RESEARCH

Research Projects

• Adam - Adaptive Associative Memories for Active Data Streams (2005-2008)
• Evo-Business - Evolutionary Computing for E-business (2003-2006)
• ICC - Inventing Communities of Conviviality (2006-2009)
• Intra - Internet Traffic Management and Computer Network Protection (2003-2006)
• Trias - Logic of Trust and Reliability of Information Agents in Science (2005-2008)

Research Topics

Explorative-Adaptive MindMap (EAMM)

In our understanding, a mind-map is an adaptive engine that basically works incrementally on the fundament of existing transactional streams. Generally, mind-maps consist of symbolic cells that are connected with each other and that become either stronger or weaker depending on the transactional stream. Based on the underlying biologic principle, these symbolic cells and their connections as well may adaptively survive or die, forming different cell agglomerates of arbitrary size. Beside the architecture of mind-maps, we intend to prove mind-maps' eligibility following diverse application scenarios, for example being an underlying management system to represent normal and abnormal traffic behaviour in computer networks, supporting the detection of the user behaviour within search engines, or being a hidden communication layer for natural language interaction. (See EAMM) Contact: Jayanta Poray, Christoph Schommer

Artificial Conviviality

We want to motivate the idea of conviviality in web portals and argue that a convivial social being deeply depends on the implicit and explicit co-operation and co-laboration of natural users inside a community. Our belief is that an individual conviviality benefits from the wisdom of the crowd, meaning that a continuously and dynamic understanding of the user's behaviour heavily influences the inidividual well-being. For this, we work on the system CUBA, which stands for Conviviality and User Behavior Analysis. CUBA is to find novel ways to support an users during their visit while discovering their interests. In this respect, CUBA comes up with certain recommendations and suggestions, which are partially based on a common behavior of participants in general. For example, concepts like time, space, and diverse user-based actions are taken into account. Contact: Sascha Kaufmann, Christoph Schommer

Bibliographic Databases and Social Communities

We propose a new perspective for the data analysis in digital libraries, bibliographic and other databases containing personal names. Knowing language/cultural background of a person can be beneficial in many applications, however this information is often not present explicitly in the databases.
We are working on a statistical tool for the automatic language detection of personal names. Our system does not require a dictionary of names for training and handles 14 different languages so far. General purpose corpora for all Western European, Chinese, Japanese and Turkish languages are used in order to build simple statistical models of the languages. The tool is fine tuned to achieve precision and recall above 90% for many languages which proves better performance than some other systems aiming at the language identification of personal names. On an example of a bibliographical database DBLP we show how our tool can be used in tasks such as data cleaning and discovery of trends. Another point is that we describe how the co-author network, which is built from the bibliographic records, can be incorporated into the process of personal name language classification. The model is tested on the DBLP data set. The results show that the extension of the language classification process with the co-author network may help to refine the name language classification obtained from the author names considered independently. It may also lead to the discovery of dependencies between the elements of the co-author network, or participation of authors in scientific communities. For more information on that please see MSC. Contact: Maria Biryukov, Christoph Schommer

Data Mining - a Cognitive Process

Since many years, theoretical concepts of Data Mining have been developed and improved. Data Mining has become applied to many academic and industrial situations, and recently, soundings of public opinion about privacy have been carried out. However, a consistent and standardized definition is still missing, and the initial explanation given by Frawley et al. has pragmatically often changed over the years. Furthermore, alternative terms like Knowledge Discovery have been conjured and forged, and a necessity of a Data Warehouse has been endeavoured to persuade the users. In this work, we pick up current definitions and introduce an unified definition that covers existing attempted explanations. For this, we appeal to the natural original of chemical states of aggregation. Contact: Christoph Schommer