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Social and health care services
as an optimization problem
Pasi Fränti
Univ. of Eastern Finland
Joensuu, FINLAND
Location-aware applications
Data mining
Information retrieval
Prof. Pasi Fränti Clustering, data mining, location-aware system
Dr. Radu Mariescu Location-aware systems, GPS data analysis, text mining
Dr. Usman Khan Health applications, neural networks
Lahari Sengupta Orienteering game, travelling salesman
Sami Sieranoja Clustering, neighborhood graphs
Gulraiz Chourhary Dynamics of physiological signals
Himat Shah Web mining, keyword extraction
Nancy Fazal Content creation, web mining
Masoud Fatemi Twitter data analysis
Jiawei Yang Outlier detection, clustering
Thomas Ho Traffic simulation, electric vehicle load prediction
Nilima Shah Image segmentation
Group members
Neighborhood
graphs
Clustering
TSP
Spatial data
GPS
trajectories
Text mining
Games
Clustering
Location-aware applications
Move type analysis
Train
Flight
K. Waga, A. Tabarcea, M. Chen and P. Fränti,
“Detecting movement type by route segmentation and
Classification”, CollaborateCom, Pittsburgh, USA, 2012
Skiing
R. Mariescu-Istodor and P. Fränti, "Gesture input for GPS route search", Joint Int. Workshop on Structural, Syntactic,
and Statistical Pattern Recognition (S+SSPR 2016), Merida, Mexico, LNCS 10029, 439-449, November 2016
Shape search
GPS Trajectories Road Network
R. Mariescu-Istodor and P. Fränti, "From Routes to Roads: Infer road network using GPS trajectories",
ACM Trans. on Spatial Algorithms and Systems, 2018
Road network extraction
Applications: share riding
Logo image
Navigation bar
Title
Images
Keywords
Text
Web content mining
M. Rezaei, N. Gali and P. Fränti "ClRank: a method for keyword extraction from web pages using clustering and distribution
of nouns", IEEE/WIC/ACM Web Intelligence and Intelligent Agent Technology Singapore, 2015
Keyword extraction
Addresses
A. Tabarcea, V. Hautamäki, P. Fränti, "Ad-hoc georeferencing of web-pages using street-name prefix trees", Int. Conf.
on Web Information Systems & Technologies (WEBIST'10), Valencia, Spain, vol.1, 237-244, April 2010.
Address detection
Generic
Search engine
A. Tabarcea, N. Gali and P. Fränti, "Framework for
location-aware search engine", Journal of Location
Based Services, 11 (1), 50-74, November 2017.
Location-aware search engine
Random Swap(X)  C, P
C  Select random representatives(X);
P  Optimal partition(X, C);
REPEAT T times
(C
new
,j)  Random swap(X, C);
Pnew
 Local repartition(X, Cnew
, P, j);
C
new
, P
new
 Kmeans(X, C
new
, P
new
);
IF f(C
new
, P
new
) < f(C, P) THEN
(C, P)  Cnew
, Pnew
;
RETURN (C, P);
GeneticAlgorithm(X)  (C, P)
FOR i1 TO Z DO
Ci
 RandomCodebook(X);
Pi
 OptimalPartition(X, Ci
);
SortSolutions(C,P);
REPEAT
{C,P}  CreateNewSolutions( {C,P} );
SortSolutions(C,P);
UNTIL no improvement;
CreateNewSolutions({C, P})  {Cnew
, Pnew
}
Cnew-1
, Pnew-1
 C1
, P1
;
FOR i2 TO Z DO
(a,b)  SelectNextPair;
Cnew-i
, Pnew-I
 Cross(Ca
, Pa
, Cb
, Pb
);
IterateK-Means(Cnew-i
, Pnew-i
);
Cross(C1
, P1
, C2
, P2
)  (Cnew
, Pnew
)
Cnew
 CombineCentroids(C1
, C2
);
Pnew
 CombinePartitions(P1
, P2
);
Cnew
 UpdateCentroids(Cnew
, Pnew
);
RemoveEmptyClusters(Cnew
, Pnew
);
IS(Cnew
, Pnew
);
CombineCentroids(C1
, C2
)  Cnew
Cnew
 C1
 C2
CombinePartitions(Cnew
, P1
, P2
)  Pnew
FOR i1 TO N DO
IF x c x ci p i pi i
  1 2
2 2
THEN
p pi
new
i 1
ELSE
p pi
new
i 2
END-FOR
UpdateCentroids(C1
, C2
)  Cnew
FOR j1 TO |Cnew
| DO
c j
new
 CalculateCentroid(Pnew
, j );
Genetic Algorithm (GA)
Random Swap (RS)
P. Fränti, "Genetic algorithm with deterministic crossover
for vector quantization", Pattern Recognition Letters, 2000.
