Browsing by Author "Santos, Manuel Filipe"
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- Adaptive knowledge discovery for decision support in intensive care unitsPublication . Gago, Pedro; Santos, Manuel FilipeClinical Decision Support Systems (CDSS) are becoming commonplace. They are used to alert doctors about drug interactions, to suggest possible diagnostics and in several other clinical situations. One of the approaches to building CDSS is by using techniques from the Knowledge Discovery from Databases (KDD) area. However using KDD for the construction of the knowledge base used in such systems, while reducing the maintenance work still demands repeated human intervention. In this work we present a KDD based architecture for CDSS for intensive care medicine. By resorting to automated data acquisition our architecture allows for the evaluation of the predictions made and subsequent action aiming at improving the predictive performance thus enhancing adaptive capacities.
- Closed loop knowledge discovery for decision support in intensive care medicinePublication . Gago, Pedro; Santos, Manuel FilipeClinical Decision Support Systems (CDSS) are becoming commonplace. They are used to alert doctors about drug interactions, to suggest possible diagnostics and in several other clinical situations. One of the approaches to building CDSS is by using techniques from the Knowledge Discovery from Databases (KDD) area. However using KDD for the construction of the knowledge base used in such systems, while reducing the maintenance work still demands repeated human intervention. In this work we present a KDD based architecture for CDSS for intensive care medicine. By resorting to automated data acquisition our architecture allows for the evaluation of the predictions made and subsequent action aiming at improving the predictive performance thus closing the KDD loop.
- Considering application domain ontologies for data miningPublication . Mota Pinto, Filipe; Santos, Manuel FilipeThe dramatically explosion of data and the growing number of different data sources are exposing researchers to a new challenge - how to acquire, maintain and share knowledge from large databases in the context of rapidly applied and evolving research. This paper describes a research of an ontological approach for leveraging the semantic content of ontologies to improve knowledge discovery in databases. We analyze how ontologies and knowledge discovery process may interoperate and present our efforts to bridge the two fields, knowledge discovery in databases and ontology learning for successful database usage projects.
- Database marketing intelligence supported by ontologiesPublication . Pinto, Filipe; Santos, Manuel Filipe; Marques, Alzira; Mota Pinto, Filipe; Marques, AlziraMarketing departments handles with a great volume of data which are normally task or marketing activity dependent. This requires the use of certain, and perhaps unique, specific knowledge background and framework. This article aims to introduce an almost unexplored research at marketing field: the ontological approach to the Database Marketing process. We propose a framework supported by ontologies and knowledge extraction from databases techniques. Therefore this paper has two purposes: to integrate the ontological approach into Database Marketing and to create a domain ontology, a knowledge base that will enhance the entire process at both levels, marketing and knowledge extraction techniques. In order to structure and systematize the marketing concepts, Action Research methodology has been applied. At the end of this research the ontologies will be used to pre-generalize the Database Marketing knowledge through a knowledge base.
- Database marketing process supported by ontologies: System architecture proposalPublication . Pinto, Filipe Mota; Marques, Alzira; Santos, Manuel FilipeThis 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.
- Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive CarePublication . Gago, Pedro; Santos, Manuel FilipeThe huge amount of data available in an Intensive Care Unit (ICU) makes ICUs an attractive field for data analysis. However, effective decision support systems operating in such an environment should not only be accurate but also as autonomous as possible, being capable of maintaining good performance levels without human intervention. Moreover, the complexity of an ICU setting is such that available data only manages to cover a limited part of the feature space. Such characteristics led us to investigate the development of ensemble update techniques capable of improving the discriminative power of the ensemble. Our chosen technique is inspired by the Dynamic Weighted Majority algorithm, an algorithm initially developed for the concept drift problem. In this paper we will show that in the problem we are addressing, simple weight updates do not improve results, whereas an ensemble, where we allow not only weight updates, but also the creation and eliminations of models, significantly increases classification performance.
- Marketing database knowledge extraction - towards a domain ontologyPublication . Pinto, Filipe Mota; Gago, Pedro; Santos, Manuel Filipe; Mota Pinto, Filipe; Gago, PedroOntologies are currently the most prominent computer science research area under development. With this paper we use ontologies at an almost unexplored research area within the marketing discipline, throughout ontological approach to the database marketing. We propose a generic framework supported by ontologies for the knowledge extraction from marketing databases. Therefore this work 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. This research was developed according to two methodological principles, ontology domain double articulation and ontology modularization. At the end of our work we use ontologies to pre-generalize the Database Marketing knowledge through a knowledge base.
- Ontological assistance for knowledge discovery in databases processPublication . Pinto, Filipe Mota; Santos, Manuel FilipeThe dramatically explosion of data and the growing number of different data sources are exposing researchers to a new challenge- how to acquire, maintain and share knowledge from large databases in the context of rapidly applied and evolving research. This paper describes a research of an ontological approach for leveraging the semantic content of ontologies to improve knowledge discovery in databases. We analyze how ontologies and knowledge discovery process may interoperate and present our efforts to bridge the two fields, knowledge discovery in databases and ontology learning for successful database usage projects.
- Ontology-supported database marketingPublication . Pinto, Filipe Mota; Marques, Alzira; Santos, Manuel Filipe; Mota Pinto, Filipe; Marques, AlziraDatabase marketing (DBM) provides in -depth analysis of marketing databases. Knowledge discovery in database techniques is one of the most prominent approaches to supporting some of the DBM process phases. However, in many cases, the benefits of these tools are not fully exploited by marketers. Complexity and amount of data constitute two major factors limiting the application of knowledge discovery techniques in marketing activities. Currently, ontologies may here play an important role in the marketing discipline. Motivated by its success in the area of artificial intelligence, we propose an ontology-supported DBM approach. The approach aims to enhance DBM with ontology by providing detailed step-phase specific information. Our research work has its foundations in a double methodological approach using the Delphi and Action Research methodologies. First, we use Delphi to structure related DBM knowledge, and then we align our work to the Action Research methodology in order to systematise the knowledge extraction process and knowledge base creation. The issues raised in this paper both respond and contribute to calls for a DBM process improvement. Our work was evaluated in the relationship marketing domain focusing on a relational marketing programme database. The findings of this study not only advance the state of DBM research, but also shed light on future research directions.