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Faculty of Technology at Polytechnic Institute of Management and Technology, Portugal

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17 Papers

Pervasive Information Systems to Intensive Care Medicine - Technology Acceptance Model

Topics: HCI on Enterprise Information Systems; Performance Evaluation and Benchmarking; Measurements, Metrics and Evaluation of EA Artefacts and Processes; Software Metrics and Measurement

Authors: Jorge Aguiar ¹ , Filipe Portela ¹ , Manuel Filipe Santos ¹ , José Machado ¹ , António Abelha ¹ , Álvaro Silva ² , Fernando Rua ² and Filipe Pinto ³

Affiliations: ¹ University of Minho, Portugal , ² Centro Hospitalar do Porto, Portugal , ³ Instituto Politécnico de Leiria, Portugal

Digital Object Identifier: 10.5220/0004441001770184

Proceedings of ICEIS 2013, ISBN: 978-989-8565-59-4

Quick Abstract

The usability of information systems in critical environments like Intensive Care Units (ICU) is far than the expected and desirable. Typically, ICUs have a set of not integrated information silos and a high number of data recorded in paper. Whenever ICU professionals need to make a decision they have to deal with a high number of data sources containing useful information. Unfortunately, they can't use those sources due to the difficulty of evaluating them in a correct time. Pervasive Intelligent Decision Support Systems (PIDSS), operating automatically and in real-time, can be used to improve the decision making if they are suited to the requirements of the ICU. In this work a PIDSS have been assessed in terms of quality and user acceptance making use of Technology Acceptance Model (TAM). TAM proved to be very useful when combined with Delphi method features to involve the professionals and to make the system usable.

Related Ontology Subjects/Areas/Topics (7)

Databases and Information Systems Integration	Human-Computer Interaction	Performance Evaluation and Benchmarking
Enterprise Information Systems	Information Systems Analysis and Specification	Software Metrics and Measurement
HCI on Enterprise Information Systems

Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine

Topics: Business Intelligence & CRM; Knowledge Management Projects; Intelligent Information Systems

Authors: Filipe Portela , Filipe Pinto and Manuel Filipe Santos

Affiliation: Universidade do Minho, Portugal

Digital Object Identifier: 10.5220/0004141500810088

Proceedings of KMIS 2012/IC3K, ISBN: 978-989-8565-31-0

Quick Abstract

The introduction of an Intelligent Decision Support System (IDSS) in a critical area like the Intensive Medicine is a complex and difficult process. In this area, their professionals don’t have much time to document the cases, because the patient direct care is always first. With the objective to reduce significantly the manual records and, enabling, at the same time, the possibility of developing an IDSS which can help in the decision making process, all data acquisition process and knowledge discovery in database phases were automated. From the data acquisition to the knowledge discovering, the entire process is autonomous and executed in real-time. On-line induced data mining models were used to predict organ failure and outcome. Preliminary results obtained with a limited population of patients showed that this approach can be applied successfully.

Related Ontology Subjects/Areas/Topics (9)

Applications	Intelligent Information Systems	Knowledge-Based Systems
Artificial Intelligence	Knowledge Management and Information Sharing	Software Engineering
Business Intelligence	Knowledge Management Projects	Symbolic Systems

KNOWLEDGE DISCOVERY FOR PERVASIVE AND REAL-TIME INTELLIGENT DECISION SUPPORT IN INTENSIVE CARE MEDICINE

Topics: Interoperability; Business Intelligence & CRM; Intelligent Information Systems

Authors: Filipe Portela ¹ , Pedro Gago ² , Manuel Filipe Santos ¹ , Alvaro Silva ³ , Fernando Rua ⁴ , José Machado ¹ , António Abelha ¹ and José Neves ¹

Affiliations: ¹ Universidade do Minho, Portugal , ² Instituto Politécnico de Leiria, Portugal , ³ Centro Hospitalar do Porto, Portugal , ⁴ Centro Hospitalar do Porto - Hospital de Santo António, Portugal

Digital Object Identifier: 10.5220/0003677002410249

Proceedings of KMIS 2011/IC3K, ISBN: 978-989-8425-81-2

Quick Abstract

Pervasiveness, real-time and online processing are important requirements included in the researchers’ agenda for the development of future generation of Intelligent Decision Support Systems (IDSS). In particular, knowledge discovery based IDSS operating in critical environments such of intensive care, should be adapted to those new requests. This paper introduces the way how INTCare, an IDSS developed in the intensive care unit of the Centro Hospitalar do Porto, will accommodate the new functionalities. Solutions are proposed for the most important constraints, e.g., paper based data, missing values, values out-of-range, data integration, data quality. The benefits and limitations of the approach are discussed.

