Efficient Processing of Top-k Spatial
Preference Queries
João B. Rocha-Junior, Akrivi Vlachou,
Christos Doulkeridis, and Kjetil Nørvåg
www.ntnu.no
VLDB’ 2011 - Seattle, USA
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Outline
• Top-k spatial preference queries
• Current approaches
• Our approach
– Mapping to distance-score space
– Query processing
– Materialization (index construction)
• Experimental evaluation
• Conclusion
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VLDB’ 2011 - Seattle, USA
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Motivation
• Increasing number of Web information
systems specialized in location-based queries
• Systems are limited to simple spatial queries
– Example: return objects in a given spatial location
• Top-k spatial preference query
– Ranks data objects based on the score of feature
objects in their spatial neighborhood
– Combines spatial and non-spatial scores
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VLDB’ 2011 - Seattle, USA
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Top-k spatial preference queries
• Given a set of data objects
and scored feature objects
hotel
y
• Query
– Spatial neighborhood
– Features of interest (e.g., bars)
b2(0.6)
b1(0.9)
c1(0.6)
Top-1
• Returns
p2
Top-1
p1
– Ranked set of k best data objects
• Score of a data object
café
bar
c2(0.4)
c4(0.8)
c3(0.2)
– Obtained from feature objects
in its spatial neighborhood
b3(0.3)
Top-1
p3
x
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Score function
• Aggregation of partial scores
– Any monotone function: sum, max, and min
• Partial score
– Score of a data object for a set of feature objects
– Defined by the score of a single feature object
• Highest score
• Satisfies the spatial constraint
• Spatial constraint
– Range, nearest neighbor, and influence
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Example (agg=sum)
Range
score(p)=1.5
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Nearest neighbor
score(p)=1.0
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Influence
score(p)=0.6
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Current approaches
• Naïve
– Compute the score of all objects, select the top-k
– Very costly
• State-of-the-art [1,2]
– Data objects and feature objects are indexed by
multi-dimensional indices
[1] Yiu, M.L., Dai, X., Mamoulis, N., Vaitis, M., : “Top-k spatial preference queries”, ICDE, 2007.
[2] Yiu, M.L., Lu, H., Mamoulis, N., Vaitis, M.: “Ranking spatial data by quality preferences”, TKDE, 2011.
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VLDB’ 2011 - Seattle, USA
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Current approaches
• Probing algorithms (SP and GP)
– Requires computing the score for all objects
• Branch and bound algorithms (BB and BB*)
– Compute an upper-bound score for the entries in the
data objects R-tree
– Prune entries whose upper-bound score is smaller
than the score of the k-th object found
• Feature join algorithm (FJ)
– Create combinations of feature sets with high score
– Combinations whose score is smaller than the score
of the k-th object found are pruned
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Motivation behind our idea…
• Few feature objects are
necessary to compute the
score of a data object
y
c2(0.6)
c1(0.5)
– Features not dominated by
any other feature in terms
of both distance and score
p1
• Nice properties
?
c4(0.4)
c3(0.2)
c5(0.8)
x
hotel
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– Small size in practice
– Sufficient to support any
neighborhood condition
and query parameter
café
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Our framework
• Mapping to distance-score space
– Pairs of objects (p, t) with t  Fi to be examined
• Identify SKY(p, Fi)
– Minimum set of pairs required to compute the
score of p according to Fi for any query
• Materialize SKY(p, Fi)
– Stored in a R-tree, one R-tree Ri per feature set Fi
– Efficient query processing and maintenance
• Query processing algorithm
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Mapping to the distance-score space
hotel
café
(p1,c1)
(p1,c2)
(p2,c3)
c1(0.9)
(p2,c2)
c3(0.5)
c2(0.7)
(p1,c4)
p2
• Mapping
(p1,c3)
(p2,c4)
• Skyline
– Pairs (object, feature)
– Space [distance X score]
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pair (p2,c)
(p2,c1)
c4(0.3)
p1
pair (p1,c)
– Minimize: distance
– Maximize: score
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Theoretical properties
• SKY(p, Fi) is sufficient to determine the partial
score of p for any spatial preference query
– Maintaining SKY(p, Fi) is sufficient to answer any
spatial preference query (stored in an R-tree)
• SKY(p, Fi) is the minimum set required
– The data required to process range queries permits
processing nn and influence queries
• The proofs of the theorems can be found in the paper
