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Aiming at the problem of high false negative rate in container escape detection technologies, a real-time detecting method of heterogeneous observation was proposed.Firstly, the container escape behavior utilizing kernel vulnerabilities was modeled, and the critical attributes of the process were selected as observation points.A heterogeneous observation method was proposed with “privilege escalation” as the detection criterion.Secondly, the kernel module was adopted to capture the attribute information of the process in real time, and the process provenance graph was constructed.The scale of the provenance graph was reduced through container boundary identification technology.Finally, a heterogeneous observation chain was built based on the process attribute information, and the prototype system HOC-Detector was implemented.The experiments show that HOC-Detector can successfully detect all container escapes using kernel vulnerabilities in the test dataset, and the increased runtime overhead is less than 0.8%.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Providing 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. [ABSTRACT FROM AUTHOR]

Copyright of Revista Eletrônica de Sistemas de Informação is the property of Revista Electronica de Sistemas de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and Schema.org to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain. [ABSTRACT FROM AUTHOR]

As blockchain technology continues to mature, its potential for use in workflow systems has garnered considerable attention. However, the adoption of blockchain-based workflow systems in practical contexts presents a range of challenges that need to be addressed. Among the challenges encountered in the context of workflow systems, one particularly significant hurdle relates to establishing trust among the various actors participating in the workflow. To address this concern, we introduce a workflow architecture that incorporates provenance support and decentralized identifiers. This strategic combination aids in identifying what actions by whom led to the creation of the data being passed on within the workflow. The addition of provenance support within our framework results in a notable enhancement of traceability, accountability, and transparency throughout the workflow processes, which ultimately improves the overall trustworthiness of the system. The key novelty of this study can be found at the provenance-enabled blockchain workflow architecture that remains unexplored until now. The proposed solution is supported by a realistic use-case scenario portraying a used-car business which has undergone testing and measurements benchmarked against comparable frameworks and solutions. [ABSTRACT FROM AUTHOR]

Given the significant increase of using provenance, this paper presented a novel security provenance model based on open provenance model to ensure the integrity and confidentiality of provenance. In the study of confidentiality, it used an improved Diffie-Hellman key agreement to negotiate the session key to encrypt the sensitive information. In the study of integrity, it described the derived relations as a set of triples. It expanded the concept of signature-based checksum to make it available to the application of provenance graph. Finally it described the algorithm of integrity verification in the form of Pseudo-code. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

The capitalisation on and transfer of technological, engineering and scientific knowledge associated with empirical know-how is an important issue for the sustainability and development of manufacturing. Indeed, certain sectors of industry are facing the increasing ageing of the labour force, recruitment difficulties and high staff turnover, leading to a loss of knowledge and know-how. In a context of numerical and digital transition and the migration of processes to industry 4.0, one of major challenges manufacturers face today is their capacity to build intelligent platforms for acquiring, storing and transferring their know-how and knowledge. It is crucial to create new media and tools for staff training and development capable of capturing knowledge and reusing it to create a project history through expertise and data collection. This paper presents the methodology and guidelines for implementing electronic knowledge books (eK-Books), along with their uses. The eK-Book is a semantic web-based hypertext medium (channel) allowing stakeholders to capitalise on, structure and transfer knowledge by using concept maps, process maps, influence graphs, downloadable documents, web pages and hypermedia knowledge sheets. They are intended for engineers, expert or novice technicians, manufacturers, sector coordinators and plant managers, as well as trainers and learners. They are usable and manageable in all types of environments and with different levels of accessibility. This paper highlights (1) the transfer knowledge capacity of eK-Books and (2) their usability in two agri-food sectors namely (1) the cheese sector with protected designation of origin (PDO) and protected geographical indication (PGI), and (2) the butchery and cold meat sectors. [ABSTRACT FROM AUTHOR]

