Knowledge Discovery and Business Intelligence

Because of advances in Information Technology, nowadays it is easy to collect, store, process and share data. In effect, the amount of data stored by organizations or individuals is estimated to be growing exponentially (Lyman, 2003). Although Expert Systems were originally devised solely mimic human experts (Buchanan, 1986), there is a pressure to extract as much useful information as possible from past data. Hence, the current tend to include data-driven models, possibly integrated with expert-driven models, into the decision-making process. Within this context, there are two relevant terms: Knowledge Discovery (KD) and Business Intelligence (BI). KD is a branch of the Artificial Intelligence field that aims to extract useful and understandable high-level knowledge from complex and/or large volumes of data (Fayyad et al., 1996). BI is an umbrella term that represents several computer architectures, tools, technologies and methods (e.g. data warehousing, online analytical processing and KD) to access past data and support decision-making in public and corporate enterprises, from operational to strategic level (Turban et al., 2010). The KD and the BI are faced with new challenges. For instance, recent communication technologies, such as WiMAX, lead to interesting network optimization problems, which can be solved using a KD approach. Also, when adopting fuzzy clustering methods, it is not clear how to correctly identify the ideal number of clusters. Moreover, a large effort has been put on static analysis of social networks. Yet, these social networks exist in time and thus may evolve under a dynamic environment. Furthermore, although there are several public dictionaries with synonymy information, the building of thesauri often requires manual work and is usually incomplete. This special issue, entitled ‘Knowledge Discovery and Business Intelligence’ (KDBI), focuses on newKDapproaches that aim to solve new challenges (such as previously described), leading to a potential valuable impact in several BI domains. This special issue consists of extended versions of papers from the second KDBI workshop of the 15th Portuguese Conference on Artificial Intelligence (EPIA 2011) which was held in Lisbon, Portugal. A total of 27 papers were submitted to the second KDBI workshop, from which the best 10 papers were invited for this special issue. Each extended paper was reviewed by a minimum of three reviewers (related to both the second KDBI workshop and the Expert Systems journal), and passed through two rounds of reviews. Finally, the best four papers were accepted, corresponding to an acceptance rate of 15%, when considering the initial second KDBI submitted papers, and 40%, when considering the extended invited papers. In the next section, we provide a brief introduction to these papers. 2.