1. Marko Grobelnik (marko.grobelnik@ijs.si)marko.grobelnik@ijs.si Jozef Stefan Institute (http://www.ijs.si/)http://www.ijs.si/ Ljubljana, Slovenia

2. Dealing with data Natural Language Processing Semantic Web Information Retrieval Machine Learning / Data Mining Databases Interoperability Storing / querying Text Model discovery Search Social Network Analysis Community

3.  Integration of three key scientific paradigms ◦ Top-down approaches – model driven  (Semantic Web, KRR, Traditional NLP) ◦ Bottom-up approaches – data driven  (Machine Learning, Data Mining, Social Network Analysis, Information Retrieval, Modern NLP) ◦ Collaborative approaches – community driven  (Web2.0, Social Computing)  …integration of ideas from different paradigms opens possibilities to solve problems which were not easy solvable before

4. Scalability Dynamicity Context Quality Usage  Research areas (such as IR, KDD, ML, NLP, SemWeb, …) are sub- cubes within the data cube

6. Can we learn from listed technologies?

8. Can we learn from listed technologies?

11.  One possible conclusion: ◦ Future lies in uncovered parts of the data cube ◦ …note that items on data cube are changing  Upcoming technology trends combine existing “healthy” technologies as building blocks