Retrospective Provenance Research Articles

Storied Past, Bright Future: A Provenance Jam Session

AbstractThis panel will explore provenance of the past, present, and future in information science and technology, as both a concept and professional and intellectual value. The OED defines “provenance” as origin, source, ownership of an artwork, or guidance to determine authenticity. Provenance today is not limited to history domains. It can be used to describe what did happen (retrospective provenance), what will happen (prospective provenance), and what could happen (subjunctive provenance). Provenance transcends disciplines, and this panel is intended for anyone who is interested in the world of provenance. Perhaps echoing life in general, provenance is more about the journey than the destination. The goal of the panel is not to find an authoritative answer to why we choose one method or theory of provenance over another. Rather, this “jam session” will empower participants‐ panelists, moderators, and audiences alike‐ to frame and answer provenance questions for their own work.

Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation

BackgroundElectronic Laboratory Notebooks (ELNs) are used to document experiments and investigations in the wet-lab. Protocols in ELNs contain a detailed description of the conducted steps including the necessary information to understand the procedure and the raised research data as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create semantic documentation of the provenance of research data by the use of ontologies and linked data methodologies.MethodsBased on an ELN protocol of a biomedical wet-lab experiment, a retrospective provenance model of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment’s description such as headings, tables, and links, to translate the ELN protocol into a semantic knowledge representation. To satisfy the Findable, Accessible, Interoperable, and Reuseable (FAIR) guiding principles, a ready-to-publish bundle is created that contains the research data together with their semantic documentation.ResultsWhile the manual modelling efforts serve as proof of concept by employing one protocol, the automated structure-based approach demonstrates the potential generalisation with seven ELN protocols. For each of those protocols, a ready-to-publish bundle is created and, by employing the SPARQL query language, it is illustrated that questions about the processes and the obtained research data can be answered.ConclusionsThe semantic documentation of research data obtained from the ELN protocols allows for the representation of the retrospective provenance of research data in a machine-interpretable way. Research Object Crate (RO-Crate) bundles including these models enable researchers to easily share the research data including the corresponding documentation, but also to search and relate the experiment to each other.

Capturing and Analyzing Provenance from Spark-based Scientific Workflows with SAMbA-RaP

Several scientists have moved their IO- and CPU-intensive workflows to Data-Intensive Scalable Computing (DISC) frameworks aiming at benefit from high scalability, broad support, and manufacturers’ infrastructure. A prominent framework is Apache Spark, which has been on an absolute tear over the last ten years and became one of the most widely used technologies in big data. Apache Spark brings several advantages along, as granting very efficient in-memory data management for large-scale applications through Resilient Distributed Datasets (RDDs). Such an in-memory replacement for MapReduce enables data handling activities of scientific workflows to be executed orders of magnitude faster in comparison to other DISC environments. A major drawback, however, is Apache Spark still lacks support for both data tracking and workflow provenance. Accordingly, the sole alternative for users that rely on provenance features is to spend countless hours collecting data from log files. Moreover, as one additional challenge, Apache Spark interprets legacy programs within workflows as “black-box” activities, which prevents the capture and analysis of data movements through RDDs. This manuscript presents the SAMbA-RaP (Spark provenAnce MAnagement with Reports and Presentation) solution for capturing, storing, and querying prospective and retrospective provenance, as well as domain data within distributed scientific workflows. SAMbA-RaP performance was evaluated upon real workflow cases (SciPhy, Montage, WordCount, BuzzFlow, and SalesForecasts) from distinct domains, e.g., literature, bioinformatics and astronomy, and results indicate the average imposed overhead for managing provenance data is acceptable. Moreover, experiments also indicate our solution is capable of handling workflows with and without legacy applications alike, which enables users to query and verify provenance data on SAMbA-RaP reports straightforwardly and transparently.

Embedding Analytics within the Curation of Scientific Workflows.

