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.

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.

Many applications require the processing of event streams from different sources in combination with large amounts of background knowledge. Semantic CEP is a paradigm designed specifically for that. It extends complex event processing (CEP) with RDF support and uses a network of operators to process RDF streams in combination with RDF knowledge bases. Another popular class of systems designed for a similar purpose are the RDF stream processors (RSPs). These are systems that extend SPARQL (the RDF query language) with stream processing capabilities. Semantic CEP and RSPs have similar purposes but focus on different things. The former focuses on scalability and distributed processing while the latter tend to focus on the intricacies of RDF stream processing per se. In this paper we propose the use of RSP engines as building blocks for Semantic CEP. We present an infrastructure, called DSCEP, that allows the encapsulation of existing RSP engines into CEP-like operators so that these can be seamlessly interconnected in a distributed, decentralized operator network. DSCEP handles the hurdles of such interconnection, such as reliable communication, stream aggregation and slicing, event identification and time-stamping, etc., allowing users to concentrate on the queries. We also discuss in the paper how DSCEP can be used to speedup monolithic SPARQL queries by splitting them into parallel subqueries operating over restricted parts of the knowledge base.

Nowadays, Internet of Things (IoT) application development must deal with a variety of devices, wireless protocols, types of gateways and particularities about each sensor. This paper presents Deklaer, an Ontology-Driven framework to facilitate the development and deployment of IoT applications. We use ContextNet, witch is a middleware for IoT applications, and a declarative language implemented using an OWL ontology that provides a way for the developer to describe which sensors to use. Moreover, depending on the data acquired by each sensor, which is the actuation that the application is to effect as response to the sensor's data. Also, we present ICare, which is an application created using Deklaer, to monitor physiological data of elderly people with special needs.

Smart Forest is a concept derived from the Internet of Things (IoT) and defines sections of a forest where remote sensing is applied to collect data about environmental conditions. One of the main objectives of Smart Forests is to detect wild fire at early stages. However, the required technology for such monitoring usually demands a complex and expensive sensor and network infrastructure and requires central processing capabilities for analyzing data from several thousands of sensors. The goal of this work is to propose a solution focused on Edge Computing, using the concept of Mobile Hubs (M-Hubs). The developed Fire Detection IoT prototype application is based on the ContextNet middleware, and uses Event Processing Agents (EPAs), running on smart phones carried by forest guards. Scalability tests up to 10,000 remote sensors connected per MHub were performed.

We present a means of generating a mobile clientserver application that enables customized remote monitoring of patients with chronic diseases. The customization is based on parameters and formal descriptions of: type of chronic disease, patient preferences, the monitoring procedure required by the doctor, prescribed medication and information about the current context (i.e., environment) of the patient, obtained from sensors. Based on this, the system determines which pieces of information should be regularly obtained from the patient through questionnaires and/or sensors of the smart phone and sensors connected to it. Relevant information are the ones that help to detect possible changes in the patient's health condition and the monitoring process of a patient by the doctor. The medical treatment and the kind of chronic disease will define the set of data to be collected. It should be stressed that the goal is not to support automatic medical diagnosis, but only to provide means for physicians to obtain updated information about their patients, so as to allow remote monitoring of patients.