2.  Motivation  Reverse Queries  From Reverse to Inverse  Inverse Queries  Formal Definition  Applications  Framework  Experiments  Future Extensions 2

3. Reverse Queries 3

4. Reverse kNN Queries 4

5. 5 q k=1

6. Reverse kNN Queries 6 q k=1

7. From Reverse to Inverse 7

8. Inverse Queries 8

9. 9

10. 10

11.  Consider a movie database containing a large number of movie records.  Each movie record contains features such as humor, suspense, romance, etc. 11 humor

12.  Users of the database are represented by the same attributes, describing their preferences.  Assume that a group of users, such as a family, want to watch a movie together 12 humor

13.  Find movies sufficiently close to ALL users preferences 13 humor

14.  Find movies sufficiently close to ALL users preferences 14 humor Recommend me!

15.  Assume that a set of households and their spatial coordinates. Applications: Mono-Chromatic Inverse kNN query 15

16.  Some households have been robbed in short succession and the robber must be found. Applications: Mono-Chromatic Inverse kNN query 16

17.  Assume that the robber will only rob houses which are in his close vicinity, e.g. within the closest hundred households.  An inverse 100NN query returns a list of possible suspects Applications: Mono-Chromatic Inverse kNN query 17

18.  Assume that the robber will only rob houses which are in his close vicinity, e.g. within the closest hundred households.  An inverse 100NN query returns a list of possible suspects Applications: Mono-Chromatic Inverse kNN query 18 Suspects!

19.  An online-shop wants to recommend items to their customers by analyzing other items clicked by them.  Clicked items Q seen as samples of products the customer is interested in, and thus, is assumed to be in the customer’s dynamic skyline. Applications: General Inverse Dynamic Skyline Query 19

20.  The inverse dynamic skyline query can be used to narrow the space which the customers preferences are located in.  Case |Q|=1: Reverse Dynamic Skyline Query. 1 Applications: General Inverse Dynamic Skyline Query 20 1 Evangelos Dellis, Bernhard Seeger: Efficient Computation of Reverse Skyline Queries. VLDB 2007

21.  Case: Q>1 Applications: General Inverse Dynamic Skyline Query 21

22. Filter-Refinement Framework 22

23. Experiments 23

24. Future Directions 24

25. 25