IDS Group deals with technologies, tools, models and techniques related to the Data Mining and Data Warehousing Systems. The main objective is the research in knowledge areas such as Adaptive Business Intelligence, Intelligent Decision Support Systems, Data Mining, Intelligent Data Analysis, Data Warehouse And OLAP.
The research addresses real-time, online, distributed and complex problems, taking in account emergent issues as is the Web, eBusiness, multi-agent systems, grid and cloud computing, and considering various types of data.
Special attention is given to the acquisition, archive, processing and diffusion of competitive/complex information, data mining and representation in order to support management, decision and planning tasks.
Some global I&D proposes/lines can be identified:
- Automate the Knowledge Discovery from Database process in order to minimize the need of problem domain knowledge;
- Create Data Mining open source components to be used by the research community in advanced applications;
- Investigate new methods for data mining in distributed environments/problems;
- Design of Intelligent Decision Support Systems recurring to Multi-agent Systems and KDD approaches;
- Augment the existing Data Mining techniques in order to cover new types of problems;
- Novel BI approaches to solve new types of problems.
IDS leader: Manuel Filipe Santos
Recent IDS Publications:
- Sentiment Classification of Consumer-Generated Online Reviews Using Topic Modeling
Calheiros, A.C.; Moro, S.; Rita, P.
Journal of Hospitality Marketing and Management, 2017
- Step towards improving the voluntary interruption of pregnancy by means of business intelligence
Brand\~ao, A.; Portela, F.
Applying Business Intelligence to Clinical and Healthcare Organizations, 2016
- Stock market sentiment lexicon acquisition using microblogging data and statistical measures
Oliveira, N.; Cortez, P.; Areal, N.
Decision Support Systems, 2016
- Risk-oriented feedforward control mechanism on business processes compliance | Mecanismo de controlo para a frente orientado ao risco como garantia da conformidade da execução de processos de negócio
Marques, R.P.; Guerreiro, S.
RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2016
- Senior Potential Analysis: A Challenge that Contributes to Social Sustainability
Guarda, Teresa; Pinto, Filipe Mota; Cordova, Juan Pablo; Augusto, Maria Fernanda; Mato, Fernando; Quina, Geovanni Ninahualpa; Gervasi&a
Computational Science and Its Applications - Iccsa 2016, Pt I, 2016
- Multiobjective Optimization of Maintenance Scheduling: Application to Slopes and Retaining Walls
Denysiuk, R.; Matos, J.C.; Tinoco, J.; Miranda, T.; Correia, A.G.
Procedia Engineering, 2016
- Pervasive Business Intelligence: A New Trend in Critical Healthcare
Pereira, A.; Portela, F.; Santos, M.F.; Machado, J.; Abelha, A.
Procedia Computer Science, 2016
- Behavioural biometrics for authentication and stress detection – A case study with children
Azevedo, A.I.; Santos, H.D.; S\'a, V.J.; Lopes, N.V.
Communications in Computer and Information Science, 2016
- Wargames applied to naval decision-making process
Guarda, T.; Vaca, O.B.; Pinguave, M.P.; Maldonado, E.P.; Augusto, M.F.
Advances in Intelligent Systems and Computing, 2017
- Local authorities and the disclosure of financial information via the internet: The Portuguese case
Mendes, H.C.A.; Santos, C.; Ferreira, A.C.S.; Marques, R.P.F.; Do Car
Global Perspectives on Risk Management and Accounting in the Public Sector, 2016
- An application of Markov chains to predict the evolution of performance indicators based on pavement historical data
Moreira, A.V.; Tinoco, J.; Oliveira, J.R.M.; Santos, A.
International Journal of Pavement Engineering, 2016
- Pervasive business intelligence as a competitive advantage
Guarda, T.; Pinto, F.M.; Cordova, J.P.; Mato, F.; Quina, G.N.; Augusto&a
Iberian Conference on Information Systems and Technologies, CISTI, 2016
- Forecasting tomorrow’s tourist
Moro, S.; Rita, P.
Worldwide Hospitality and Tourism Themes, 2016
- Why Big Data? Towards a Project Assessment Framework
Portela, F.; Lima, L.; Santos, M.F.
Procedia Computer Science, 2016
- Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete
Coelho, M.R.F.; Sena-Cruz, J.M.; Neves, L.A.C.; Pereira, M.; Cortez, P
Construction and Building Materials, 2016