Knowledge Graphs (KGs) present factual information about domains of interest. They are used in a wide variety of applications and in different domains, serving as powerful backbones for organizing and extracting knowledge from complex data. In both industry and academia, a variety of platforms have been proposed for managing Knowledge Graphs. To use the full potential of KGs within these platforms, it is essential to have proper provenance management to understand where certain information in a KG stems from. This plays an important role in increasing trust and supporting open science principles. It enables reproducibility and updatability of KGs. In this paper, we propose a framework for provenance management of KG generation within a web portal. We present how our framework captures, stores, and retrieves provenance information. Our provenance representation is aligned with the standardized W3C Provenance Ontology. Through our framework, we can rerun the KG generation process over the same or different source data. With this, we support four applications: reproducibility, altered rerun, undo operation, and provenance retrieval. In summary, our framework aligns with the core principles of open science. By promoting transparency and reproducibility, it enhances the reliability and trustworthiness of research outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As data collected and provided by Internet of Things (IoT) devices power an ever-growing number of applications and services, it is crucial that this data can be trusted. Data provenance solutions combined with blockchain technology are one way to make data more trustworthy by providing tamper-proof information about the origin and history of data records. However, current blockchain-based solutions for data provenance fail to take the heterogeneous nature of IoT applications and their data into account. In this work, we identify functional and non-functional requirements for a secure and extensible IoT data provenance framework, and conceptualise the framework as a layered architecture. Evaluating the framework using a proof-of-concept implementation based on Ethereum smart contracts, we conclude that our framework can be used to realise data provenance concepts for a wide range of IoT use cases. While blockchain technology generally poses constraints on scalability and privacy, we discuss multiple solutions aiming to overcome these issues. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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, this monograph contends that provenance can and should reliably be tracked and exploited on the Web, and investigates the necessary foundations to achieve such a vision. Multiple data sources have been used to compile the largest bibliographical database on provenance so far. This large corpus permits the analysis of emerging trends in the research community. Specifically, the CiteSpace tool identifies clusters of papers that constitute research fronts, from which characteristics are extracted to structure a foundational framework for provenance on the Web. 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 such a vision, it is necessary to provide a definition of provenance that applies to the Web context. A 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, this definition is refined as a query over assertions made by applications. Given that the majority of work on provenance has been undertaken by the database, workflow and e-science communities, some of their work is reviewed, contrasting approaches, and focusing on important topics believed 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, it is meant 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. For this purpose, this monograph puts forward the Open Provenance Vision, which is an approach that consists of controlled vocabulary, serialisation formats and interfaces to allow the provenance of individual systems to be expressed, connected in a coherent fashion, and queried seamlessly. In this context, the Open Provenance Model is an emerging community-driven representation of provenance, which has been actively used by some 20 teams to exchange provenance information, in line with the Open Provenance Vision. After identifying an open approach and a model for provenance, techniques to expose provenance over the Web are investigated. In particular, Semantic Web technologies are discussed since they have been successfully exploited to express, query and reason over provenance. Symmetrically, Semantic Web technologies such as RDF, underpinning the Linked Data effort, are analysed since they offer their own difficulties with respect to provenance. A powerful argument for provenance is that it can help make systems transparent, so that it becomes possible to determine whether a particular use of information is appropriate under a set of rules. Such capability helps make systems and information accountable. To offer accountability, provenance itself must be authentic, and rely on security approaches, which are described in the monograph. This is then followed by systems where provenance is the basis of an auditing mechanism to check past processes against rules or regulations. In practice, not all users want to check and audit provenance, instead, they may rely on measures of quality or trust; hence, emerging provenance-based approaches to compute trust and quality of data are reviewed. [ABSTRACT FROM AUTHOR]

Provenance information corresponds to essential metadata that describes the entities, users, and processes involved in the history and evolution of a data object. The benefits of tracking provenance information have been widely understood in a variety of domains; however, only recently have provenance solutions gained interest in the security community. Indeed, on the one hand, provenance allows for a reliable historical analysis enabling security-related applications such as forensic analysis and attribution of malicious activity. On the other hand, the unprecedented changes in the threat landscape place demands for securing provenance information to facilitate its trustworthiness. With the recent growth of provenance studies in security, in this work we examine the role of data provenance in security and privacy. To set this work in context, we outline fundamental principles and models of data provenance and explore how the existing studies achieve security principles. We further review the existing schemes for securing data provenance collection and manipulation known as secure provenance and the role of data provenance for security and privacy, which we refer to as threat provenance. [ABSTRACT FROM AUTHOR]

