GeoCrowd: Enabling Query Answering with
Spatial Crowdsourcing
By
GOPIKRISHNA KURRA
SRIKANTH GOLI
SIVATEJA KOTIPALLI
1
OUTLINE
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Introduction.
Related work.
Problem Definition.
Taxonomy of crowd sourcing.
Preliminaries.
Assignment protocol.
Performance evaluation.
Experimental Methodologies.
Conclusion and Future work.
2
Introduction
 Spatial crowd sourcing is emerging
as a new platform, enabling spatial
tasks assigned to and performed
by human workers.
3
Introduction
 Spatial crowd sourcing is the process of crowd sourcing a set of
spatial tasks to a set of workers, which requires the workers to
perform the spatial tasks by physically traveling to those locations.
4
Related work
 Most existing work on spatial crowd sourcing focus on a particular
class of spatial crowd sourcing called participatory sensing.
 Mobile Millennium project:
It uses GPS-enabled mobile phones to collect en route traffic
information and upload it to a server in real time.
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Related work (Continued)
 All the previous studies on participatory sensing focus on a single
campaign and try to address challenges specific to that campaign.
Examples: Amazon’s Mechanical Turk, Crowd Flower etc
 More examples of single campaign include campaign for watching
petrol prices and a campaign for monitoring urban air pollution.
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Problem Definition
 Maximum task assignment (MTA) :
In spatial crowd sourcing, the main
optimization goal is to maximize the
overall task assignment while confirming
to the constraints of the workers.
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Problem Definition (Continued)
 In this paper author proposes three solutions to the maximum task
assignment
 Greedy (GR)
 Least Location Entropy Priority (LLEP)
 Nearest Neighbor Priority (NNP)
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Taxonomy of Crowd sourcing
 Spatial Crowd sourcing Classification:
Spatial crowd sourcing can be classified based on the motivation
of the workers into two classes
 Reward-based Spatial Crowd sourcing
 Self-incentivised Spatial Crowd sourcing
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Taxonomy of Crowd sourcing
 Spatial Task Publishing Modes:
With spatial crowd sourcing, tasks can be published in two
different modes
 Worker Selected Tasks (WST) Mode
 Server Assigned Tasks (SAT) Mode
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Taxonomy of Crowd sourcing
 Spatial Task Assignment Modes :
In this section, author defines two modes for task assignment in
terms of how to verify the validity of the spatial tasks.
 Single Task Assignment.
 Redundant Task Assignment.
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Taxonomy of Crowd sourcing
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Preliminaries:
 Definition 1 (Spatial Task):
A spatial task t of form <l, q, s, δ> is a query q to be answered at
location l, where l is a point in the 2D space. The query is asked at
time s and will be expired at time s + δ.
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Preliminaries:
 Definition 2 (Spatial Crowd sourced Query):
A spatial crowd sourced query of form
(<t1, t2, ...> , k) is a set of spatial tasks
and a parameter k issued by a requester,
where every spatial task t1 is to be
crowd sourced k number of times.
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Preliminaries
 Definition 3 (Worker):
A worker, denoted by w, is a carrier of a mobile device who
volunteers to perform spatial tasks. A worker can be in an either
online or offline mode. A worker is online when he is ready to
accept tasks.
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Preliminaries
 Definition 4 (Task Inquiry or TI):
Task inquiry is a request that an online worker w sends to the SCserver, when ready to work. The inquiry includes location of w, l,
along with two constraints: A spatial region R, and the maximum
number of acceptable tasks maxT.
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Preliminaries
 Definition 5 (Task Assignment Instance Set):
Let Wi={w1, w2, ...} be the set of online workers at time si. so, let
Ti={t1, t2, ...} be the set of available tasks at time si. The task
assignment instance set, denoted by Ii is the set of tuples of form
<w,t>, where a spatial task t is assigned to a worker w, while
satisfying the workers’ constraints.
