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Ad-hoc Distributed Spatial Joins on Mobile Devices
Panos Kalnis, Xiaochen LiNational University of Singapore
Nikos MamoulisThe University of Hong Kong
Spiridon BakirasHong Kong University of Science and Technology
Motivation
Users are equipped with a mobile device (eg. PDA)
Ad-hoc spatial queries Combine data from remote servers
Hotels Restaurants
“Find hotels which are within 500m of a seafood restaurant”
Servers do not collaborate with each other The query is executed on the mobile device
Mediators?
Services may only allow end-user connections (eg., subscribers only)
Access through mediators may be more expensive
Requests are ad-hoc; existing mediators may not support them
Hotels Restaurants
Mediator
Cost
Telecommunication companies typically charge by the bulk of transferred data (eg. GPRS), instead of connection time.
Goal: Minimize the amount of transferred data.
Solution
Ask aggregate queries to estimate the data distribution (i.e., statistics)
Partition the space recursively to achieve sub-linear transfer cost
Choose the physical operator indepen-dently for each partition
Related Work
Hash-based methods (eg. PBSM): require all data to be transferred
R-tree based methods (eg., [Tan et.al, TKDE, 2000]): require access to internal index
Mediators : HERMES : Statistics from previous queries DISCO, Garlic : Statistics during initialization Tuckila : Optimize parts of the execution tree
Operators
WINDOW query: return all objects intersecting a window w
COUNT query: return the number of objects intersecting w
ε-RANGE query: return all objects within range ε from a point p
NO access to the internal indices!
ε
w
p
Query Types Intersection Join
Find hotels which are inside parks
E-range Join Find restaurants which
are within 500m of a hotel
Iceberg Semi-join Find hotels which are
close to at least 3 restaurants
ε
Hash Based Spatial Join
Each partition must fit in memory
Recursive evaluation
Retrieve statistics for each subpart
Inefficient HBSJ
Nested Loop Spatial Join
Recursive HBSJ : 4 QRY + 2 RCV + 5 RCV
NLSJ : 2 RCV + 2 SND + 2 RES
Inefficient NLSJ
Cost Model
TCP/IP: MTU = MSS + BH
MSS
BBBBT DHDDB )(
c1: download |RW| objects from R and |Sw| objects from S and join them on the PDA
C2,3: download |RW| objects from R, send them as window queries to S and retrieve the results
c4: repartition w, retrieve detailed statistics and apply the algorithm recursively
UpJoin (Uniform Partition Join)
Decide if datasets are uniform
If HBSJ is cheaper and both datasets are uniform then perform HBSJ
If NLSJ is cheaper and the largest dataset is uniform then perform NLSJ
Else repartition
Uniformity check
wiww DDD
'4
Dw
Dw’0 Dw’1
Dw’3 Dw’2
% variation from uniform distribution
Note: UpJoin will not repartition if the cost for retrieving statistics is larger than the cost of joining
Inefficient UpJoin
SR-Join (Similarity Related Join)
wiw
wwi A
A
DD
Area% variationof density
Identify dense and sparse quadrants
If the distribution is similar then apply HBSJ or NLSJ
Else repartitionX
X
X
X
Experimental setup Implementation
Server: Unix Client: HP-Ipaq PDA (WiFi network, 400MHz
RISC CPU, 64MB RAM, Windows Pocket PC) Datasets:
Synthetic: 1K – 10K points, varying skew Real: Roads and railways of Germany
Setting the parameters
α (for UpJoin) ρ (for SR-Join)Uniform Uniform
Real Dataset
Uniform
Comparison with SemiJoin
•SemiJoin: Use intermediate levels of R-Tree index•We cannot use it in practice, because we cannot access the index
Uniform
Conclusions Distributed spatial joins on mobile devices No mediator – non collaborative servers – limited
set of supported operators Two algorithms
UpJoin SRJoin Both estimate the datasets’ distribution
Future work Support multi-way spatial joins Improve the accuracy of the cost model
Questions?