Game Provenance has been proposed and employed for Game Analytics tasks as they capture game session data in detail and allow exploratory analysis and visualizations. Games are highly heterogeneous models with several interacting agents and game-world environment elements. Game Provenance Graphs can accommodate the heterogeneous nature of such applications with different types of nodes and edges that tend to share information across themselves, enhancing cause-effect features rarely addressed by any other approach. On the other hand, existing Heterogeneous Graph Neural Network (HGNN) solutions disregard node feature information, overlooking shared features across distinct node types, and rely on naïve approaches, such as projecting each type of node to the same n-dimensional space. We conjecture that leveraging heterogeneous feature information is essential for tackling Game Analytics tasks, especially through Machine Learning based models. To achieve that, we propose a novel approach that allows HGNNs to leverage Game Provenance Graphs' heterogeneous node feature information. Hence, we introduce in this paper three strategies for Heterogeneous Graph Representation Learning that encodes feature set information into the HGNN architecture and projects feature values leveraging similarities across such feature sets. We conduct experiments on two Game Provenance Graphs datasets, the Smoke Squadron and the Game Provenance Profile datasets, which gather game session data from different games. Our results show that encoding feature set information in the representation learning process improves the outcomes of GNN models in non-disjoint feature datasets. [ABSTRACT FROM AUTHOR]

Information management is Silos of the impending conditions across the sectors. Predominantly in the case of the clinical trials management, one of the critical challenges is about the information silos among the various stakeholder's integral to the process. Processing the information over real-time and ensuring there is a holistic system in place shall help in improving the quality of collaboration and integration. The objective of this research article is to assess the feasibility of blockchain implementation for collaborative clinical trial auditing and to propose a blockchain-based solution that can help in improving the quality of solutions structure practised, leading to sustainable ways of handling the clinical trial auditing. The proposed model develops grid-based blocks for each of the phases of clinical trials, forming a comprehensive block network to be shared among the consortium handling the data. The proposed framework has a significant scope of security enhancement and customization that can help in shaping the systematic improvement to the information systems of clinical trials. The research methodology adopts the blockchain framework, which has been discussed in the paper. The consensus protocol used is Practical Byzantine Fault Tolerance (PBFT). The experimental study was conducted using simulation to verify the performance with regard to different performance metrics and attacks. The results showed that the proposed blockchain framework provides improved transaction throughput, reduced latency, and enhanced scalability, as compared to other existing models. [ABSTRACT FROM AUTHOR]

Background: This paper proposes Cyrus, a new transparency evaluation framework, for Open Knowledge Extraction (OKE) systems. Cyrus is based on the state-of-the-art transparency models and linked data quality assessment dimensions. It brings together a comprehensive view of transparency dimensions for OKE systems. The Cyrus framework is used to evaluate the transparency of three linked datasets, which are built from the same corpus by three state-of-the-art OKE systems. The evaluation is automatically performed using a combination of three state-of-the-art FAIRness (Findability, Accessibility, Interoperability, Reusability) assessment tools and a linked data quality evaluation framework, called Luzzu. This evaluation includes six Cyrus data transparency dimensions for which existing assessment tools could be identified. OKE systems extract structured knowledge from unstructured or semi-structured text in the form of linked data. These systems are fundamental components of advanced knowledge services. However, due to the lack of a transparency framework for OKE, most OKE systems are not transparent. This means that their processes and outcomes are not understandable and interpretable. A comprehensive framework sheds light on different aspects of transparency, allows comparison between the transparency of different systems by supporting the development of transparency scores, gives insight into the transparency weaknesses of the system, and ways to improve them. Automatic transparency evaluation helps with scalability and facilitates transparency assessment. The transparency problem has been identified as critical by the European Union Trustworthy Artificial Intelligence (AI) guidelines. In this paper, Cyrus provides the first comprehensive view of transparency dimensions for OKE systems by merging the perspectives of the FAccT (Fairness, Accountability, and Transparency), FAIR, and linked data quality research communities. Results: In Cyrus, data transparency includes ten dimensions which are grouped in two categories. In this paper, six of these dimensions, i.e., provenance, interpretability, understandability, licensing, availability, interlinking have been evaluated automatically for three state-of-the-art OKE systems, using the state-of-the-art metrics and tools. Covid-on-the-Web is identified to have the highest mean transparency. Conclusions: This is the first research to study the transparency of OKE systems that provides a comprehensive set of transparency dimensions spanning ethics, trustworthy AI, and data quality approaches to transparency. It also demonstrates how to perform automated transparency evaluation that combines existing FAIRness and linked data quality assessment tools for the first time. We show that state-of-the-art OKE systems vary in the transparency of the linked data generated and that these differences can be automatically quantified leading to potential applications in trustworthy AI, compliance, data protection, data governance, and future OKE system design and testing. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) models are the most important instruments to predict future weather. Provenance information is of central importance for detecting unexpected events that may develop during the long course of model execution. Besides, the need to share scientific data and results between researchers also highlights the importance of data quality and reliability. The weather research and forecasting (WRF) Model is an open‐source NWP model. In this study, we propose a methodology for tracking the WRF model and for generating, storing, and analyzing provenance. We implement the proposed methodology—with a machine learning‐based parser, which utilizes classification algorithms to extract provenance information. The proposed approach enables easy management and understanding of numerical weather forecast workflows by providing provenance graphs. By analyzing these graphs, potential faulty situations that may occur during the execution of WRF can be traced to their root causes. Our proposed approach has been evaluated and has been shown to perform well even in a high‐frequency provenance information flow. [ABSTRACT FROM AUTHOR]

