction policy.

BlockTrack-L: A Lightweight Blockchain-based Provenance Message Tracking in IoT

Data tracking is of great significance and a central part in digital forensics. In today's complex network design, Internet of Things (IoT) devices communicate with each other and require strong security mechanisms. In maintaining an audit trail of IoT devices or provenance of IoT device data, it is important to know the origins of requests to ensure certain level of trust in IoT data. Blockchain can provide traceability of records generated from IoT devices in a sensitive environment. In this paper, we present an application layer data provenance model that works on execute-order architecture for cloud based IoT networks. It supports high throughput of transactions on the blockchain network with lightweight security overhead by using outsourced encryption on edge nodes. All communications among the IoT devices are connected to a blockchain network and stored on permissioned blockchain peers. The proposed system is evaluated to have less cryptographic load by offloading the IoT nodes with Edge nodes.

A Big Data Provenance Model for Data Security Supervision Based on PROV-DM Model

Nowadays, big data has become a hot research topic. It gives fresh impetus to the economic and social development. However, the huge value of big data also makes it the focus of attacks. Big data security incidents occur frequently in recent years. The security supervision capacities for big data do not match its important role. Data provenance which describes the origins of data and the process by which it arrived the current state, is an effective approach for data supervision. For the full use of provenance in big data supervision, a provenance model which defines the concepts used to represent the provenance types and relations is required to be built in advance, but current provenance models do not adapt to big data scenarios well. In this paper, we comprehensively consider the characteristics of big data and the requirements of data security supervision, extend the widely used provenance model PROV-DM by subtyping and new relation definition, and propose a big data provenance model (BDPM) for data supervision. BDPM model supports the provenance representation of various data types and diverse data processing modes to represent the entire data transformation process through different components in the big data system, and defines new relations to enrich provenance analysis functions. Based on BDPM model, we introduce the constraints that should be satisfied in the construction of valid provenance graph and present the data security supervision methods via provenance graph analysis. Finally, we evaluated the satisfiability of BDPM model through a case study.

An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance

The future of healthcare may look completely different from the current clinic-center services. Rapidly growing and developing technologies are expected to change clinics throughout the world. However, the healthcare delivered to impaired patients, such as elderly and disabled people, possibly still requires hands-on human expertise. The aim of this study is to propose a predictive model that pre-diagnose illnesses by analyzing symptoms that are interactively taken from patients via several hand gestures during a period of time. This is particularly helpful in assisting clinicians and doctors to gain better understanding and make more accurate decisions about future plans for their patients’ situations. The hand gestures are detected, the time of the gesture is recorded and then they are associated to their designated symptoms. This information is captured in the form of provenance graphs constructed based on the W3C PROV data model. The provenance graph is analyzed by extracting several network metrics and then supervised machine-learning algorithms are used to build a predictive model. The model is used to predict diseases from the symptoms with a maximum accuracy of 84.5%.

Security of Internet of Things using Provenance Data Model with Blockchain

Internet of Things has emerged as an area of high potential due to the confluence of multiple technologies and is having a great impact on consumer as well as industrial applications. The amount of IoT devices is estimated to be around 51 billion by 2020. Owing to the pervasive presence of IoT device, concerns have been raised on its privacy and security aspects. IoT devices have limited storage and computing ability leading to limitations in securing the devices and thus are more vulnerable to attacks. These limited resources hinder the ability to diagnose the incorrect behaviour of the devices leading to failure in detecting the root cause of the attacks. In this paper, we present a decentralized provenance data model, which generates data provenance providing the complete information of system activities of work-flows. Provenance graph and policy storage using block-chain is implemented to recreate the causal relationships in IoT defined areas. The existing provenance analysis is centralized and less secure therefore, we have proposed a decentralized architecture to increase the security. We have evaluated and compared the performance of decentralized provenance analysis in IoT devices with the normal system.

Bioinformatics Workflows With NoSQL Database in Cloud Computing.

Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

Trust evaluation of multimedia documents based on extended provenance model in social semantic web

Recently, the importance of social semantic web, which is a combination of the semantic web and the social web, has been increasing with active data creation and sharing through the Web. In this paper, we proposes a provenance based trust evaluation scheme of multimedia documents by extending the PROV data model in social semantic web. The proposed scheme extends the PROV data model of W3C to manage the provenance and evaluate the trust of multimedia documents in a social semantic web environment. The trust of multimedia documents is evaluated by considering the agent trust, source document trust, and reputation score of current document. The evaluated trust is managed as provenance information, and when users request a query, the query results are generated by considering trust. To verify the validity of the proposed scheme, the trust is compared and evaluated using SPARQL queries.