P. Fränti, "Efficiency of random swap clustering",
Journal of Big Data, 5:13, 1-29, 2018
CI = 0
CI = 0
Clustering algorithms


D
i
ii yxyxd
1
2
),(
yx
yx
yxdDice



2
1),(
Euclidean distance:
Dice coefficients: Strings divided into bi-grams:
String (5)
{st, tr, ri, in, ng}
Mining (5)
{mi, in, ni, in, ng}
xy={st, tr, ri, in, ng, mi, ni}
xy={in, ng}
d = 1-4/(5+5) = 60%
Edit distance:
Minimum number of edit operations
(insert, delete, substitute)
to transform string x to string y.
Distance functions
Calculating mean
HLSQPKPK
THELSIFBJI EOMLSNI
HEHTLSINKI ZULSINKI
HOELSVVKIG
HELSSINI
DHELSIRIWKJII
QHSELINI
EVSDNFCKVM
UCGNRTION
RRCOOGEPIONN
RXONUIUOK
ECOBNFITIUOND
RUPCOWGNIPZTHUN
REJOGNEITION
RRSCGNXIIUN
RCOGQNOIRTON
RECOGUITPON
WEIOTNNIHTMIOJ
HELSINKI RECOGNITION
Means from scrambled text
Cluster 41 Cluster 43 Cluster 247 Cluster 292 Cluster 326
soft-bill
soot-grimed
sweet-toothed
split-tongued
black-visaged
soft-winged
short-witted
short-termed
stout-armed
still-fishing
stiff-limbed
swift-stealing
short-leaved
snotty-nosed
ivory-billed
hot-mettled
soft-going
snowy-winged
Livingstone
herringbone
Burlingham
Neowashingtonia
Upington
Hillingdon
Lovington
Arlington
Lexington
Herington
Stringtown
Arrington
milliangstrom
Accrington
Northington
Farlington
Ellington
slommacky
crummock
bummack
mimmock
slammock
bummalos
mimmocky
malmock
hommocks
earthgrubber
crumhorn
malbrouck
krumhorn
shammocky
Babcock
plumrock
fleadock
Cummock
Kurtz
dinarchy
myriarchy
freshly
triarcuated
matriarch
mandriarch
dyarchic
myriarch
BSLArch
taxiarch
Bush
Ruthi
Knuth
fleshy
gush
Thushi
Furth
injelly
johnin
Conley
moulvi
Solly
wolly
Poulenc
woodsy
doozy
oofy
goodbyes
coolly
Woodlyn
woofy
boolya
Lolly
Coplay
Goodbys
Clustering English words
466,544 words
If you're looking for work in #Oslo, Oslo, check out this #job: https://t.co/ycYqTgJc8r #BusinessMgmt #Hiring
#CareerArc
If you're looking for work in #Göteborg, check out this #job: https://t.co/bJGKIco2SN #CustomerService #Hiring
#CareerArc
Interested in a #job in #Uppsala, Uppsala County? This could be a great fit: https://t.co/O7O91oVF1j #Hiring
#CareerArc
See our latest #kirkkonummi #job and click to apply: Software Developer Trainee - https://t.co/vrsxjPMhsA
#SoftwareDev #Hiring #CareerArc
If you're looking for work in #Solna, check out this #job: https://t.co/BUwsuBfXiO #LEGO #Hiring #CareerArc
Många stör sig på andra på Twitter. På snälla Fredagen vill jag komma med lite bra tips: 1
https://t.co/TFh40S7cC1… https://t.co/FjfR8ALvnn
If you're looking for work in #HKI, check out this #job: https://t.co/ZJLzylpqxf #DellJobs #Sales #Hiring #CareerArc
See our latest #kirkkonummi #job and click to apply: SW Developer Intern, IoT Device and Data Management -…
https://t.co/5GEkyiMUlh
We're #hiring! Click to apply: Technical Program Manager - https://t.co/BP0qrfigRK #ProjectMgmt #stockholm #Job
#Jobs #CareerArc
Clustering Tweets
544,113 tweets
Detection and omission of outliers
Full search:
O(N) distance
calculations.
Graph structure:
O(k) distance
calculations.