Related Ontology Subjects/Areas/Topics (22)

Applications	Enterprise Information Systems	Ontologies and the Semantic Web
Artificial Intelligence	Health Information Systems	Sensor Networks
Biomedical Engineering	Integration/Interoperability	Simulation and Modeling
Business Intelligence	Intelligent Information Systems	Software Agents and Internet Computing
Collaboration and e-Services	Interoperability	Software and Architectures
Complex Systems Modeling and Simulation	Knowledge Management and Information Sharing	Software Engineering
Data Engineering	Knowledge-Based Systems	Symbolic Systems
e-Business

KNOWLEDGE EXTRACTION GUIDED BY ONTOLOGIES - Database Marketing Application

Topic: Coupling and Integrating Heterogeneous Data Sources

Authors: Filipe Mota Pinto ¹ and Teresa Guarda ²

Affiliations: ¹ Politechnic Institute of Leiria, Portugal , ² Superior Institute of Languages and Administration of Leiria, Portugal

Digital Object Identifier: 10.5220/0003555103200325

Proceedings of ICEIS 2011, ISBN: 978-989-8425-53-9

Quick Abstract

The knowledge extraction in large databases has being known as a long term and interactive project. In spite of such complexity and different options for the knowledge achievement, there is a research opportunity that could be explored, throughout the ontologies support. Then this support may be used for knowledge sharing and reuse. This paper describes a research of an ontological approach for leveraging semantic content of ontologies to improve knowledge extraction in a oil company marketing databases. We attain to analyze how ontologies and knowledge discovery process may interoperate and present our efforts to propose a possible framework for a formal integration.

Related Ontology Subjects/Areas/Topics (3)

Coupling and Integrating Heterogeneous Data Sources

Databases and Information Systems Integration

Enterprise Information Systems

HYBRID POPULATION-BASED INCREMENTAL LEARNING TO ASSIGN TERMINALS TO CONCENTRATORS

Topics: Genetic Algorithms; Bio-Inspired Hardware and Networks; Swarm/Collective Intelligence

Authors: Eugénia Moreira Bernardino ¹ , Anabela Moreira Bernardino ¹ , Juan Manuel Sánchez-Pérez ² , Juan Antonio Gómez-Pulido ² and Miguel Angel Vega-Rodríguez ²

Affiliations: ¹ School of Technology and Management and Polytechnic Institute of Leiria, Portugal , ² Polytechnic School and University of Extremadura, Spain

Digital Object Identifier: 10.5220/0003076301820189

Proceedings of ICEC 2010/IJCCI, ISBN: 978-989-8425-31-7

Quick Abstract

In the last decade, we have seen a significant growth in communication networks. In centralised communication networks, a central computer serves several terminals or workstations. In large networks, some concentrators are used to increase the network efficiency. A collection of terminals is connected to a concentrator and each concentrator is connected to the central computer. In this paper we propose a Hybrid Population-based Incremental Learning (HPBIL) to assign terminals to concentrators. We use this algorithm to determine the minimum cost to form a network by connecting a given collection of terminals to a given collection of concentrators. We show that HPBIL is able to achieve good solutions, improving the results obtained by previous approaches.

Related Ontology Subjects/Areas/Topics (9)

Artificial Intelligence	Evolutionary Computing	Intelligent Control Systems and Optimization
Bio-inspired Hardware and Networks	Genetic Algorithms	Soft Computing
Computational Intelligence	Informatics in Control, Automation and Robotics	Swarm/Collective Intelligence

PROMOTING STUDENTS SUCCESS WITH A BUSINESS INTELLIGENCE SYSTEM

Topic: Supervising and Managing Student Projects

Authors: Maria Beatriz Piedade ¹ and Maribel Yasmina Santos ²

Affiliations: ¹ School of Technology and Management, Polytechnic Institute of Leiria, Portugal , ² Algoritmi Research Centre, University of Minho, Portugal

