1 
Thanks Paya Barnaghi
Kno.e.sis in 2013 = ~100 researchers (15 faculty, ~50 PhD students) 
Amit Sheth’s 
PHD students 
Ashutosh Jadhav 
Hemant 
...
• Among top universities in the world in World Wide Web (cf: 10-yr impact, 
Microsoft Academic Search: among top 10 in Jun...
Top organization in WWW: 10-yr Field Rating 
(MAS) 
4
• Social Media Big Data – Twitris, eDrugTrends 
• Sensor/IoT Big Data – CityPulse, kHealth 
• Healthcare Big Data – kHealt...
6 
Smart Cities and 
Back to the future 
Thanks to Dr. Payam Barnaghi for sharing the slide
7 
Future cities: a view from 1998 
Source LAT Times, http://documents.latimes.com/la-2013/ 
Thanks to Dr. Payam Barnaghi ...
8 
Image courtesy: Avatar wiki 
Thanks to Dr. Payam Barnaghi for sharing the slide
9 
Thanks to Dr. Payam Barnaghi for sharing the slide
Imperatives 
Why: 
Improved Economic/Social/Human development in an era of 
increased Urbanization 
What? 
All aspects of ...
Enablers of Economic Developments 
Economic de Civilizations on river banks velopment on trade routes 
Economic developmen...
General Economic Trends 
Over 340 million people live in cities of India in 2008 and it is expected to 
grow to 590 millio...
One aspect of characterizing a City: All its 
functions 
Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_citi...
14
Five Key Elements of Smart City* 
Utility Services 
Transportation Services 
Social Infrastructure 
Safety & health Servic...
Unprecedented Digital Data Growth 
• Every thing is becoming data driven 
• Many types of data: Physical, Cyber, and Socia...
Understanding wealth of data 
• Increased citizen participation (Social) 
• Increase monitoring using sensors (Physical) 
...
What do we need for developing Smart 
18 
City Applications? 
Physical 
Amit Sheth, Pramod Anantharam, Cory Henson, 'Physi...
19 
Physical: Sensors monitoring physical 
world 
- Programmable devices 
- Off-the-shelf gadgets/tools 
Thanks to Dr. Pay...
20 
Cyber: Observations pushed to the 
cyber world 
Thanks to Dr. Payam Barnaghi for sharing the slide
21 
Social: People interacting with the 
physical world 
ECG sensor 
Motion sensor 
Motion sensor 
Motion sensor 
World Wi...
Scope of this talk 
• Smart City application in Indian Context 
• Smart City Use Cases in Developed World 
– Smart City ap...
• Smart City application in Indian Context 
• Smart City Use Cases in Developed World 
– Smart City application in Europea...
Dynamic schedule update of Public Transport vehicles 
in A CITY Lacking Traffic Instrumentation* 
Pramod Anantharam 
Joint...
Motivation 
By 2001 over 285 million Indians lived in cities, more than in all 
North American cities combined (Office of ...
Motivation: Why SMS for Events? 
• Prevalence 
– In India, 11 cities provide notifications to citizens using SMS 
– SMS ba...
Problem 
• Input: 
– Traffic related text alerts, domain knowledge, public 
transport routes, and historical data. 
• Outp...
Solution Components 
As events are reported to MDU (Multi-modal Dynamic Update): 
• Traffic event detection from SMS alert...
eventtype = BreakDown 
eventdescription = “Traffic movement is slow from Sanjay 
point towards Vasant Vihar due to break d...
Evaluation: Event Extraction 
•Run for ~50 messages in Delhi 
•Accurate extraction of location from, to 
and type. 
Sample...
Bayesian Model: Impact of Events on Delay 
The probability of having a delay at a stop , Si, given events observed at the ...
Impact (Delay) Propagation Across 
Stops 
Vehicle moves from S1 towards S4 
Actual steps are by loopy belief propagation a...
Application: adaptive route 
recommendations in IRL Transit 
33
1-Slide Summary: Multi-Mode Commuting Recommender in Delhi And Bangalore 
Highlights 
• Published data of multiple 
author...
IRL – Transit in Aug 2012 
Key Points 
•SMS message from city 
• Event and location identified 
• Impact assessed 
• Impac...
Matching Stop Names to OSM 
Location 
36 
• 3931 multi-modal stops in Delhi 