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Access to partial scores
• Only node entries that
satisfy the spatial
constraint are accessed
– Items are retrieved in
decreasing order of score
• Minor modifications to
support nn and influence
root:
e1: (p3,t4) (p2,t1) (p1,t3)
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e1 e2
Max-heap: <p
<e13(0.8),p
> 2(0.6)>
e2: (p3,t4) (p2,t4) (p3,t4)
VLDB’ 2011 - Seattle, USA
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Query processing
• Compute top-k data objects progressively
aggregating partial scores retrieved from Ri
– Similar to Fagin’s algorithm (NRA)
• Algorithm
– Each time an object p is retrieved from Ri, any unseen
object p’ in Ri has a score(p’) ≤ score(p)
– Keep track of lower and upper-bound score of the
seen objects
– Terminates when the lower-bound of the k-th object is
better than the upper-bound of the remaining objects
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VLDB’ 2011 - Seattle, USA
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Example (range, r=4.5)
hotel
hotel
X
X
R1
restaurant
p3(0.8)
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R2
bar
p1(0.9)
+
Object
R1
R2
p3
0.8
p1
-
= 1.7
Score
Upper-bound
-
0.8
1.7
0.9
0.9
1.7
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Example (range, r=4.5)
R1
R2
p2(0.6)
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p2(0.6)
+
= 1.2
Object
R1
R2
Score
Upper-bound
p3
0.8
-
0.8
1.4
p1
-
0.9
0.9
1.5
p2
0.6
0.6
1.2
1.2
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Example (range, r=4.5)
R1
R2
p1(0.2)
= 0.5
Object
R1
R2
Score
Upper-bound
p3
0.8
1.1
1.1
Top-1 p1
0.2
0.6
0.3
0.9
1.1
1.2
1.1
1.2
p2
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p3(0.3)
+
0.6
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Materialization
• Objects are partitioned into regions
– The distance among objects in the same region is small
– The skyline set of the objects in the same region is
similar with high probability
• Compute SKY(R, Fi) for the region R
– SKY(p, Fi)  SKY(R, Fi), p  R
• Advantage
– The feature set is accessed only once to compute the
dynamic skyline of all objects in the region
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VLDB’ 2011 - Seattle, USA
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Experimental evaluation
• We compare our approach (SFA) against SP,
GP, BB, BB*, and FJ algorithms [1,2]
• All approaches are implemented in Java
• Measures: response time, I/O, update time,
index construction time, and index size
[1] Yiu, M.L., Dai, X., Mamoulis, N., Vaitis, M., : “Top-k spatial preference queries”, ICDE, 2007.
[2] Yiu, M.L., Lu, H., Mamoulis, N., Vaitis, M.: “Ranking spatial data by quality preferences”, TKDE, 2011.
www.ntnu.no
VLDB’ 2011 - Seattle, USA
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Variables studied
• Data distribution
– Uniform (UN), Synthetic (CN), Real (RL)
• Cardinality (object and features)
– 50K, 100K, 200K, 400K, 800K, 1600K
• Number of results (k)
– 10, 20, 30, 40, 50
• Number of feature sets
– 1, 2, 3, 4 5
• Query range (r), for range and influence queries
– 10, 40, 160, 640, 2560
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Datasets
Datasets
Number of
data objects
Number of
feature objects
Dynamic
skyline set
Wal-Mart (WM)
11K
4K
1.98
Hotels (HT)
11K
31K
4.82
Synthetic (CN)
100K
100K
11.26
Uniform (UN)
100K
100K
12.04
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Number of features
a) I/O varying the number
of feature sets
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b) response time varying the
number of feature sets
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Scalability
a) response time varying |Fi|
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b) response time varying |O|
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Real datasets
a) range
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b) influence
VLDB’ 2011 - Seattle, USA
c) nearest neighbor
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Conclusion
• Top-k spatial preference queries are a useful tool for
novel location-based applications
• We propose a new approach for processing top-k
spatial preference queries efficiently
– We find and materialize SKY(p, Fi)
– We prove that SKY(p, Fi) is sufficient to determine the
partial score of p for any spatial preference query
– The size of SKY(p, Fi) is small in practice
• We propose algorithms to process queries using our
index
• The efficiency of our approach is verified through
experiments on synthetic and real datasets
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Thanks!
More information:
João B. Rocha-Junior
joao@idi.ntnu.no
http://www.idi.ntnu.no/~joao
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VLDB’ 2011 - Seattle, USA
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