Lowell 2003 Challenges

1
Lowell 2003 Challenges

• Alon Y. Halevy
• University of Washington

2
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 havent 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

3
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.

4
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 8th grade.
• Need to combine text and structured data, and
  make it accessible to everyone.