• Matching algorithm involves chucking of sto...
Event Information 
VasantVihar 
StartLoc = Sanjay Point 
EndLoc = VasantVihar 
OnLoc = Signal Enclave 
Lat-lon = 28.556102...
Evaluation: Reasoning over traffic events 
• Traffic alerts collected for 10 cities in India for 
two years. 
• Prior prob...
Number of SMS messages for bus stops in 
Delhi for 2 years (Aug 2010 – Aug 2012) 
• 344 stops 
with updates 
• 3931 total ...
• Smart City application in India Context 
• Smart City Use Cases in Developed World 
– Smart City application in European...
41 
CityPulse Consortium 
Partners: 
Industrial SIE, ERIC 
SME AI, 
Higher 
Education 
UNIS, NUIG, 
UASO, WSU 
City BR, AA...
CityPulse 
42
43 
Exposure APIs 
Analytics 
Toolbox 
Context-aware 
Decision 
Support, 
Visualisation 
Knowledge-based 
Stream 
Processi...
In summary 
44 
Data:
Use cases 
45
Scenario ranking 
46
101 Scenarios 
47
101 Scenarios 
• http://www.ict-citypulse. 
eu/page/content/smart-city- 
use-cases-and-requirements 
48
Public parking space availability prediction 
• Finding parking space in a city can be challenging 
• Predicting the proba...
• Smart City application in India Context 
• Smart City Use Cases in Developed World 
– Smart City application in European...
Extracting City Events from Social 
Streams 
Toward a Citizen Centered Smart City 
Pramod Anantharam1 
1Kno.e.sis – Ohio C...
Pulse of a City (CityPulse) 
Public Safety Urban planning Gov. & agency 
admin. 
Energy & 
water 
Environmental Transporta...
Research Questions 
• Are people talking about city infrastructure on 
twitter? 
• Can we extract city infrastructure rela...
Are People Talking About City Infrastructure on Twitter? 
54
Some Challenges in Extracting Events from Tweets 
• No well accepted definition of ‘events related to a 
city’ 
• Tweets a...
Open Domain [Kumaran and Allan 2004] [Roitman et al. 2012] 
[Ritter et al. 2012] 
[Wang et al. 2012] 
Formal Text Informal...
Tweets from a city 
City Infrastructure 
POS 
Tagging 
Hybrid NER+ 
Event term 
extraction 
Impact 
Assessment 
Temporal 
...
Evaluation 
• City Event Annotation 
– Automated creation of training data 
– Annotation task (our CRF model vs. baseline ...
Ground Truth Data (only incident reports) -- City Event Extraction 
We have around 162 million data records from sensors m...
Evaluation – Extracted Events AND Ground Truth 
60
Traffic Analytics using Probabilistic Graphical Models 
Enhanced with Knowledge Bases 
Pramod Anantharam, T. K. Prasad, Am...
Slow moving 
traffic 
Link 
Description 
Scheduled 
Event 
Scheduled 
Event 
511.org 
511.org 
Schedule Information 
511.o...
Uncertainty in the Real-world 
• Observation: Slow Moving Traffic 
• Multiple Causes (Uncertain about the cause): 
– Sched...
Why Probabilistic Graphical Models? 
“As far as the laws of mathematics refer to reality, they are not 
certain, as far as...
Graphical Models – Bayesian Network 
Example 
Cold 
T 0.33 
F 0.67 
IcyRoad PoorVisibilit 
SlowTraffic 
y 
Random 
variabl...
How do we get nodes and edges? 
Domain Experts 
Declarative domain knowledge 
ColdWeather 
PoorVisibility 
SlowTraffic 
Va...
Domain Knowledge 
• Declarative knowledge about various domains 
are increasingly being published on the web1,2. 
• Declar...
ConceptNet 5 
ScheduledEvent 
http://conceptnet5.media.mit.edu/web/c/en/traffic_jam 
Delay 
go to baseball game 
traffic j...
Key Idea 
• Probabilistic Graphical Models (PGM) use 
statistical approaches to uncover correlations. 
• Declarative knowl...
Complementing graphical model structure extraction 
traffic jam CapableOfoccur twice each day 
traffic jam CapableOf slow ...
Smart Cities: Opportunities 
• empower citizens 
• provide more business opportunities for 
companies (and SMEs) and priva...
Smart Cities: Challenges 
• Adherence to open data standards by all the 
city authorities 
• Sufficient guidance and suppo...
Thank you  
73
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Role of Big Data for Smart City Applications

