1. Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias SIGMOD 2006 1

2.  Motivation  Preliminaries  Method ◦ TMA(Top-k Monitoring Algorithm) ◦ SMA(Skyband Monitoring Algorithm)  Experimental evaluation  Conclusion 2

3.  The existing methods are inapplicable to highly dynamic environments involving numerous longrunning queries.  This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. 3

4.  f(X 1,X 2 )=X 1 +2*X 2 (0.2,1) (1,0.7) 4 (0.6,0.8)

5. 5 K-skyband :Returns those objects that are dominated by at most K-1 other objects.

6. 6

7.  f(X 1,X 2 )=X 1 +2*X 2  Top-1 7

8.  F(X 1,X 2 )=X 1 -X 2  Top-2 8

9.  P 1,P 2 expire  P 3,P 4 arrive Search influence list-> P 3 has maxscore P 3 become the result of top-1 query 9

10.  P 3 expire  P 5 arrive invokes the top-k computation module 10

11.  SMA applies the reduction from top-k to k-skyband queries in order to avoid computation from scratch when some results expire. 11

12.  DC(dominance counter)  2-skyband When DC reach 2, then delete. 12

13.  P 9 arrive 13

14.  P 9 arrive (2) (1) (2) 14

15.  P 9 arrive 15

16.  SMA is expected to be faster than TMA, since it involves less frequent calls to the top-k computation module. 16

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18.  TMA re-computes the result from scratch, whereas SMA maintains a superset of the current answer in the form of a k-skyband, in order to avoid frequent recomputations. 18