Digital Object Identifier: 10.5220/0002793300460052

Proceedings of CSEDU 2010, ISBN: 978-989-674-024-5

Quick Abstract

The promotion of the students’ success is usually associated to the closely monitoring of the students’ activities. This requires the implementation of mechanisms that, in the scope of the teaching/learning process, allow the students’ academic activities monitoring, the students’ academic success/failure evaluation and the teacher/tutor approximation to the students’ day-by-day academic activities. Although important, the activities involved in these processes do not take place in many higher education institutions, due to the lack of an appropriate support. To sustain these complex processes and activities, a conceptual framework and a technological infrastructure was proposed and integrated in a Student Relationship Management (SRM) System. The SRM System supports the SRM concept and practice and it has been implemented using concepts and technologies associated with the Business Intelligence systems. To validate the SRM System, several application cases were implemented in real contexts. They demonstrated the system relevance in the process of acquisition of knowledge about the students and their academic behaviours. The system supports the decision making process in the teaching/learning scope and facilitates the automatic interaction with the students. The impact of the several undertaken actions are presented and analysed in this paper.

Related Ontology Subjects/Areas/Topics (3)

Computer-Supported Education

Learning/Teaching Methodologies and Assessment

Supervising and Managing Student Projects

SOLVING THE RING ARC-LOADING PROBLEM USING A HYBRID SCATTER SEARCH ALGORITHM

Topics: Genetic Algorithms; Co-Evolution and Collective Behavior; Bio-Inspired Hardware and Networks; Swarm/Collective Intelligence

Authors: Anabela Moreira Bernardino ¹ , Eugénia Moreira Bernardino ¹ , Juan Manuel Sánchez-Pérez ² , Juan Antonio Gómez-Pulido ² and Miguel Angel Vega-Rodríguez ²

Affiliations: ¹ Polytechnic Institute of Leiria, Portugal , ² University of Extremadura, Spain

Digital Object Identifier: 10.5220/0003076200600069

Proceedings of ICEC 2010/IJCCI, ISBN: 978-989-8425-31-7

Quick Abstract

Resilient Packet Ring (RPR) is a standard that uses Ethernet switching and a dual counter-rotating ring topology to provide SONET-like network resiliency and optimised bandwidth usage, while it delivers multipoint Ethernet/IP services. An important optimisation problem arising in this context is the Weighted Ring Arc Loading Problem (WRALP). That is the design of a direct path for each request in a communication network, in such a way that high load on the arcs will be avoided, where an arc is an edge endowed with a direction. The load of an arc is defined as the total weight of those requests routed through the arc in its direction. WRALP ask for a routing scheme such that the maximum load on the arcs will be minimum. In this paper we study the loading problem without demand splitting and for solving it we propose a Hybrid Scatter Search (HSS) algorithm. Coupled with the Scatter Search algorithm we use a Tabu Search algorithm to locate the global minimum. We show that HSS is able to achieve feasible solutions to WRALP instances, improving the results obtained by previous approaches.

Related Ontology Subjects/Areas/Topics (10)

Artificial Intelligence	Evolutionary Computing	Intelligent Control Systems and Optimization
Bio-inspired Hardware and Networks	Genetic Algorithms	Soft Computing
Co-Evolution and Collective Behavior	Informatics in Control, Automation and Robotics	Swarm/Collective Intelligence
Computational Intelligence

AN APPLICATION OF THE STUDENT RELATIONSHIP MANAGEMENT CONCEPT

Topic: Knowledge Management

Authors: Maria Beatriz Piedade ¹ and Maribel Yasmina Santos ²

Affiliations: ¹ Polytechnic Institute of Leiria, Portugal , ² University of Minho, Portugal

Digital Object Identifier: 10.5220/0002002003600367

Proceedings of ICEIS 2009, ISBN: 978-989-8111-85-2

Quick Abstract

It is largely accepted that a way to promote the students’ success is by implementing processes that allow the students closely monitoring, the evaluation of their success and the approximation to their day-by-day activities. However, the implementation of these processes does not take place in many Higher Education Institutions due to the lack of appropriates institutional practices and an adequate technological infrastructure able to support these practices. In order to overcome these conceptual and technological limitations, this paper presents the Student Relationship Management System (SRM System). The SRM System supports the SRM concept and the SRM practice, also here presented, and it is implemented using the technological infrastructure that supports the Business Intelligence (BI) systems. The SRM system was used in an application case (in a real context) to obtain knowledge about the students and their academic behaviour. Such information is fundamental to support the decision-making associated with the teaching-learning process. All the obtained results are also presented and analysed in this paper.

