Efficient algorithms for collaborative decision making for large scale settings

• Authors: Ira Assent
• Venue: Proceedings of the 3rd international workshop on Collaborative information retrieval
• Year: 2011

Collaborative decision making is a successful approach in settings where data analysis and querying can be done interactively. In large scale systems with huge data volumes or many users, collaboration is often hindered by impractical runtimes. Existing work on improving collaboration focuses on avoiding redundancy for users working on the same task. While this improves the effectiveness of the user work process, the underlying query processing engine is typically considered a "black box" and left unchanged. Research in multiple query processing, on the other hand, ignores the application, and focuses on improving runtimes regardless of where the queries are issued from. In this work, we claim that progress can be made by taking a novel, more holistic view of the problem. We discuss a new approach that combines the two strands of research on the user experience and query engine parts in order to bring about more effective and more efficient retrieval systems that support the users' decision making process. We sketch promising research directions for more efficient algorithms for collaborative decision making, especially for large scale systems.