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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 Speaker: Adam Adviser: Yuling Hsueh 16th International Conference, DASFAA 2011 Wen-Chi Wang En Tzu WangArbee L.P. Chen3
INTRODUCTION • What is “Skyline” ? DM+ Page 2
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]|. DM+ Page 3
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]|. DM+ Page 4
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. DM+ Page 5
INTRODUCTION • 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
INTRODUCTION • Dynamic skyline considering range queries DM+ Page 7
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. DM+ Page 8
PRELIMINARIES DM+ Page 9
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) DM+ Page 10
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. DM+ Page 11
PRELIMINARIES DM+ Page 12
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]) DM+ Page 13
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 DM+ Page 14
PRELIMINARIES • Query (3, 4), p4 =(4, 4)(1, 0), p1 = (1, 6 ) (2, 2) DM+ Page 15
PRELIMINARIES 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
ALGORITHM DM+ Page 20
EXPERIMENT DM+ Page 21
EXPERIMENT DM+ Page 22
EXPERIMENT DM+ Page 23
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. DM+ Page 24
THE END Thank you for listening! DM+ Page 25
THE END Q & A DM+ Page 26