Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientists' expertise, tools, and workflows. If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to explain to stakeholders how it was created. The main limitation of provenance tracking solutions is that they cannot cope with provenance capture and integration of domain and ML data processed in the multiple workflows in the lifecycle, while keeping the provenance capture overhead low. To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary. We show a practical use in a real case in the O&G industry, along with its evaluation using 48 GPUs in parallel.Index Terms-Machine Learning Lifecycle, Workflow Provenance, Computational Science and Engineering (ii) PROV-ML: a new data representation, which combines W3C PROV [18] with W3C ML Schema [19], for prove-R. Souza et al. Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering.

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Machine learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains, users need to perform comprehensive data analyses combining scientific data and ML models to provide for critical requirements, such as reproducibility, model explainability, and experiment data understanding. However, scientific ML is multidisciplinary, heterogeneous, and affected by the physical constraints of the domain, making such analyses even more challenging. In this work, we leverage workflow provenance techniques to build a holistic view to support the lifecycle of scientific ML. We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design decisions to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs. The experiments show that the decisions enable queries that integrate domain semantics with ML models while keeping low overhead (<1%), high scalability, and an order of magnitude of query acceleration under certain workloads against without our representation.

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RESUMOO crescente aumento da criminalidade em cidades brasileiras é um tema recorrente tanto nos veículos de comunicação como nas pautas das autoridades governamentais. Para combater efetivamente a criminalidade é necessário que recursos humanos e infraestrutura sejam cuidadosamente aplicados, de forma a não apenas punir quem cometeu o crime, mas preferencialmente prever e evitar que o mesmo aconteça. Dada a dificuldade de coletar um grande volume de informações oficiais relacionadas a crimes em todas as regiões de um município, uma tendência é que os próprios cidadãos atuem como fonte de dados, a partir de sistemas colaborativos baseados na Web. Entretanto, tal fonte de dados pode se tornar muito complexa e vasta, dificultando a análise manual de padrões de ocorrências de crimes, de forma a evitar que eles aconteçam. Com essa motivação, desenvolvemos nesse artigo um sistema denominado SiAPP (Sistema de Apoio ao Policiamento Preditivo), para apoiar a análise e predição de padrões relacionados a ocorrências de crimes, a partir de um método de aprendizado de máquina. O SIAPP tem como habilidades a coleta automática de informações a partir de dados colaborativos, a criação automática de regras lógicas a partir de tais informações e a visualização geográfica dos padrões descobertos. Resultados experimentais mostram que o SiAPP é uma abordagem promissora para o auxílio no combate ao crime. ABSTRACTThe growing of criminality in Brazilian cities is a common theme addressed by media as well as by the legal authorities. To effectively reduce the criminality, people and infrastructure must be carefully involved to not only punish who had committed crimes, but also predict and prevent it. Since acquiring official data about crimes is far from trivial, citizens have become important data sources through Web-based collaborative systems. These systems provide a huge volume of data that has to be analyzed. How to analyze this volume of data and identify patterns in crimes is an important, yet open, issue. Thus, this work presents a system called SiAPP. Its main objective is to support the analysis and prediction of crime patterns using a machine learning algorithm. SiAPP automatically acquires data from collaborative sources, generate logical rules and visualizes the found patterns. Experimental analysis shows that SiAPP is a promising solution tool to assist crimes prevention.