Data Research Vision
1 like327 views
Marko Grobelnik discusses the integration of three scientific paradigms—top-down, bottom-up, and collaborative approaches—in the context of data processing and analysis. This integration opens new possibilities for solving previously challenging problems in fields such as data mining, machine learning, and semantic web. He emphasizes that future innovations will emerge from exploring the evolving aspects of the data quality cube.
Data Research Vision
- 1. Marko Grobelnik (marko.grobelnik@ijs.si) Jozef Stefan Institute (http://www.ijs.si/) Ljubljana, Slovenia
- 2. Dealing with data Natural Databases Language Machine Social Semantic Web Processing Learning / Information Network Data Mining Retrieval Analysis Storing / Model querying Community Interoperability Text discovery Search
- 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. Research areas (such as IR, KDD, ML, NLP, Usage SemWeb, …) are sub- cubes within the data Quality cube Context Dynamicity Scalability
- 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