Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos 1
“Continuous All k Nearest Neighbor Queries in Smartphone Networks”
Tuesday, July 24, 201213th IEEE Int. Conference on Mobile Data Management
(MDM’12), Bangalore, India
Georgios Chatzimilioudis
Demetrios Zeinalipour-Yazti
Marios D. Dikaiakos
Wang-Chien Lee
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• Smartphone: A powerful sensing device!– Processing: 1.4 GHz quad core (Samsung Exynos)
– RAM & Flash Storage: 2GB & 48GB, respectively
– Networking: WiFi, 3G (Mbps) / 4G (100Mbps–1Gbps)
– Sensing: Proximity, Ambient Light, Accelerometer, Microphone, Geographic Coordinates based on AGPS (fine), WiFi or Cellular Towers (coarse).
• In-House Applications!
Smartphones
SmartTrace (ICDE’09,MDM’09,TKDE’12)
Airplace(MobiSys’12, MDM’12)
SmartP2P(MDM’11, MDM’12)
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• Crowdsourcing with Smartphones– A smartphone crowd is constantly moving and sensing providing
large amounts of opportunistic data that enables new services and applications
Motivation
"Crowdsourcing with Smartphones", Georgios Chatzimiloudis, Andreas Konstantinides, Christos Laoudias, Demetrios Zeinalipour-Yazti, IEEE Internet Computing, Special Issue: Crowdsourcing (Sep/Oct 2012), accepted May 2012. IEEE Press, 2012
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Continuous k Nearest Neighbor Queries
Find 2 Closest Neighbors for 1 User
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Motivation
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“Create a Framework for Efficient Proximity Interactions”
Screenshots from a prototype system we've implemented for Windows Phonehttp://www.zegathem.com/
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Continuous All k Nearest Neighbor Queries
Find 2 Closest Neighbors for ALL User
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• Motivation• System Model and Problem
Formulation• Proximity Algorithm• Experimental Evaluation• Future Work
7
Presentation Outline
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• A set of users moving in the plane of a region (u1, u2, … un)
• Area covered by a set of Network Connectivity Points (NCP)– Each NCP creates the notion of a cell
– W.l.o.g., let the cell be represented by a circular area with an arbitrary radius
• A mobile user u is serviced at any given time point by one NCP.
• There is some centralized service, denoted as QP (Query Processor), which is aware of the coverage of each NCP.
• Each user u reports its positional information to QP regularly
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System Model
C
.u7
.u4
u5.
u2.
u6.
u0.
u3.
.u1
Query Processor
QP
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
QueryProcessor
• Definition of the CAkNN problem:Given a set U of n users and their location reports ri,t R∈ at timestep t T ∈ , then the CAkNN problem is to find in each timestep t T ∈ and for each user ui U ∈ the k objects Usol U − u⊆ i such that for all other objects uo ∈ U − Usol − ui, dist(uk, ui) ≤ dist(uo, ui) holds
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Problem Definition
C
.u7
.u4
u5.
u2.
u6.u0.
u3.
.u1
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Query Processor
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Naïve Solution / Related Work
Find 2-NN for u0 at timestep t.
C
.u7
.u4
u5.
u2.
u6.u0.
u3.
.u1
For u5?
Look inside your cell!
Perform iterative deepening!
TOO EXPENSIVE!
WRONG!(u1 closer)
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos 11
Related Work• Existing algorithms for CkNN (not CAkNN)
Yu et al. (YPK) 11 and Mouratidis et al. (CPM) 12
• Stateless Version: Iteratively expand the search space for each user into neighboring cells to find the kNN (like previous slide)
• Stateful Version: Improve the stateless version by utilizing previous state, under the following assumptions:
– i) Static Querying User (i.e., designated for point queries)– ii) Target users move slowly (i.e., state does not decay)– iii) Few Target users
11. Yu, Pu, Koudas. “Monitoring k-nearest neighbor queries over moving objects,” ICDE ’0512. Mouratidis, Papadias, Hadjieleftheriou, “Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring,” SIGMOD ’05.
Not Appropriate
for CAkNN
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• Motivation• System Model and Problem
Formulation• Proximity Algorithm• Experimental Evaluation• Future Work
12
Presentation Outline
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Proximity Overview
• The first specialized algorithm for Continuous All k-NN (CAkNN) queries
• Important characteristics:– Stateless (i.e., optimized for high mobility)– Batch processing (i.e., with search space
searching)– Parameter-free (i.e., no tuning parameters)
• Generic operator for proximity-based queries
– See Crowdcast application presented later.
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
QueryProcessor
C
.u7
.u4
u5.
u2.
u6.u0.
u3.
.u1
Proximity Outline• For every timestep:
1. Initialize a k+-heap for every cell
2. Insert every user’s location report to every k+-heap• Notice that k+-heap is a heap-based structure and most location
reports will be dropped as a result of an insert operation
3. For every user scan the k+-heap of his cell to find his k-NN
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
QueryProcessor
C
.u7
.u4
u5.
u2.
u6.
u0.
u3.