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Preliminaries
 Definition 6 (Maximum Task Assignment (MTA)).
Given a time interval ϕ = {s1, s2, ..., sn}, let |Ii| be the number of
assigned tasks at time instance si. The maximum task assignment
problem is the process of assigning tasks to the workers during the
time interval ϕ, while the total number of assigned tasks
is maximized.
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ASSIGNMENT PROTOCOL:
Sc-server ideally should have global knowledge of all the workers
and tasks.
Global optimal solution is not feasible.
Using the spatial information and the capacity of the workers,scserver should arrive at local optimal solution.
Three solutions are proposed based on the local optimal strategy.
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ASSIGNMENT PROTOCOL(Contnd.):
The three solutions of assignment protocol are:
Greedy (GR) Strategy.
Least Location Entropy Priority(LLEP) Strategy.
Nearest Neighbor Priority(NNP) Strategy.
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Greedy(GR) Strategy:
At every instance of time, tries to maximize the current
assignment.
Does not provide a globally optimal solution.
Goal is to maximize overall assignment by solving the
maximum task assignment instance problem for every instance
of time
Every worker forms two constraints :the spatial region R, and
the maximum number of tasks maxT during task inquiry.
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Greedy(GR) Strategy(Contnd):
Theorm1:The maximum task assignment instance problem is
reducible to the maximum flow problem.
Consider a time instance si with Wi={w1,w2,…….} as the set of
online workers and Ti={t1,t2,……..} as the set of available spatial
tasks.
Let Gi=(V,E) be the flow network graph.
Set V contains Wi+Ti+2 vertices and set E contains Wi+Ti+m
edges.
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Greedy(GR) Strategy(Contnd):
An example of Wi and Ti
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Greedy(GR) Strategy(Contnd):
Flow network graph Gi=(V,E)
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Greedy(GR) Strategy(Contnd):
We can now use any algorithm that computes the maximum flow
in the network.
One such method is Ford-Fulkerson method.
Ford-Fulkerson method: start sending flow from source vertex to
destination vertex, as long as there is a path between the two with
available capacity.
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Least Location Entropy Priority(LLEP) strategy:
Greedy strategy does not consider future optimizations.
The idea here is to assign higher priority to tasks which are
located in worker sparse areas.
Location entropy: measure of total number of workers in that
location as well as relative proportion of their future visits to that
location.
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Least Location Entropy Priority(LLEP) strategy
(Contnd):
Pl(w) is the fraction of total visits to l that belongs to worker w.
Ol is the total number of visits to location l.
Ow,l is total number of visits to location l that belongs to
worker w.
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Least Location Entropy Priority(LLEP) strategy
(Contnd):
Here the goal is to assign maximum number of tasks during every
instance of time while the total cost(location entropy) associated to
assigned tasks is lowest.
Theorem 2. The minimum-cost maximum task assignment instance
problem is reducible to the minimum-cost maximum flow problem.
Consider a time instance si with Wi={w1,w2,…….} as the set of
online workers and Ti={t1,t2,……..} as the set of available spatial
tasks, let Gi=(V,E) be the flow network graph
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Least Location Entropy Priority(LLEP) strategy
(Contnd):
Every task is associated with a cost.
Let Vj be the vertices mapped to every worker Wi and vwi+j be the
vertices mapped to every task.
For every u belong to Vj let (u, vwi+j) be the edge connecting the
above two.
Every edge in the above set has a cost associated to it.
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Least Location Entropy Priority(LLEP) strategy
(Contnd):
Cost of all the other edges is set to 0.
Hence in the above graph, we have to find the minimum-cost
maximum flow.
First find maximum flow by Ford-Fulkerson method.
Then the cost of the flow can be minimized by linear programming.
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Least Location Entropy Priority(LLEP) strategy
(Contnd):
The total cost of the flow is defined as follows.
Here f is the flow in the edge (u,v) and a is the cost associated with
the edge (u,v).
