Efficient Profile Indexing Algorithms for Information Dissemination

The quantity of information in the web is huge and rapidly increasing. People that want to use this information have to cope with the problem of information overload. As a result, information querying and retrieval relies on search engines and other specialized systems designed for this task. However, the user still has to spend much time in order to seek for the information he (or she) is interested in and to filter out unwanted information. A solution to this problem is the use of selective dissemination of information. The idea of selective information dissemination is that users express their desire and preferences for information by posting profiles (or long-standing queries) to a computer system. The system informs the user about any incoming information matching his (or her) profile. This dissertation deals with the problem of the dissemination of textual information. More specifically, we study the data models and query languages suitable for such an application. Next, we propose some main memory algorithms that support these languages. We implement these algorithms and calculate their space and time complexities. Finally, we end up with their experimental evaluation.