Optimization of Spatial Joins on Mobile Devices

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Optimization of Spatial Joins on Mobile Devices
N. Mamoulis1, P. Kalnis2, S. Bakiras3, X. Li2
1 Department of Computer Science and Information
Systems, University of Hong Kong
2 Department of Computer Science, National
University of Singapore
3 Department of Electrical and Electronic
Engineering, University of Hong Kong
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Motivation

• Users are equipped with a mobile device (eg. PDA)
• Ad-hoc spatial queries
• Combine data from remote servers

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

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Cost

• Telecommunication companies typically charge by
  the bulk of transferred data (eg. GPRS), instead
  of connection time.
• Goal Minimize the amount of transferred data.

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Mediators?
Restaurants
Hotels
Mediator

• 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

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Solution

• Integrate the statistics retrieval with the query
  processing phase
• Ask aggregate queries to estimate the data
  distribution
• Partition the space recursively to achieve
  sub-linear transfer cost
• Choose the physical operator indepen-dently for
  each partition

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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

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Operators

• WINDOW query return all objects intersecting a
  window w
• COUNT query return the number of objects
  intersecting w
• e-RANGE query return all objects within range e
  from a point p

We do not have access to the internal indices!
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Hash based spatial join
Each partition must fit in memory
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Recursive evaluation
Retrieve statistics for each subpart
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Nested loop spatial join
Recursive HBSJ 4 QRY 2 RCV 5 RCV NLSJ 2
RCV 2 SND 2 RES
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Cost Model

• TCP/IP MTU MSS BH

• c1 download RW objects from R and Sw objects
  from S and join them on the PDA
• c2 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

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MobiJoin algorithm

• MobiJoin(w, Rw, Sw)
• if Rw0 or Sw0 then return
• compute c1, c2, c3, c4
• cmin min(c1,c2,c3,c4)
• if cmin c4 then
• impose a regular grid over w
• for each cell w in w
• retrieve Rw and Sw
• MobiJoin(w, Rw, Sw)
• else follow action specified by cmin

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Iceberg Spatial Semi-Join

• SELECT H.id
• FROM Hotels H, Restaurants R
• WHERE dist(H.location, R.location) e
• GROUP BY H.id
• HAVING COUNT() m

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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
• Algorithms
• NLSP Only nested loop spatial join
• HBSJ Only hash-based spatial join

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Varying the distance threshold e
PDA buffer 5
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Varying the data skew
Uniform data gt MobiJoin reduces to HBSJ
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Varying the PDAs buffer size
Packets
Bytes
Large buffer gt HBSJ fails to prune the empty
areas
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Iceberg queries
Uniform data
Skewed data
Real dataset (35K) joins a synthetic dataset (1K)
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Conclusions

• Distributed spatial joins on mobile devices
• No mediator non collaborative servers limited
  set of supported operators
• MobiJoin
• Dynamically optimizes the entire process of
  statistics retrieval and query execution
• Single ad-hoc query
• Future work
• Support multi-way spatial joins
• Improve the accuracy of the cost model