This paper reports on the ongoing activities and curation practices of the National Center for Biomolecular NMR Data Processing and Analysis1. Over the past several years, the Center has been developing and extending computational workflow management software for use by a community of biomolecular NMR spectroscopists. Previous work had been to refactor the workflow system to utilize the PREMIS framework for reporting retrospective provenance as well as for sharing workflows between scientists and to support data reuse. In this paper, we report on our recent efforts to embed analytics within the workflow execution and within provenance tracking. Important metrics for each of the intermediate datasets are included within the corresponding PREMIS intellectual object, which allows for both inspection of the operation of individual actors as well as visualization of the changes throughout a full processing workflow.These metrics can be viewed within the workflow management system or through standalone metadata widgets. Our approach is to support a hybrid approach of both automated, workflow execution as well as manual intervention and metadata management. In this combination, the workflow system and metadata widgets encourage the domain experts to be avid curators of the data which they create, fostering both computational reproducibility and scientific data reuse.

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv.

BackgroundThe automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Computationally driven data-intensive experiments using workflows enable automation, scaling, adaptation, and provenance support. However, there are still several challenges associated with the effective sharing, publication, and reproducibility of such workflows due to the incomplete capture of provenance and lack of interoperability between different technical (software) platforms.ResultsBased on best-practice recommendations identified from the literature on workflow design, sharing, and publishing, we define a hierarchical provenance framework to achieve uniformity in provenance and support comprehensive and fully re-executable workflows equipped with domain-specific information. To realize this framework, we present CWLProv, a standard-based format to represent any workflow-based computational analysis to produce workflow output artefacts that satisfy the various levels of provenance. We use open source community-driven standards, interoperable workflow definitions in Common Workflow Language (CWL), structured provenance representation using the W3C PROV model, and resource aggregation and sharing as workflow-centric research objects generated along with the final outputs of a given workflow enactment. We demonstrate the utility of this approach through a practical implementation of CWLProv and evaluation using real-life genomic workflows developed by independent groups.ConclusionsThe underlying principles of the standards utilized by CWLProv enable semantically rich and executable research objects that capture computational workflows with retrospective provenance such that any platform supporting CWL will be able to understand the analysis, reuse the methods for partial reruns, or reproduce the analysis to validate the published findings.

CWLProv - Interoperable Retrospective Provenance capture and its challenges

CWLProv - Interoperable Retrospective Provenance capture and its challenges

P-PIF: a ProvONE provenance interoperability framework for analyzing heterogeneous workflow specifications and provenance traces

Enabling provenance interoperability by analyzing heterogeneous provenance information from different scientific workflow management systems is a novel research topic. With the advent of the ProvONE model, it is now possible to model both the prospective as well as the retrospective provenance in a single provenance model. Scientific workflows are composed using a declarative definition language, such as BPEL, SCUFL/t2flow, or MoML. Associated with the execution of a workflow is its corresponding provenance that is modeled and stored in the data model specified by the workflow system. However, sharing of provenance generated by heterogeneous workflows is a challenging task and prevents the aggregate analysis and comparison of workflows and their associated provenance. To address these challenges, this paper introduces a ProvONE-based Provenance Interoperability Framework that completely automates the modeling of provenance from heterogeneous WfMSs by: (a) automatically translating the scientific workflows to their equivalent representation in a ProvONE prospective graph using the Prov2ONE algorithm, (b) enriching the ProvONE prospective graph with the retrospective provenance exported by the WfMSs, and (c) native support for storing the ProvONE provenance graphs in a Resource Description Framework triplestore that supports the SPARQL query language for querying and retrieving ProvONE graphs. The Prov2ONE algorithm is based on a set of vocabulary translation rules between workflow specifications and the ProvONE model. The correctness and completeness proof of the algorithm is shown and its complexity is analyzed. Moreover, to demonstrate the practical applicability of the complete framework, ProvONE graphs for workflows defined in BPEL, SCUFL, and MoML are generated. Finally, the provenance challenge queries are extended with six additional queries for retrieving the provenance modeled in ProvONE.

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.