1. “Artificial Intelligence” in Database Querying Dept. of CSE Seung-won Hwang

2. Why do you need to ace this class? “producing machines to automate tasks requiring intelligent behavior” (wikipedia) AI techniques are highly relevant to many research fields, including database

3. More obvious applications

4. But…

5. Crash course on DB SQL queries select * from cars where color=‘red’ and type=‘convertible’ and brand=`hyundai’

6. Crash course on DB Deciding the most efficient execution plan among:  hyundai->red->convertible?  red->convertible->hyundai?  convertible->hyundai->red? …… Depends on data structures (B+-tree), data distributions, … However, all these efforts are useless efforts, if no object qualifies

7. Our strength

8. Internet shopping, web bulletin board, cyworld, … You are sending SQL queries without you knowing (at least until you see DB errors) DBMS is optimizing your query for you without you knowing

9. Our weakness But do you use DBMS for managing your word files, photos, etc.. What do you use?  File system (Browsing)  Google desktop (Searching) SQL semantics is too strict  No red hyundai convertible! Or too many red hyundai elantra?

10. While Google makes $$$ for

11. Giving “Artificial Intelligence” What are the intelligent behaviors expected?  Suggesting alternatives: Red hyundai Red convertible Orange convertible What are the possible automation?  Deciding Red hyundai < Red convertible

12. But how? Any idea? Underspecified/Overspecified Queries GAP

13. [S1] Borrowing wisdom from data (as google does) Useful for both too many or empty results

14. Text ranking tf (term frequency): how often query term appears in document idf (inverse document frequency): how rare query term is in document collection hyundai red convertible red convertible high tf cars.com low idf red

15. Applying to database brandidfcoloridf hyundai0.5black0.1 BMW0.8red0.4 kia0.3purple0.9 Red hyundai = 0.9 Red honda = 0.4 Black hyundai = 0.8

16. What is the assumption? Rare items are preferred Can you think of exceptions?  ‘purple pony’ vs. ‘purple lexus’ How can we handle this problem?

17. [S2] Borrowing wisdom of other users

18. Query frequency Keyword frequency in prior queries  Eg., car=‘BMW’ appearing in 50% of prior queries Summing up, we can highly rank cars that are heavily queried before and rare in stocks

19. [S3] Borrowing wisdom from domain knowledge

20. Example 1: color (a) (b) (c)(d) (e)

21. Example 2: shape=‘retro’

22. [S4] Borrowing wisdom from specific user Notion of similarity significantly differs across users Shape? A B C

23. You cannot expect users to describe (or machine to understand) explicit explanation like I want a photo of a building similar to eiffel tower in terms of shape, but not in terms of the overall shape, but in terms of the shape of the steel material…………..

24. Mindreader? (mediabakery.com)

25. In our car search example You can show ‘red bmw’ and ‘hyundai sedan’ Based on user response (or clicks), you can figure out which is more important factors, e.g., color Then you can show more red cars to figure out further on preference on brands

26. Summing up You need to bridge the gap between SQL and ideal results, by collecting/analyzing as much as information available from data, prior users, user himself/herself, … Implicitly and automatically

27. Another implicit info to think about Tagging frequency ranking/ automatic classification?

28. Summary Networks enables access to a large amount of user created contents/info “Web 2.0”  http://youtube.com/watch?v=6gmP4nk0EOE (interesting web 2.0 video) http://youtube.com/watch?v=6gmP4nk0EOE Intelligent retrieval techniques is the key in new era  Ranking  Classification I will then show how AI techniques (that you already know!) got me a PhD in intelligent retrieval research  Rank Formulation: machine learning  Rank/Classification Processing : best first search, hill climbing

29. Q&A