The increasing interest among manufacturers in monitoring and analyzing industrial systems is generating a problem related to the considerable costs associated with the storage of captured data. This paper presents a three-level hierarchical architecture for time-series data storage on cloud environments that helps to decrease those costs. In the first level, new raw time-series data is stored for a short-period of time (e.g., one day) on electronic non-volatile storage such as solid-state drives (SSDs) that provide fast access for real time visualization of the latest data. In the second level, recent time series are stored for a medium-period of time (e.g., one week) on magnetic hard disk drives (HDDs) that are lower-cost devices with slower data transfer speed. In the third level, a reduced representation of the time series obtained by applying time-series reduction techniques are stored in HDDs, for a longer period of time (e.g., one year). Dealing with those reduced representations, data storage and transmission costs can be decreased, without limiting the future use of the data in different processes.The architecture has been implemented by using the top Database Management System from three different categories: Wide column store, Time series DBMS and Graph DBMS. It has been tested using industrial time series coming from a real manufacturing environment, and with three different types of queries proposed by domain experts. The performance results regarding storage space, storage costs and query time processing are shown on the paper.

It is important for both computer science academics and students to clearly comprehend the differences between academic and professional perspectives in terms of assessing a deliverable. It is especially interesting to determine whether the aspects deemed important to evaluate by a computer science expert are the same as those established by academics and students. Such potential discrepancies are indicative of the unexpected challenges students may encounter once they graduate and begin working. In this article, we propose a learning activity in which computer science students made a video about their future profession after hearing an expert in the field who discussed about the characteristics and difficulties of his or her work. Academics, professional experts, and students assessed the videos by means of a questionnaire. This article reports a quantitative study of the results of this experience, which was conducted for three academic years. The study involved 63 students, 6 academics, and 4 computing professionals with extensive experience, and 14 videos were evaluated. Professional experts proved to be the most demanding in the assessment, followed by academics. The least demanding group was the students. These differences are more salient if more substantial issues are examined. The experts focused more on aspects of content, whereas the student preferred to concentrate on format. The academics’ focus falls between these two extremes. Understanding how experts value knowledge can guide educators in their search for effective learning environments in computing education.

The massive captured data from industrial sensors (time-series data) that could serve as relevant indicators for predictive maintenance of equipment, fault diagnosis, etc. is generating a problem related to the considerable costs associated with their storage.

Big Data is one of the key enabling technologies in smart manufacturing, where manufacturing companies aim at leveraging the data generated throughout their processes. The potential of Big Data Analytics is particularly significant in the context of manufacturing companies distributed worldwide. These companies own several manufacturing plants operating the same process in different environments and conditions. This generates massive amounts of data that could be analyzed in order to improve process efficiency and product quality. This paper presents the requirements for an architecture to capture, integrate and analyze the large-scale volumes of data generated in a real-world manufacturing business scenario - a chemical manufacturing sector distributed worldwide-. This scenario serves as a case study for an applied research project on Big Data Analytics. The business nature of this scenario provides those real-life requirements the architecture has to deal with. Existing approaches can be extended to fulfill these requirements, in order to be effectively applied in similar manufacturing business contexts.

One of the most promising areas where Big Data Analytics can be integrated into business-oriented projects-allowing research and development teams to work hand in hand with industry representatives - is the digitalization of manufacturing industry. There are two main driving forces for the interest in this area: the promotion of key strategies such as German Government's Industrie 4.0 or General Electric's Industrial Internet, and the use of servitization strategies to transform manufacturing business models. This paper presents a case study based on a Big Data Analytics applied research project developed for a capital equipment manufacturer. Their current business model is based on selling machinery and storage infrastructure for larger chemical manufacturing companies spread worldwide. The project is developed in the context of a servitization strategy where this capital equipment manufacturer aims at attaching valued-added services to their products, leveraging the use of Big Data Analytics to assist their customers in order to optimize their production process. The paper uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology as a framework to organize and present our main findings related to the Business Understanding phase. This allow us to provide pragmatic, business-oriented considerations that can be leveraged by research and development teams when exploring opportunities to develop Big Data Analytics projects in the context of manufacturing servitization. Thus, they can face the initial steps of those projects with a better understanding of the specificities of this application field, as well as an a priori identification of problematic situations that may arise and required competencies to be covered by their team members.

Due to the emerging interest in integrating different application environments, there have been many recent proposals for federated systems. In this paper, a federated system that permits the integration of heterogeneous relational databases using a terminological knowledge representation system is presented. In particular, two of the system's components: the translator and the integrator are explained in depth. The translator permits one to obtain a terminology from a relational schema, either semiautomatically, by expressing database properties, or manually, by using a set of predefined operations. In turn, the integrator generates a federated terminology by integrating several terminologies using the semantics expressed as correspondences between the data elements of different terminologies. Unlike many other approaches, the use of a terminological system permits us to obtain a semantically richer federated terminology and, at the same time, define a wider and more consistent integration process.

The processing cost of queries with an empty answer, both in the database and knowledge base context, is usually high. One purpose of semantic query optimization methods in the database context is to use semantic knowledge to detect such types of queries. Although semantic query optimization is well known in the database context, this is not the case for knowledge base systems (KBSs). This paper presents a method that allows the detection of queries with an empty answer using only semantic information expressed in the knowledge base definition. The method can be applied in the context of KBSs that provide some of the following features: structuring mechanisms, assertional knowledge, temporal information, and handling of inequality expressions.>