New search tools help separate the wheat from the (data) chaff
working time sifting through customer, supplier, and specialist databases or text documents in search of specific information. The ConWeaver search engine can automatically link heterogeneous corporate know-how and make it available for use in business processes. A single entry is enough, and the software searches all the different data sources in a company. ConWeaver includes the term entered by the user, and also its translation in other languages and any given thematic relationships in the search process. On the basis of company data, the engine automatically generates a semantic knowledge network which enables it to recognize that the word “customer” in the sales database, for example, is synonymous with the German word “Kunde” in the e-mail archive and “orderer” in the project documents. “Unlike conventional search engines, ConWeaver establishes a connection between different data formats,” says Thomas Kamps. “This means that the software can efficiently search both structured and unstructured information sources.”
Then there is visual analytics. Being able to find the right information in large volumes of data is one thing. Presenting it in a user-friendly way is a challenge of a different order. The more extensive the information, the more difficult it is to maintain a clear overview. A team led by Dr. Jörn Kohlhammer at the IGD is combining automatic data analysis with novel methods of visualization. The researchers are using the various capabilities of computers and people. The computer’s task is sequentially to process large volumes of data and convert them into a visual form of representation that people can perceive and understand. The user can then focus on recognizing patterns, and evaluating and analyzing the observed data. “This process involves very close interaction between man and computer,” says Jörn Kohlhammer, “but man always has priority. It is always the user who decides, not the system.”
Visualizations of this type are of particular interest to financial service providers, for example. The data they have to deal with are usually so substantial and varied that it is impossible to carry out conclusive analyses in a short space of time. Visual representations help to make things clearer. If an evaluation of corporate shareholder structures, for example, is presented on the screen in an intuitively comprehensible form instead of in dry numerical tables, the analyst can quickly and accurately draw conclusions from it. Other visualization techniques make it possible to observe the stock quotations of numerous companies simultaneously, and to draw conclusions from previous developments. This often visually highlights correlations that would otherwise be lost in a maze of numbers.
