Abstract The potential to obtain the correct information, manage it, and interchange it are some of the keys to the success of a project. Integrating multiple information systems and presenting functionalities within a unified view remains challenging, particularly for long-term projects such as production plants in the Oil and Gas industry. Digital Engineering contributes to representing, consuming, and managing the engineering information associated with process plants. In the Oil and Gas industry context, the Capital Facilities Information Handover Specifications (CFIHOS) represents an initiative to improve how information is exchanged between companies that own, operate, and build equipment for the process and energy sectors. In that sense, CFIHOS proposes a data model and a library (Reference Data Library—RDL) with standard terms for data interchange. A review of current solutions supporting CFIHOS guidelines and its proposed data model revealed that few software implementations fully support CFIHOS specifications. This work presents an implementation using a subset of the CFIHOS data model for all CFIHOS´s Contract Scenario Templates. The use case was implemented over the INSIDE system, allowing dynamic integration between heterogeneous databases using an architecture based on web services. The knowledge of extracting the information from databases and generating the data structured to accomplish the CFIHOS data model was encoded in INSIDE by defining data services in the knowledge base. Addressing the gap in data model verification is critical to enhancing the effectiveness of data integration practices and ensuring compliance with CFIHOS standards, but no available tools verify if the information in a file complies with the CFIHOS data model and RDL information. For this reason, a prototype of a validator was implemented to verify the challenges of using CFIHOS. It was tested following the use case context of Petrobras company. The developed CFIHOS Validator complements the data extraction, and data verification flows according to this standard.

The short variant discovery is one of the most important steps into genomics studies since it allows genetic variants identification that influences the emergence and evolution of some diseases. Specifically, cancer can be associated with germline variants present in small populations, such as somatic variants located in tumor cells. Therefore, it is necessary to implement workflows that allow data analysis resulting from the new generation sequencing while taking advantage of the resources available in HPC infrastructures. This work presents the PIPEMB-WDL workflow for HPC infrastructure to integrate the short variant discovery for germline and somatic calling, including pre-processing and variants refinement steps, following the best practices of GATK4. This workflow was developed using emerging technologies in current development like WDL and Cromwell engine. The challenges we address in this paper are integrating and deploying container technologies, workload manager technologies, Cromwell, and WDL in our HPC infrastructure.

In the new era of Information & Communication Technologies, the interoperability between systems has been gaining importance in the industry, becoming the key to achieve better information management, production control, decision making, failure control, and risk management, significantly reducing the production costs. Studies to provide interoperability between systems have been focused on computational architectures, including conceptual models and implementations. In this work, a novel proposed architecture is presented. It is based on paradigms like Polystores and Ontology-based Data Access (OBDA), and streaming data processing over micro-services. Also, an implementation of the proposed architecture was developed. Finally, the approach was evaluated over a case study that integrates engineering data from different data sources in the oil and gas industry context.

Fragment assembly is a current fundamental problem in bioinformatics. In the absence of a reference genome sequence that could guide the whole process, a de Bruijn Graph data structure has been considered to improve the computational processing. Notably, we need to count on a broad set of k-mers, biological sequences substrings. However, the construction of a de Bruijn Graph has a high computational cost, primarily due to main memory consumption. Some approaches use external memory processing to achieve feasibility. These solutions generate all k-mers with high redundancy, increasing the number of managed data and, consequently, the number of I/O operations. This work proposes a new approach for de Bruijn Graph construction that does not need to generate all k-mers. The solution enables to reduce computational requirements and execution feasibility.

K-mer Mapping, an internal process for many de novo genome fragments assembly methods, constitutes a computational challenge due to its high main memory consumption. We present in this paper a study of indexing methods to deal with this problem, considering plant genome assembling. We propose an ad-hoc I/O cost model to analyze the performance of B+- tree and hashing index structures. We use indexes to detect duplicate k-mers and improve the execution time. An actual RDBMS implementation for experiments with a sugarcane data set shows that one can obtain considerable performance gains while reducing RAM requirements.