Full search: Graph structure:
1. Fast NN-search 2. Outlier detection
4. Density estimation
3. Fast
clustering
j
a b
e
f
g
k
c
d
h
i
8.0
2.6
5. Recommendation system
Neighborhood graphs
Health care services
Data Knowledge
Helsinki
Goals of the project
1. Create web interface
- To access data
- Importing external data in compatible formats
2. Interactive optimization tool
- Working on map
- Optimizes service locations and allocations
for given cost provision site for specific groups
3. Algorithms and methods
- Balanced data clustering
- Network optimization
- Data analysis
23
24
Touring nurse
TSP
Clustering
Work allocation
Scheduling
Dentist
8:00
Physio
16:30
FSDM
10:00
Where we started?
25
Health care services provided
1. Health center
2. Hospital
3. Dental
4. Rehabilitation
5. Aging therapies
6. Aging accomodation
7. Children's daycare
Solutions found on web
Clustering of data filtering?
Visualizations of clustering (1)
Visualizations of clustering (2)
OpenOpen
clustercluster
OpenOpen
clustercluster
OpenOpen
clustercluster
Asia clusterAsia cluster PeninsularPeninsular
MalaysiaMalaysia
Singapore clusterSingapore cluster
Singapore flierSingapore flier
Two more clicks
Two more clicks
5
Clusters in Mopsi
M. Rezaei and P. Fränti "Real-time clustering of large geo-referenced data for
visualizing on map", Advances in Electrical and Computer Engineering, 2018.
220 photos220 photos
9 clusters9 clusters
47 photos47 photos
14 clusters14 clusters
14 photos14 photos
7 clusters7 clusters
OpenOpen
clustercluster
OpenOpen
clustercluster
Clusters in Mopsi
32
Various map view prototypes
Oulun karttaratkaisu
Pasi’s team proto
Radu’s quick & dirty
http://cs.uef.fi/impro_dev/map/map.php
Making the tools
to meet the data
Data Knowledge
Helsinki
1. Esophageal varices – Fibrosis and Cirrhosis of liver
2. Malignant neoplasm of pancreas – Aftercare
3. Sleep disorders – Non-classified respiratory failure
…
Correlation analysis of patient records
35
Service locations in city
Clustering problem
36
Services in rural area: touring nurse
37
Travelling salesman problem
Touring nurse
38
Recompiling the services
Resources
A
B
39
Scheduling problem
Worker-task allocation
Home take care proto
MOPSI project
Task scheduling
41
Self-monitoring
Market place of services
Ideal case
15 €
11 €
12 €
70 €
15 €
10 €
45 €
52 €
60 €
55 €
12 €
45 €
25 €
120 €
500 €
120 €
Market
70 €
45 €
52 €
60 €
55 €
12 €
45 €
25 €
120 €
500 €
120 €
Market
Government
Market place of services
Possible future scenario
15 €
11 €
12 € 15 €
10 €
44
What to optimize exactly?
?
Market
Case 1: No services, no costs
0 €
Market
Case 2: Full service to everyone
1,000,000,000…€
47
… future under process…
The end

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Pasi Fränti: Social and health care services as an optimization problem

  • 1. Social and health care services as an optimization problem Pasi Fränti Univ. of Eastern Finland Joensuu, FINLAND
  • 2.
