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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

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dynamic skylines considering range queries

Dynamic Skylines Considering Range Queries

Speaker: Adam

Adviser: Yuling Hsueh

16th International Conference, DASFAA 2011

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

introduction
INTRODUCTION
  • What is “Skyline” ?

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introduction1
INTRODUCTION
  • 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]|.

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introduction2
INTRODUCTION
  • |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]|.

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introduction3
INTRODUCTION
  • 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.

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introduction4
INTRODUCTION
  • Query=(2000, 4), C1=(1992, 8), C2=(1995, 8), C3=(1998, 3)
  • = (|1992 − 2000|, |8 − 4|) = (8, 4), = (5, 4) and = (2, 1)

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introduction5
INTRODUCTION
  • Dynamic skyline considering range queries

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preliminaries
PRELIMINARIES
  • 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.

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preliminaries1
PRELIMINARIES

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preliminaries2
PRELIMINARIES
  • 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)

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preliminaries3
PRELIMINARIES
  • 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.

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preliminaries4
PRELIMINARIES

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preliminaries5
PRELIMINARIES
  • 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])

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preliminaries6
PRELIMINARIES
  • 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

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preliminaries7
PRELIMINARIES
  • Query (3, 4), p4 =(4, 4)(1, 0), p1 = (1, 6 )  (2, 2)

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preliminaries8
PRELIMINARIES

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dynamic skyline processing
Dynamic Skyline Processing
  • Principle of Pruning Strategies

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dynamic skyline processing1
Dynamic Skyline Processing
  • Principle of Pruning Strategies

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dynamic skyline processing2
Dynamic Skyline Processing
  • Principle of Pruning Strategies

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algorithm
ALGORITHM

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experiment
EXPERIMENT

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experiment1
EXPERIMENT

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experiment2
EXPERIMENT

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conclusions
CONCLUSIONS
  • 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.

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the end
THE END

Thank you for listening!

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the end1
THE END

Q & A

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