Lowell 2003 Challenges Alon Y. Halevy University of Washington

The KR/DB Impedance Mismatch We often feel that knowledge and inference would be helpful. E.g., semantic query optimization, web search, data integration, meta-data management, wrapper construction. To date, most KR/DB work has been quite theoretical (data models, query containment, integrity constraint reasoning). Why haven ’ t database systems benefited more from KR technology? One explanation: there is an impedance mismatch: Classical KR systems provide pure logical reasoning. DB applications often need knowledge to rank multiple plausible answers/plans/transformations …

Going Forward The good news from KR: Probabilistic reasoning answers questions such as “ what is the probability of X? ”, With machine learning it is possible to create models from data, rather than brittle hand-crafting. The DB community needs to define the needs (API) Example: Self-tuning systems Too many knobs to set. Took several years to understand a small subset (index selection). We need a giant rule set, but the rules need to handle uncertainty. Need to learn this rule set from data. Other applications: web querying (focused crawling), user interfaces, schema matching, and lifelong personal data management.

Lifelong Personal Data Management Save all the stuff I ever care(d) about (contacts, grades, boy scout assignments, stock portfolio, files, talks, restaurant reviews) Challenges: Our schema evolves (radically) over time. Data management systems change constantly. Our focus of attention changes. Find data in your information attic: find the photo of the girl next door from 8 th grade. Need to combine text and structured data, and make it accessible to everyone.