Context awareness is critical for the successful execution of processes. In the abundance of business process management (BPM) research, frameworks exclusively devoted to extracting context from textual process data are scarce. With the deluge of textual data and its increasing value for organizations, it be-comes essential to employ relevant text analytics techniques to increase the awareness of business process (BP) workers,...

Recommender systems are software tools and techniques which aim at suggesting new items that may be of interest to a user. This dissertation is focused on four problems in recommender systems domain. The first one is context-awareness, i.e. how to obtain relevant contextual information, how to model user preferences in a context and use them to make predictions. The second one is multi-domain recommendation, which aim at suggesting...

This study examines the scholarly communication activities in two scientific fields, chemistry and economics, in a Polish context. The dissertation aims at investigating what characterises the publishing choices of researchers and what supplementary forms for the dissemination of research output are used by scholars. In addition, the paper is looking into what are the views and awareness of open access and institutional repositories...

A proposed Internet systems security layer with context-oriented security mechanisms reduces the risk associated with possible vulnerabilities. A metric of the system trust level is proposed, and then evaluated according to a university Internet system.

Exploiting contextual information is considered a good solution to improve the quality of recommendations, aiming at suggesting more relevant items for a specific context. On the other hand, recommender systems research still strive for solving the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we propose a new rating prediction algorithm to face the cold-start...