k-Dense POI Search in Road Network

Md. Rashidujjaman Rifat, Shubrami Moutushy, Abu Wasif, Dr. Mohammed Eunus Ali

Department of Computer Science and Engineering (CSE), BUET

In a road network while travelling

from source to destination, k-

Dense POI Search finds a path

containing k- POIs where total

distance from current location to

destination including travel cost of

k-clustered POIs is minimum.

METHODOLOGY

OBJECTIVE This work aims to give flexibility to users’

for searching a facility point.

The main contributions are:

Finding the best set of k POIs

Minimize total distance from current

location to destination including travel

cost of clustered k POIs

Finding travel order among selected

POIs

Defining a distance for different types

of POIs which distinguishes a target

POIs for selection in the set of k-POI

from pruned one

PROBLEM DEFINITION

OUTCOME The major outcomes of our approach are:

• A new query processing to find a

path containing k-POIs where total

distance including the travel cost of

k-POIs is minimum

• An algorithm that identifies the

best set of POI to form k-cluster

and update the threshold value

efficiently

Input Output

n1

n5

n4

n6

n3

n2

tB

A

C

D

s

n1

n5

n4

n6

n3

n2

tB

A

C

D

s

KDPS(s,t,Q((q.x,q.y),k,G(V,E))

s: Source

q: Query point

t: destination

T: pharmacy(object type)

k=3

nodeobject/POI

n2

n6

n7

n4

o1

o2

s

o3

o4o13

o12

o9o10 o11

o7

o8

o14

o15

o16

o17

o6

o5

q

n3

n1

t

n5 n2

n6

n7

n4

o1

o2

s

o3

o4o13

o12

o9o10 o11

o7

o8

o14

o15

o16

o17

o6

o5

q

n3

n1

t

n5

base POItarget POI

Query point q

2

1

3

4 5

q

q Kdps outputs a preferred path

P={q, n1, n4, n5, n6, t} containing

3(k=3) pharmacy where the

particular drug is found..

O9 is the nearest POI to q. It is initial base

POI obi, based on which cluster and

threshold is calculated. Other target objects

are selected as base POI for clustering based

on threshold.

For any target object oi in target POI set, if the sum of linear

distance from source and from destination to oi is greater than

threshold, then oi can be pruned from search region.

For o5, Dlinear= dc+dd ≥ Dthreshold. So o5 is pruned.

o10 is selected as next base POI for finding another ‘better’ cluster. If new POI

exists (not already in target POI set),will be inserted into target POI set.

Process is repeated through finding base POI from target POI set, finding new

cluster and choosing better one and updating threshold until it finds the best

POI.

n6

n7

n4

o1

o2

s

o3

o4o13

o12

o9o10 o11

o7

o8

o14

o15

o16

o17

o6

o5

q

n3

n1

t

n5

4 4 5

node

Clustered

POIs2

8

n

RERERENCE [1] J. S. E. Martin, H. P. Kriegel and X. Xu.

A density-based algorithm for discovering

clusters in large spatial databases with

noise. In ACM SIGKDD, 1996.

n2

n6

n7

n4

o1

o2

s

o3

o4o13

o12

o9o10 o11

o7

o8

o14

o15

o16

o17

o6

o5

q

n3

n1

t

n5Cd

dd

nodepruned

POI

n2

n6

n7

n4

o1

o2

s

o3

o4o13

o12

o9o10 o11

o7

o8

o14

o15

o16

o17

o6

o5

q

n3

n1

t

n5

initial base POItarget POI

Finding cluster using €-link algorithm[1].

For €=4 and k=3,

Distance from o9 to o10, DL(o9, o10)≤4

Distance from o10 to o11, DL(o10, o11)≤5

Distance from o9 to n1, DL(o9, n1)≤4

Distance from n1 to o12, DL(n1, o12)≤4

Cluster C={o9, o10, o12}