.u1
Intuition behind Proximity• Users in a cell will share the same search
space (search space sharing)• Compute 1 search space per cell only!
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Search Space(contains the right answers for all users in
C)
Cell
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• The k+-heap structure for a cell
Proximity k+-heap
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All users inside the
cell
K nearest users outside the cell
Beyond k nearest outside users (correctness)
k+-heap structure
Ktop-k heap
Bordered list
Ulist
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
k+-heap structure
Ktop-k heap
Bordered list
Ulist
k+-heap Construction (for Cell C)
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ArrivingReports
StructureU
StructureK
StructureB
u6 u6
u4 u6 u4
u7 u6 u7, u4
u2 u6 u4, u2 u7
u3 u6 u3, u2 u4, u7
u1 u6 u2, u1 u3, u4
u5 u6 u2, u1 u3, u4, u5
u0 u6, u0 u2, u1 u3, u4, u5
Assume k=2.
C
.u7
.u4
u5.
u2.
u6.
u0.
u3.
.u1
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
k+-heap structure
Ktop-k heap
Bordered list
Ulist
k+-heap Construction (for Cell C)
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ArrivingReports
StructureU
StructureK
StructureB
u6 u6
u4 u6 u4
u7 u6 u7, u4
u2 u6 u4, u2 u7
u3 u6 u3, u2 u4, u7
u1 u6 u2, u1 u3, u4
u5 u6 u2, u1 u3, u4, u5
u0 u6, u0 u2, u1 u3, u4, u5
Assume k=2.
C
.u7
.u4
u5.
u2.
u6.
u0.
u3.
.u1
2 nearest users to cell
x.
Diameter +
d k
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• Motivation• System Model and Problem
Formulation• Proximity Algorithm• Experimental Evaluation• Future Work
19
Presentation Outline
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Experimental Methodology
• Dataset:– Oldenburg Dataset:
• Spatiotemporal generator with roadmap of Oldenburg as input (25km x 25km)• 5000 maximum users (Oldenburg population: 160.000)
– Manhattan Dataset:• Vehicular mobility generator with roadmap of Manhattan as input (3km x 3km)• 500 users
• Network connectivity points (NCPs) uniformly in space– Communication ranges 1km, 4km and 16km for the Oldenburg
dataset and ranges 1km and 4km for the Manhattan dataset
• Query: CAkNN• Comparison: Proximity vs YPK vs CPM (using the
optimal parameter value for YPK and CPM)
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Proximity - Experiments
• Bottleneck for Proximity: build time• Bottleneck for the adapted YPK and CPM: search time
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Proximity - Experiments
• Benefit of Proximity in the search time• Proximity is scalable with respect to k
– Search space for Proximity is not proportional to k like YPK, CPM
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Proximity - Experiments
• Proximity: build time drops as k increases!• Machine’s memory scheduler makes more efficient use of
buffers when the search spaces are larger
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Proximity - Experiments
• Proximity scales with the number of users• Proximity outperforms YPK, CPM by an order of magnitude• Proximity does not converge to YPK, CPM for higher values of Nmax.
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Crowdcast Suite
The Crowdcast Application Suite
PROXIMITY
WIN7 API
…
Ranked 3rd in Microsoft’s ImagineCup contest local competition (to appear in the next demo session!)
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Crowdcast: MsgCast
"Get your location-based questions answered!"
MsgCast- Location-based Chat Channel (Post / Follow)- Provide Guidelines
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Crowdcast: HelpCast
HelpCast"The Ubiquitous Help Platform for Anyone in Need”
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Crowdcast: EyeCast
"Extend your Vision beyond your 2 eyes!"
EyeCast- Disaster Recovery Operations- Indoor Video Conferencing Network
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
• Motivation• System Model and Problem
Formulation• Proximity Algorithm• Experimental Evaluation• Future Work
29
Presentation Outline
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Proximity – Future Work
• Privacy extensions (e.g., spatial cloaking, strong privacy)
• Parallelizing server computation
• Proximity for cloud server
• User-defined k values
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Future Work: SmartLab
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SmartLab.cs.ucy.ac.cy
Install APK, Upload File,Reboot, Screenshots, Monkey Runners, etc.…
Programming cloud for the development of smartphone network applications & protocols as well as experimentation with real smartphone devices.
"Demo: A Programming Cloud of Smartphones", A. Konstantinidis, C. Costa, G. Larkou and D. Zeinalipour-Yazti, "Demo at the 10th International Conference on Mobile Systems Applications and
Services" (Mobisys '12), Low Wood Bay Lake District UK, 2012
Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos
Future Work: SmartLab
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Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010
MDM 2012 © Chatzimilioudis, Zeinalipour-Yazti, Lee, Dikaiakos 33
“Continuous All k Nearest Neighbor Queries in Smartphone Networks”
Tuesday, July 24, 201213th IEEE Int. Conference on Mobile Data Management
(MDM’12), Bangalore, India
Georgios Chatzimilioudis
Demetrios Zeinalipour-Yazti
Marios D. Dikaiakos
Wang-Chien Lee
Thanks!Questions?