Automating Provenance Capture in Software Engineering with UML2PROV

Sáenz-Adán, Carlos; Moreau, Luc; Pérez, Beatriz; Miles, Simon; García‐Izquierdo, Francisco J.

doi:10.1007/978-3-319-98379-0_5

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Future work includes to add more sources to the provenance graph. For example, from design documents (UML2PROV [9]) or IDE's. The provenance graph can also be extended with provenance for (running) algorithms, data, or machine learning processes [3].…”

Section: Discussionmentioning

confidence: 99%

Modelling Knowledge about Software Processes using Provenance Graphs and its Application to Git-based Version Control Systems

Schreiber

Boer

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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Section: Discussionmentioning

confidence: 99%

Modelling Knowledge about Software Processes using Provenance Graphs and its Application to Git-based Version Control Systems

Schreiber

Boer

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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“…Implementing a decision support system in the health domain has shown potential for integrating trust into computerized systems, enabling transparency and auditability (Curcin et al, 2017). In software engineering, a template-based provenance capture mechanism can also be implemented by mapping the structural diagrams of designed applications into provenance templates (Sáenz-Adán et al, 2018). Furthermore, a service for uploading and disseminating provenance templates has been established in the field of environmental and earth sciences, which can be used to build uniform provenance traces from input data in accordance with standards (Magagna et al, 2020).…”

Section: Template-based Provenance Capturementioning

confidence: 99%

Automatic Generation of Temporal Data Provenance From Biodiversity Information Systems

Akbar

Saleh

Kartika

et al. 2022

IJIKM

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Aim/Purpose: Although the significance of data provenance has been recognized in a variety of sectors, there is currently no standardized technique or approach for gathering data provenance. The present automated technique mostly employs workflow-based strategies. Unfortunately, the majority of current information systems do not embrace the strategy, particularly biodiversity information systems in which data is acquired by a variety of persons using a wide range of equipment, tools, and protocols. Background: This article presents an automated technique for producing temporal data provenance that is independent of biodiversity information systems. The approach is dependent on the changes in contextual information of data items. By mapping the modifications to a schema, a standardized representation of data provenance may be created. Consequently, temporal information may be automatically inferred. Methodology: The research methodology consists of three main activities: database event detection, event-schema mapping, and temporal information inference. First, a list of events will be detected from databases. After that, the detected events will be mapped to an ontology, so a common representation of data provenance will be obtained. Based on the derived data provenance, rule-based reasoning will be automatically used to infer temporal information. Consequently, a temporal provenance will be produced. Contribution: This paper provides a new method for generating data provenance automatically without interfering with the existing biodiversity information system. In addition to this, it does not mandate that any information system adheres to any particular form. Ontology and the rule-based system as the core components of the solution have been confirmed to be highly valuable in biodiversity science. Findings: Detaching the solution from any biodiversity information system provides scalability in the implementation. Based on the evaluation of a typical biodiversity information system for species traits of plants, a high number of temporal information can be generated to the highest degree possible. Using rules to encode different types of knowledge provides high flexibility to generate temporal information, enabling different temporal-based analyses and reasoning. Recommendations for Practitioners: The strategy is based on the contextual information of data items, yet most information systems simply save the most recent ones. As a result, in order for the solution to function properly, database snapshots must be stored on a frequent basis. Furthermore, a more practical technique for recording changes in contextual information would be preferable. Recommendation for Researchers: The capability to uniformly represent events using a schema has paved the way for automatic inference of temporal information. Therefore, a richer representation of temporal information should be investigated further. Also, this work demonstrates that rule-based inference provides flexibility to encode different types of knowledge from experts. Consequently, a variety of temporal-based data analyses and reasoning can be performed. Therefore, it will be better to investigate multiple domain-oriented knowledge using the solution. Impact on Society: Using a typical information system to store and manage biodiversity data has not prohibited us from generating data provenance. Since there is no restriction on the type of information system, our solution has a high potential to be widely adopted. Future Research: The data analysis of this work was limited to species traits data. However, there are other types of biodiversity data, including genetic composition, species population, and community composition. In the future, this work will be expanded to cover all those types of biodiversity data. The ultimate goal is to have a standard methodology or strategy for collecting provenance from any biodiversity data regardless of how the data was stored or managed.

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Provenance Supporting Hyperparameter Analysis in Deep Neural Networks

Pina

Kunstmann

Oliveira

et al. 2021

Lecture Notes in Computer Science

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The duration of the life cycle in deep neural networks (DNN) depends on the data configuration decisions that lead to success in obtaining models. Analyzing hyperparameters along the evolution of the network's execution allows for adapting the data. Provenance data derivation traces help the parameter fine-tuning by providing a global data picture with clear dependencies. Provenance can also contribute to the interpretation of models resulting from the DNN life cycle. However, there are challenges in collecting hyperparameters and in modeling the relationships between the data involved in the DNN life cycle to build a provenance database. Current approaches adopt different notions of provenance in their representation and require the execution of the DNN under a specific software framework, which limits interoperability and flexibility when choosing the DNN execution environment. This work presents a provenance data-based approach to address these challenges, proposing a collection mechanism with flexibility in the choice and representation of data to be analyzed. Experiments of the approach, using a convolutional neural network focused on image recognition, provide evidence of the flexibility, the efficiency of data collection, the analysis and the validation of network data.

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Automating Provenance Capture in Software Engineering with UML2PROV

Cited by 7 publications

References 12 publications

Modelling Knowledge about Software Processes using Provenance Graphs and its Application to Git-based Version Control Systems

Modelling Knowledge about Software Processes using Provenance Graphs and its Application to Git-based Version Control Systems

Automatic Generation of Temporal Data Provenance From Biodiversity Information Systems

Provenance Supporting Hyperparameter Analysis in Deep Neural Networks

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