  • 4. Prof. Pasi Fränti Clustering, data mining, location-aware system Dr. Radu Mariescu Location-aware systems, GPS data analysis, text mining Dr. Usman Khan Health applications, neural networks Lahari Sengupta Orienteering game, travelling salesman Sami Sieranoja Clustering, neighborhood graphs Gulraiz Chourhary Dynamics of physiological signals Himat Shah Web mining, keyword extraction Nancy Fazal Content creation, web mining Masoud Fatemi Twitter data analysis Jiawei Yang Outlier detection, clustering Thomas Ho Traffic simulation, electric vehicle load prediction Nilima Shah Image segmentation Group members
  • 6. Move type analysis Train Flight K. Waga, A. Tabarcea, M. Chen and P. Fränti, “Detecting movement type by route segmentation and Classification”, CollaborateCom, Pittsburgh, USA, 2012 Skiing
  • 7. R. Mariescu-Istodor and P. Fränti, "Gesture input for GPS route search", Joint Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2016), Merida, Mexico, LNCS 10029, 439-449, November 2016 Shape search
  • 8. GPS Trajectories Road Network R. Mariescu-Istodor and P. Fränti, "From Routes to Roads: Infer road network using GPS trajectories", ACM Trans. on Spatial Algorithms and Systems, 2018 Road network extraction
  • 11. M. Rezaei, N. Gali and P. Fränti "ClRank: a method for keyword extraction from web pages using clustering and distribution of nouns", IEEE/WIC/ACM Web Intelligence and Intelligent Agent Technology Singapore, 2015 Keyword extraction
  • 12. Addresses A. Tabarcea, V. Hautamäki, P. Fränti, "Ad-hoc georeferencing of web-pages using street-name prefix trees", Int. Conf. on Web Information Systems & Technologies (WEBIST'10), Valencia, Spain, vol.1, 237-244, April 2010. Address detection
  • 13. Generic Search engine A. Tabarcea, N. Gali and P. Fränti, "Framework for location-aware search engine", Journal of Location Based Services, 11 (1), 50-74, November 2017. Location-aware search engine
  • 14. Random Swap(X)  C, P C  Select random representatives(X); P  Optimal partition(X, C); REPEAT T times (C new ,j)  Random swap(X, C); Pnew  Local repartition(X, Cnew , P, j); C new , P new  Kmeans(X, C new , P new ); IF f(C new , P new ) < f(C, P) THEN (C, P)  Cnew , Pnew ; RETURN (C, P); GeneticAlgorithm(X)  (C, P) FOR i1 TO Z DO Ci  RandomCodebook(X); Pi  OptimalPartition(X, Ci ); SortSolutions(C,P); REPEAT {C,P}  CreateNewSolutions( {C,P} ); SortSolutions(C,P); UNTIL no improvement; CreateNewSolutions({C, P})  {Cnew , Pnew } Cnew-1 , Pnew-1  C1 , P1 ; FOR i2 TO Z DO (a,b)  SelectNextPair; Cnew-i , Pnew-I  Cross(Ca , Pa , Cb , Pb ); IterateK-Means(Cnew-i , Pnew-i ); Cross(C1 , P1 , C2 , P2 )  (Cnew , Pnew ) Cnew  CombineCentroids(C1 , C2 ); Pnew  CombinePartitions(P1 , P2 ); Cnew  UpdateCentroids(Cnew , Pnew ); RemoveEmptyClusters(Cnew , Pnew ); IS(Cnew , Pnew ); CombineCentroids(C1 , C2 )  Cnew Cnew  C1  C2 CombinePartitions(Cnew , P1 , P2 )  Pnew FOR i1 TO N DO IF x c x ci p i pi i   1 2 2 2 THEN p pi new i 1 ELSE p pi new i 2 END-FOR UpdateCentroids(C1 , C2 )  Cnew FOR j1 TO |Cnew | DO c j new  CalculateCentroid(Pnew , j ); Genetic Algorithm (GA) Random Swap (RS) P. Fränti, "Genetic algorithm with deterministic crossover for vector quantization", Pattern Recognition Letters, 2000. P. Fränti, "Efficiency of random swap clustering", Journal of Big Data, 5:13, 1-29, 2018 CI = 0 CI = 0 Clustering algorithms
  • 15.   D i ii yxyxd 1 2 ),( yx yx yxdDice    2 1),( Euclidean distance: Dice coefficients: Strings divided into bi-grams: String (5) {st, tr, ri, in, ng} Mining (5) {mi, in, ni, in, ng} xy={st, tr, ri, in, ng, mi, ni} xy={in, ng} d = 1-4/(5+5) = 60% Edit distance: Minimum number of edit operations (insert, delete, substitute) to transform string x to string y. Distance functions
  • 18. Cluster 41 Cluster 43 Cluster 247 Cluster 292 Cluster 326 soft-bill soot-grimed sweet-toothed split-tongued black-visaged soft-winged short-witted short-termed stout-armed still-fishing stiff-limbed swift-stealing short-leaved snotty-nosed ivory-billed hot-mettled soft-going snowy-winged Livingstone herringbone Burlingham Neowashingtonia Upington Hillingdon Lovington Arlington Lexington Herington Stringtown Arrington milliangstrom Accrington Northington Farlington Ellington slommacky crummock bummack mimmock slammock bummalos mimmocky malmock hommocks earthgrubber crumhorn malbrouck krumhorn shammocky Babcock plumrock fleadock Cummock Kurtz dinarchy myriarchy freshly triarcuated matriarch mandriarch dyarchic myriarch BSLArch taxiarch Bush Ruthi Knuth fleshy gush Thushi Furth injelly johnin Conley moulvi Solly wolly Poulenc woodsy doozy oofy goodbyes coolly Woodlyn woofy boolya Lolly Coplay Goodbys Clustering English words 466,544 words
