Business Data Mining

We have participated in the 9th edition of International Conference “Advanced Information Technologies for Management AITM 2011”.

The main purpose of the conference is to provide a forum for researchers and practitioners to present and discuss the current issues of IT in business applications. There will be also the opportunity to demonstrate by the software houses and firms their solutions as well as achievements in management information systems.

This conference was organized by Department of Information Technologies. Our great partner of this event was Teta Business Intelligence.

For more details and current topics please visit conference site.

We have extended the list of our projects with "Job Shop Scheduling and Project Management" and "Discovery of Customer Patterns".

There is also a new paper "FP-Growth in Discovery of Customer Patterns" by Korczak and Skrzypczak. New downloadable papers will be available soon.

At the SPUDM23 conference, in Kingston Upon Thames (London) (http://spudm23.eadm.eu/) Aleksander Fafula will give presentation about latest improvements related to detection of cognitive biases.

Short paper will be available shortly. In the meantime consult Behavioral Indicators page.

In recent years there has been an increasing interest in financial institutions and individual investors with modern methods and technologies supporting decision-making processes on stock markets. The problem of modeling trading strategy is not new. Many methods and tools which are
often not only failed in practice, but also are difficult to understand for a growing group of individual investors.

The main objective of the project is to disseminate modern technologies in stock trading using data-driven approaches, large financial databases and information technology. Main attention is the decision by investors using publicly available databases of financial markets. Among others, in this project it will be developed behavioral models of individual investors.
This work will be done on extensive databases containing historical data of buy-sell trading decisions of investors. In addition, analytical models will be used, as well as information published on Web pages and highly frequent time series.

The area of research will include the following issues:

- modeling of investment decisions with the use of econometric methods,
the theory of evolution and neural networks,
- evaluation of technological constraints of accessing Internet databases,
- analysis of erroneous decisions and frequency of transactions;
- performance analysis of market models,
- application of mobile technology in the analysis of economic data.