José Miguel Blanco

BUILDING A FEDERATED RELATIONAL DATABASE SYSTEM: AN APPROACH USING A KNOWLEDGE-BASED SYSTEM

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 systems 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.

An Optimal Cache for a Federated Database System

Federated database systems allow users to query different autonomousdatabases with a single request. The answer to those requests mustbe found on the underlying databases. This answering process can beimproved if some data are cached within the federated databasesystem. The article presents an approach that allows the definitionof an optimal cache for a federated database system according to aset of parameters. We show the types of objects to be cached, thecost model used to decide which ones are worth caching and the methodto find the optimal set of objects to cache. Moreover, this approachcontinuously updates the set of parameter values and periodicallyredefines the optimal cache in order to reflect changes in the userrequirements or in the implementation features of the underlyingdatabases. The article also presents how cached data can be used toanswer a user query. Furthermore, the advantages of using a KnowledgeRepresentation System based on Description Logics in order to definean optimal cache for a federated database system are shown throughthe paper.

Interuniversity telecollaboration to improve academic results and identify preferred communication tools

Telecollaboration is defined as a collaborative activity that involves people from distant geographic locations working together through Internet tools and other resources. This technique has not been frequently used in learning experiences and has produced diverse academic results, as well as degrees of satisfaction. This paper describes a telecollaboration experience among students at two universities. We analyzed data from 255 students divided into different groups in order to compare their academic results and satisfaction derived from the experience. We also studied the communication tools used by the telecollaborators and their effect on with student satisfaction. The telecollaborators obtained better academic results but lower levels of satisfaction than the face-to-face groups. Furthermore, the students preferred day-to-day tools, and the use of non-institutional email was the most popular option. Social networks and chat and voice tools also proved to be useful. And, those who employed synchronous tools were more satisfied with the experience.

Mapping among knowledge bases and data repositories: precise definition of its syntax and semantics

Abstract Nowadays there is an explosion of data repositories available in the Global Information System. In this context, users meet with the problems related to the heterogeneity and distribution of the data repositories. Access to the data may be facilitated by providing semantic views over the repositories that hide all the technical and organizative details associated with them. In this paper we present the definition of the mapping relation that permits one to relate data elements of the semantic views and those of the underlying data repositories. We define the syntax and the semantics of the mapping. From the syntactic point of view, mapping expressions are tuples whose main components are relational algebra expressions. From the semantic point of view, the notions of well formed mapping relation and the extension defined by that mapping are introduced. Moreover, we demonstrate the completeness property of the mapping relation in the sense that it allows for answers to all queries formulated over the semantic view using the data stored in the data repositories.

Business understanding, challenges and issues of Big Data Analytics for the servitization of a capital equipment manufacturer

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 Governments Industrie 4.0 or General Electrics 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.

Making the knowledge base systems more efficient: a method to detect inconsistent queries

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. >

Making a Federated System Active

A Federated Database System consists of autonomous component database systems that participate in the federation to allow controlled sharing of their data. It usually provides a global integrated view, a federated schema, that describes data stored in the component database systems. A very important aspect for this type of systems is to maintain the autonomy of the components while preserving the correct semantics of the federated schema. In order to achieve this goal we propose a system that responds automatically to design changes made in a component database, that affect the federated schema, without user intervention. Further, our system provides each component that decides to participate in a federation with the opportunity to choose between assuming the default control offered to it, or customizing the control by defining declaratively the system responses. Both cases rely on the use of Event-Condition-Action rules supported by the system. We also present a federated system architecture in which this system is a component.

I4TSRS: A System to Assist a Data Engineer in Time-Series Dimensionality Reduction in Industry 4.0 Scenarios

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. In this paper we present a system called I4TSRS1, available as a Web Application, that efficiently guides a data engineer in the task of obtaining industrial time-series data reduced representations that preserve their main characteristics. Dealing with those reduced representations, data storage and transmission costs can be decreased, without limiting the future exploitation of the data in different processes. The novel contribution of the I4TSRS is that it is an intelligent system that recommends the best techniques to achieve a reduced representation of time-series captured in industrial settings. Its core element is a machine learning model that combines time-series reduction techniques with extracted features from industrial time-series. We have built the model using several heterogeneous real industrial time-series.

Requirements for a big data capturing and integration architecture in a distributed manufacturing scenario

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.