There are several constraints like f(u,v)<=c(u,v) and f(u,v)=-f(v,u).
All constraints are linear and the goal is to optimize a linear
function ,which can be done by linear programming.
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Nearest Neighbor Priority (NNP) Strategy:
GR and LLEP do not consider the travel cost( in time or distance).
Travel cost of the workers in the assignment process is
incorporated here.
Tasks which are closer to a worker will have smaller travel costs.
Here the goal is to maximize the task assignment at every instance
while minimizing the travel cost of workers.
Hence higher priority is given to tasks which are closer.
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PERFORMANCE EVALUATION :
 The Author conducted several experiments on both real-world
and synthetic data to evaluate the performance of our proposed
approaches: GR, LLEP, and NNP.
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Experimental Methodology :
 In the first set of experiments, author evaluated the
scalability of our proposed approaches by varying the
number of spatial tasks from 50k to 200k.
 The Figures show the result of our experiments
using both the synthetic data and real data.
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Experimental Methodology :
CONTD…
 The figures show that LLEP outperforms both GR and NNP
in terms of the number of assigned tasks
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Experimental Methodology : CONTD…
 As the figures show, the average travel cost of the workers
decreases in all cases because in a task-dense area, there is a higher
probability that an assigned task is in a closer distance to a worker.
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Effect of Maximum Acceptable Tasks
Constraint:
 In the next set of experiments, author evaluated the impact of
the maximum acceptable tasks (i.e., maxT) constraint using
the synthetic data.
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Effect of Maximum Acceptable Tasks
Constraint : CONTD…
 LLEP is the superior approach in terms of improving the number of
task Assignment while NNP outperforms both GR and LLEP in
terms of the travel cost.
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Effect of Spatial Region Constraint :
 In
our final set of experiments, author measured the performance
of our approaches with respect to expanding the spatial region
of every worker .
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Effect of Spatial Region Constraint
CONTD…
 LLEP outperforms both GR and NNP in terms of the number of task
assignment, while the NNP approach is superior in terms of the travel
cost.
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RELATEDWORK :
With the increasing popularity of crowdsourcing, recently, a set of
crowdsourcing services such as Amazon’s Mechanical Turk (AMT)
and CrowdFlower have emerged which allow requesters to issue tasks
that workers can perform for a certain reward.
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RELATEDWORK CONTD :…
One class of spatial crowdsourcing is known as participatory
sensing, in which workers form a campaign to perform sensing
tasks.They use GPS-enabled mobile phones
to collect traffic information.
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RELATEDWORK CONTD :…
Another class of spatial crowdsourcing is known as volunteered
geographic information (or VGI), in which the goal is to create
geographic information provided voluntarily by individuals.
Some examples include:
1.WikiMapia
2.StreetMap
3.Google Map Maker
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CONCLUSION AND FUTUREWORK :
In this paper, the author introduced spatial crowdsourcing as the
process of crowdsourcing a set of spatial tasks to a set of
workers.
 As future work, the author aims to focus on the other classes of
spatial crowdsourcing.
 Moreover, since location privacy is one of the major impediments that
may hinder workers from participation in spatial crowdsourcing, they
plan to extend their work to protect the location privacy of the workers.
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REFERENCES :
[1] Amazon mechanical turk. http://www.mturk.com.
[2] Center for embedded networked sensing (cens).
http://urban.cens.ucla.edu/projects/.
[3] Crowdflower. http://www.crowdflower.com.
[4] Google map maker.
http://www.wikipedia.org/wiki/Google Map Maker.
[5] Gowalla. http://www.wikipedia.org/wiki/Gowalla.
[6] Minimum-cost maximum flow problem.
http://www.wikipedia.org/wiki/Minimum-cost
flow problem.
[7] Openstreetmap.
http://www.wikipedia.org/wiki/OpenStreetMap.
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Thank You
http://students.cse.unt.edu/~gk0096/
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