Related Ontology Subjects/Areas/Topics (9)

Biomedical Engineering	Health Information Systems	Ontologies and the Semantic Web
Data Engineering	Information Systems Analysis and Specification	Society, e-Business and e-Government
Enterprise Information Systems	Knowledge Management	Web Information Systems and Technologies

BUSINESS INTELLIGENCE IN HIGHER EDUCATION - Managing the Relationships with Students

Topic: Business Intelligence Applications

Authors: Maria Beatriz Piedade ¹ and Maribel Yasmina Santos ²

Affiliations: ¹ Polytechnic Institute of Leiria, Portugal , ² University of Minho, Portugal

Digital Object Identifier: 10.5220/0002302602970302

Proceedings of KDIR 2009/IC3K, ISBN: 978-989-674-011-5

Quick Abstract

The closely monitoring of the students’ academic activities, the evaluation of their academic success and the approximation to their day-by-day academic activities are key factors in the promotion of the student’s academic success in higher education institutions. To be possible the implementation of monitoring processes and activities, it is essential the acquisition of knowledge about the students and their academic behaviour. This knowledge supports the decision-making associated with teaching-learning process, enhancing an effective institution-student relationship. This paper presents a Student Relationship Management (SRM) system that is under development. The SRM system supports the SRM concept and practice and has been implemented using concepts and technologies associated to the Business Intelligence systems. To demonstrate the SRM system relevance in the process of acquisition of knowledge about the students and in the support of actions and decisions based on such knowledge, an application case carried out in a real context is also presented.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Knowledge Discovery and Information Retrieval		Symbolic Systems
Business Intelligence Applications		Knowledge-Based Systems

DATABASE MARKETING PROCESS SUPPORTED BY ONTOLOGIES - System Architecture Proposal

Topics: Intelligent Tutoring Systems; Intelligent Agents; Ontology Engineering; Knowledge-Based Systems Engineering; Knowledge Management

Authors: Filipe Mota Pinto ¹ , Alzira Marques ¹ and Manuel Filipe Santos ²

Affiliations: ¹ Instituto Politécnico de Leiria, Portugal , ² Escola de Engenharia, Universidade do Minho, Portugal

Digital Object Identifier: 10.5220/0002011704070410

Proceedings of ICEIS 2009, ISBN: 978-989-8111-86-9

Quick Abstract

This work proposes an ontology based system architecture which works as developer guide to a database marketing practitioner. Actually marketing departments handles daily with a great volume of data which are normally task or marketing activity dependent. This sometimes requires specific knowledge background and framework. This article aims to introduce an unexplored research at Database Marketing: the ontological approach to the Database Marketing process. Here we propose a generic framework supported by ontologies and knowledge extraction from databases techniques. Therefore this paper has two purposes: to integrate ontological approach in Database Marketing and to create domain ontology with a knowledge base that will enhance the entire process at both levels: marketing and knowledge extraction techniques. Our work is based in the Action Research methodology. At the end of this research we present some experiments in order to illustrate how knowledge base works and how can it be useful to user.

Related Ontology Subjects/Areas/Topics (27)

Agents	Informatics in Control, Automation and Robotics	Knowledge Management
Artificial Intelligence	Information Systems Analysis and Specification	Knowledge-Based Systems
Artificial Intelligence and Decision Support Systems	Information Technologies Supporting Learning	Knowledge-Based Systems Applications
Biomedical Engineering	Intelligent Agents	Ontologies and the Semantic Web
Computer-Supported Education	Intelligent Control Systems and Optimization	Ontology Engineering
Data Engineering	Intelligent Tutoring Systems	Society, e-Business and e-Government
e-Learning	Internet Technology	Software Engineering
Enterprise Information Systems	Knowledge Engineering	Symbolic Systems
Health Information Systems	Knowledge Engineering and Ontology Development	Web Information Systems and Technologies