1 / 26

# Dynamic Skylines Considering Range Queries - PowerPoint PPT Presentation

Dynamic Skylines Considering Range Queries. Speaker: Adam Adviser: Yuling Hsueh. 16th International Conference, DASFAA 2011. Wen-Chi Wang En Tzu Wang Arbee L.P. Chen3. INTRODUCTION. What is “Skyline” ?. INTRODUCTION. Dynamic skyline considering query

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Dynamic Skylines Considering Range Queries' - tiger

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Dynamic Skylines Considering Range Queries

16th International Conference, DASFAA 2011

Wen-Chi Wang En Tzu WangArbee L.P. Chen3

• What is “Skyline” ?

DM+  Page 2

• Dynamic skyline considering query

• Dynamic skyline query regarding query q retrieves the data points notdynamically dominated by any other data points, with respect to q.

• Dynamically dominated

• A data point t (t[1], t[2],…,t[n]) is defined to dynamically dominate another data point s(s[1], s[2],…,s[n]), with respect to query q (q[1], q[2],…,q[n]), iff

• |t[i] − q[i]| ≤ |s[i] − q[i]|, ∀ i = 1 to n, and

• at least in one dimension, say j, |t[j] − q[j]| < |s[j] − q[j]|.

DM+  Page 3

• |t[i] − q[i]| ≤ |s[i] − q[i]|, ∀ i = 1 to n, and

• at least in one dimension, say j, |t[j] − q[j]| < |s[j] − q[j]|.

DM+  Page 4

• We turn to find the skyline in a transferred dataset in which all of the data points in the original space are transferred to the other space whose origin is equal to query.

DM+  Page 5

• Query=(2000, 4), C1=(1992, 8), C2=(1995, 8), C3=(1998, 3)

• = (|1992 − 2000|, |8 − 4|) = (8, 4), = (5, 4) and = (2, 1)

DM+  Page 6

• Dynamic skyline considering range queries

DM+  Page 7

• Problem Formulation

• Given an n-dimensional dataset D and a range query q ([q1, q1'], [q2, q2'], …, [qn, qn']), where [qi, qi'] is an interval representing the user interests in the ith dimension, ∀ i = 1 to n, the dynamic skyline query regarding q returns the data points from D, not dynamically dominated by any other data points, with respect to q.

DM+  Page 8

DM+  Page 9

• query q ([15, 20], [20, 25]), p8 = (17, 30)(|17 − 17|, |30 − 25|) = (0, 5)

• P7(|25 − 20|, |25 - 25|) = (5, 0), p3(|25 − 20|, |5 − 20|) = (5, 15)

DM+  Page 10

• Data Structures Used in Algorithm

• Grid index

• Multidirectional Z-order curves

• Grid index

• Each dimension of the n-dimensional space is partitioned into b blocks, each associated with an equal domain range of r.

DM+  Page 11

DM+  Page 12

• Query cells: (3, 4), (3, 5), (4, 4), and (4, 5), range form: ([3, 4], [4, 5])

• Pivot cells:([0, 2], [4, 5]), ([5, 7], [4, 5]), ([3, 4], [0, 3]), and ([3, 4], [6, 7])

DM+  Page 13

• Z-order curve

• point (5, 4) = (101, 100)

• the Z-address of (5, 4) is (110010)

• Monotonic Ordering of Z-order curve

• a data point in a cell with a former order cannot be dominated by the data points in the cells with the latter order

DM+  Page 14

• Query (3, 4), p4 =(4, 4)(1, 0), p1 = (1, 6 )  (2, 2)

DM+  Page 15

DM+  Page 16

Dynamic Skyline Processing

• Principle of Pruning Strategies

DM+  Page 17

Dynamic Skyline Processing

• Principle of Pruning Strategies

DM+  Page 18

Dynamic Skyline Processing

• Principle of Pruning Strategies

DM+  Page 19

DM+  Page 20

DM+  Page 21

DM+  Page 22

DM+  Page 23

• Author propose a new problem on dynamic skyline computation regarding a range query.

• To efficiently answer this query, Author propose an approach based on the gird index and a newly designed variant of the well-known Z-order curve. By these two components, three efficient pruning strategies are devised, thus avoiding the need to scan the whole dataset for generating the transferred dataset and also reducing the times of dominance checking.

DM+  Page 24

Thank you for listening!

DM+  Page 25

Q & A

DM+  Page 26