In the "14th Five-Year Plan" era, the digitization of the financial industry has entered a phase of deepening and high-quality development. Strengthening Financial Data Security Governance (FDSG) and protecting financial data security have become an objective need and essential requirements for developing national economic security. FDSG utilizes data governance measures as a fundamental tool with a focus on sensitive data to ensure the comprehensive security of the entire lifecycle of financial data. It aims to promote data circulation among financial institutions, activate data value, and facilitate market-oriented allocation of financial data elements. FDSG is increasingly intertwined with big data, artificial intelligence, cloud computing, and blockchain technologies, transforming traditional data security governance into intelligent governance and accelerating the evolution of FDSG towards automation, intelligence, efficiency, and precision. FDSG's essence, scope, and governance framework were introduced, and the core concepts and critical supporting technologies for the intellectual development of FDSG were elaborated, outlining the roadmap for intelligent governance. With consideration of FDSG's demands and characteristics of intelligent technologies, vital directions for the practical application of intelligent technologies in FDSG were outlined, including data classification, data traceability, content control, privacy protection, and data twinning. Examples of current intelligent governance practices in banking, securities, and insurance industries were provided. Policy recommendations were proposed in the paper to promote the standardized and intelligent development of FDSG in China's new era, aiming to foster the sustainable development of financial institutions and industries and ensure a healthy and secure environment for the future. [ABSTRACT FROM AUTHOR]

Clinical trials are a multi-billion-dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [7] and ISO 27789 [40]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance. [ABSTRACT FROM AUTHOR]

Aims: 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. Method: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, systems that can actively adapt to their surroundings and cooperate with other systems have become increasingly important. One of the most important factors to consider during the process of cooperation among systems is trust. Trust is a social concept that assumes that cooperation with an object will produce positive results in the direction we intend. Our objectives are to propose a method for defining trust during the requirements engineering phase in the process of developing self-adaptive systems and to define the trust evidence models required to evaluate the defined trust at runtime. To achieve this objective, we propose in this study a provenance-based trust-aware requirement engineering framework for self-adaptive systems. The framework helps system engineers derive the user's requirements as a trust-aware goal model through analysis of the trust concept in the requirements engineering process. We also propose a provenance-based trust evidence model to evaluate trust and provide a method for defining this model for the target domain. Through the proposed framework, a system engineer can treat trust as a factor emerging from the requirements engineering phase for the self-adaptive system and understand the factors affecting trust using the standardized format. [ABSTRACT FROM AUTHOR]

In this paper, we present an architecture for time-constrained ontology evolution comprised of two tools: the J2OIM (JSON to Ontology Instance Mapper), which uses JavaScript Object Notation (JSON) objects to populate an ontology, and TICO (Time Constrained instance-guided Ontology evolution), which analyses streams or batches of instances as they are generated and attempts to identify potential changes to their definitions that may trigger evolutionary processes. These tools help compensate for identified gaps in literature in instance mapping and modular versioning. The case-study for these tools involves a predictive maintenance (PdM) scenario in which near real-time data sensor enriched by contextual data is continuously transformed into ontology individuals that trigger ontology evolution mechanisms. Results show it is possible to use the instance mapping mechanisms in an incremental fashion while assuring no duplicates are generated and the aggregation of similar information from distinct data points into intervals. Furthermore, they show how the ontology evolution processes effectively detect variations in ontology individuals, generating and updating existing concepts and roles. [ABSTRACT FROM AUTHOR]

The automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Data-intensive experiments using workflows enabled automation and provenance support, which contribute to alleviating the reproducibility crisis. This paper investigates the existing provenance models as well as scientific workflow applications. Furthermore, here we not only summarize the models at different levels, but also compare the applications, particularly the blockchain applied to the provenance in scientific workflows. After that, a new design of secure provenance system is proposed. Provenance that would be enabled by the emerging technology is also discussed at the end. [ABSTRACT FROM AUTHOR]

Provenanc 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. [ABSTRACT FROM AUTHOR]

Aiming at the problem of high false negative rate in container escape detection technologies, a real-time detecting method of heterogeneous observation was proposed. Firstly, the container escape behavior utilizing kernel vulnerabilities was modeled, and the critical attributes of the process were selected as observation points. A heterogeneous observation method was proposed with "privilege escalation" as the detection criterion. Secondly, the kernel module was adopted to capture the attribute information of the process in real time, and the process provenance graph was constructed. The scale of the provenance graph was reduced through container boundary identification technology. Finally, a heterogeneous observation chain was built based on the process attribute information, and the prototype system HOC-Detector was implemented. The experiments show that HOC-Detector can successfully detect all container escapes using kernel vulnerabilities in the test dataset, and the increased runtime overhead is less than 0.8%. [ABSTRACT FROM AUTHOR]

In scientific collaboration, data sharing, the exchange of ideas and results are essential to knowledge construction and the development of science. Hence, we must guarantee interoperability, privacy, traceability (reinforcing transparency), and trust. Provenance has been widely recognized for providing a history of the steps taken in scientific experiments. Consequently, we must support traceability, assisting in scientific results’ reproducibility. One of the technologies that can enhance trust in collaborative scientific experimentation is blockchain. This work proposes an architecture, named BlockFlow, based on blockchain, provenance, and cloud infrastructure to bring trust and traceability in the execution of collaborative scientific experiments. The proposed architecture is implemented on Hyperledger, and a scenario about the genomic sequencing of the SARS-CoV-2 coronavirus is used to evaluate the architecture, discussing the benefits of providing traceability and trust in collaborative scientific experimentation. Furthermore, the architecture addresses the heterogeneity of shared data, facilitating interpretation by geographically distributed researchers and analysis of such data. Through a blockchain-based architecture that provides support on provenance and blockchain, we can enhance data sharing, traceability, and trust in collaborative scientific experiments. [ABSTRACT FROM AUTHOR]

Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g., due to the cost and effort required, or to protect their interests. In response, this article introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents, acting on behalf of clients, rank and select service providers according to reputation, while provider agents, acting on behalf of service providers, learn from the environment and adjust provider's circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model. [ABSTRACT FROM AUTHOR]

Colouring Australia is a digital platform for collecting and visualising building level information across several Australian cities. It provides a valuable resource for bringing together data on building age, material, sustainability ratings, walkability and other key metrics as we plan for net zero cities. Colouring Australia comprises part of the international Colouring Cities Research Programme, which supports the development of open-source platforms that provide open data on national building stocks. In this paper we outline the technical architecture of the platform, and the development and visualisation of a building level walkability metric based on pedestrian access to destinations. This platform provides a useful digital tool for planners to understand which parts of the city are walkable and in turn this can support future active transport programs and policies. Future research will be to validate this novel walkability index through a series of stakeholder and public workshops using the Colouring Australia platform in an interactive tabletop environment where usability testing can be undertaken. [ABSTRACT FROM AUTHOR]

Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline — starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain. [ABSTRACT FROM AUTHOR]

Summary: Data provenance has led to a developing need for the technologies to empower end‐users to assess and take action on the data life cycle. In the Big Data era, companies' amount of data over the world increases each day. As data increases, metadata on the data origin and lifecycle of data also overgrows. Thus, this requires innovations that can provide a better understanding and interpretation of data using data provenance. This study addresses the challenge of extracting data in the form of graphs from scientific workflows and facilitating demanded visualization approaches such as graph comparison, summarization, backward‐forward querying, and stream data visualization. W3C‐PROV‐O provenance specification is implemented via a visualization tool to assess the applicability of proposed algorithms. The proposed algorithms are tested on a large‐scale provenance dataset to explore their performance. In addition, this study discusses the details of a comprehensive usability study of the prototype visualization tool. Results indicate that proposed visualization approaches are usable and processing overhead is insignificant. [ABSTRACT FROM AUTHOR]