  • 19. If you're looking for work in #Oslo, Oslo, check out this #job: https://t.co/ycYqTgJc8r #BusinessMgmt #Hiring #CareerArc If you're looking for work in #Göteborg, check out this #job: https://t.co/bJGKIco2SN #CustomerService #Hiring #CareerArc Interested in a #job in #Uppsala, Uppsala County? This could be a great fit: https://t.co/O7O91oVF1j #Hiring #CareerArc See our latest #kirkkonummi #job and click to apply: Software Developer Trainee - https://t.co/vrsxjPMhsA #SoftwareDev #Hiring #CareerArc If you're looking for work in #Solna, check out this #job: https://t.co/BUwsuBfXiO #LEGO #Hiring #CareerArc Många stör sig på andra på Twitter. På snälla Fredagen vill jag komma med lite bra tips: 1 https://t.co/TFh40S7cC1… https://t.co/FjfR8ALvnn If you're looking for work in #HKI, check out this #job: https://t.co/ZJLzylpqxf #DellJobs #Sales #Hiring #CareerArc See our latest #kirkkonummi #job and click to apply: SW Developer Intern, IoT Device and Data Management -… https://t.co/5GEkyiMUlh We're #hiring! Click to apply: Technical Program Manager - https://t.co/BP0qrfigRK #ProjectMgmt #stockholm #Job #Jobs #CareerArc Clustering Tweets 544,113 tweets
  • 20. Detection and omission of outliers
  • 21. Full search: O(N) distance calculations. Graph structure: O(k) distance calculations. Full search: Graph structure: 1. Fast NN-search 2. Outlier detection 4. Density estimation 3. Fast clustering j a b e f g k c d h i 8.0 2.6 5. Recommendation system Neighborhood graphs
  • 22. Health care services Data Knowledge Helsinki
  • 23. Goals of the project 1. Create web interface - To access data - Importing external data in compatible formats 2. Interactive optimization tool - Working on map - Optimizes service locations and allocations for given cost provision site for specific groups 3. Algorithms and methods - Balanced data clustering - Network optimization - Data analysis 23
  • 25. 25 Health care services provided 1. Health center 2. Hospital 3. Dental 4. Rehabilitation 5. Aging therapies 6. Aging accomodation 7. Children's daycare
  • 27. Clustering of data filtering?
  • 30. OpenOpen clustercluster OpenOpen clustercluster OpenOpen clustercluster Asia clusterAsia cluster PeninsularPeninsular MalaysiaMalaysia Singapore clusterSingapore cluster Singapore flierSingapore flier Two more clicks Two more clicks 5 Clusters in Mopsi
  • 31. M. Rezaei and P. Fränti "Real-time clustering of large geo-referenced data for visualizing on map", Advances in Electrical and Computer Engineering, 2018. 220 photos220 photos 9 clusters9 clusters 47 photos47 photos 14 clusters14 clusters 14 photos14 photos 7 clusters7 clusters OpenOpen clustercluster OpenOpen clustercluster Clusters in Mopsi
  • 32. 32 Various map view prototypes Oulun karttaratkaisu Pasi’s team proto Radu’s quick & dirty http://cs.uef.fi/impro_dev/map/map.php
  • 33. Making the tools to meet the data Data Knowledge Helsinki
  • 34. 1. Esophageal varices – Fibrosis and Cirrhosis of liver 2. Malignant neoplasm of pancreas – Aftercare 3. Sleep disorders – Non-classified respiratory failure … Correlation analysis of patient records
  • 35. 35 Service locations in city Clustering problem
  • 36. 36 Services in rural area: touring nurse
  • 40. Worker-task allocation Home take care proto MOPSI project Task scheduling
  • 42. Market place of services Ideal case 15 € 11 € 12 € 70 € 15 € 10 € 45 € 52 € 60 € 55 € 12 € 45 € 25 € 120 € 500 € 120 € Market
  • 43. 70 € 45 € 52 € 60 € 55 € 12 € 45 € 25 € 120 € 500 € 120 € Market Government Market place of services Possible future scenario 15 € 11 € 12 € 15 € 10 €
  • 44. 44 What to optimize exactly? ?
  • 45. Market Case 1: No services, no costs 0 €
  • 46. Market Case 2: Full service to everyone 1,000,000,000…€
  • 47. 47 … future under process… The end