Knowledge discovery generally focuses on finding patterns within a reasonably well connected domain of interest. In this article we outline a framework for the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a more powerful way. We motivate this approach, show the difference to classical data analysis and conclude by describing...

Coping with complex and dynamic environments forces companies to invest in the resource knowledge for the maintenance of their operational reliability; they have to create and to renew the resource knowledge continuously. Elementary activities of knowledge management comprise collecting, organizing, connecting, sharing, and applying of knowledge. A necessary prerequisite before the outcome of k...

Scopus is a well known repository of metadata about scientific research articles. In this work, we gather data from this repository to create a social graph of scientific authors, starting from citations among their articles. Moreover, using data mining techniques, we infer some relevant research topics for each author, from the textual analysis of the abstracts of his articles. As a case study...

Knowledge discovery and data mining deal with the task of finding useful information and especially rules in unstructured data. Most knowledge discovery approaches associate conditional probabilities to discovered rules in order to specify their strength. In this paper, we propose a qualitative approach to knowledge discovery. We do so by abstracting from actual probabilities to qualitative inf...

* " Universal knowledge " also can be named as " pervasive knowledge / information " or " ubiquitous knowledge / information ". Abstract: To overcome the shortcomings of knowledge discovered by traditional data mining methods, I argue that we should follow the paradigm shift from " traditional data mining " to " wisdom mining " in the era of big data, and propose Universal Knowledge Discovery f...

Uninformed or blind search, which processes and evaluates all nodes of a search space in the worst case, is not realistic for extracting knowledge from large data sets because of time constraints that are closely related to the dimension of the data. Generally, the search space increases exponentially with problem size, thereby limiting the size of problems that can realistically be solved usin...

With the flood of information on the Web, Web mining is a new research issue which draws great interest from many communities. Currently, there is no agreement about Web mining yet. It needs more discussion among researchers in order to define what it is exactly. Meanwhile, the development of Web mining system will promote its research in turn. In this paper we present a preliminary discussion ...

Knowledge reduction, includes attribute reduction and value reduction, is an important topic in rough set literature. It is also closely relevant to other fields, such as machine learning and data mining. In this paper, an algorithm called TWI-SQUEEZE is proposed. It is so named because it can find a reduct, or an irreducible attribute subset that maintains certainty of classification, after tw...

Over the last years, ubiquitous computing has started to create a new world of small, heterogeneous, and distributed devices that have the ability to sense, to communicate and interact in ad hoc or sensor networks and peer2peer systems. These large scale distributed systems have in many cases to interact in real-time with their users. Knowledge Discovery in ubiquitous environments (KDubiq) is a...