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Talk given to the Smart City course students at CEPT University. Oct 19, 2014.

* Overview on Physical (IoT/Sensor), Cyber (OpenGov) and Social (citizen Sensing) data
* Relevance to City Departments
* Three smart city applications (from India, Europe and US)

More on the course: http://indianexpress.com/article/india/india-others/cept-launches-first-ever-course-on-smart-cities/

Published in: Data & Analytics

Role of Big Data for Smart City Applications

  1. 1. 1 Thanks Paya Barnaghi
  2. 2. Kno.e.sis in 2013 = ~100 researchers (15 faculty, ~50 PhD students) Amit Sheth’s PHD students Ashutosh Jadhav Hemant Purohit Vinh Nguyen Lu Chen Pramod Sujan Anantharam Perera Alan Smith Maryam Panahiazar Sarasi Lalithsena Cory Henson Kalpa Gunaratna Delroy Cameron Sanjaya Wijeratne Wenbo Wang Pavan Kapanipathi Shreyansh Bhatt Acknowledgements: Kno.e.sis team, Funds - NSF, NIH, AFRL, Industry… 2
  3. 3. • Among top universities in the world in World Wide Web (cf: 10-yr impact, Microsoft Academic Search: among top 10 in June2014) • Among the largest academic groups in the US in Semantic Web + Social/Sensor Webs, Mobile/Cloud/Cognitive Computing, Big Data, IoT, Health/Clinical & Biomedicine Applications • Exceptional student success: internships and jobs at top salary (IBM Watson/Research, MSR, Amazon, CISCO, Oracle, Yahoo!, Samsung, research universities, NLM, startups ) • 100 researchers including 15 World Class faculty (>3K citations/faculty avg) and ~45 PhD students- practically all funded • Extensive research for largely multidisciplinary projects; world class resources; industry sponsorships/collaborations (Google, IBM, …) 3
  4. 4. Top organization in WWW: 10-yr Field Rating (MAS) 4
  5. 5. • Social Media Big Data – Twitris, eDrugTrends • Sensor/IoT Big Data – CityPulse, kHealth • Healthcare Big Data – kHealth, EMR, Prediction • Biomedical Big Data – Biomarker from NextGen Sequencing and Proteomics, SCOONER • Big and Smart Data Certificate Kno.e.sis private cloud: 864 CPU cores, 18TB RAM, 17TB SSD, 435TB disk 5
  6. 6. 6 Smart Cities and Back to the future Thanks to Dr. Payam Barnaghi for sharing the slide
  7. 7. 7 Future cities: a view from 1998 Source LAT Times, http://documents.latimes.com/la-2013/ Thanks to Dr. Payam Barnaghi for sharing the slide
  8. 8. 8 Image courtesy: Avatar wiki Thanks to Dr. Payam Barnaghi for sharing the slide
  9. 9. 9 Thanks to Dr. Payam Barnaghi for sharing the slide
  10. 10. Imperatives Why: Improved Economic/Social/Human development in an era of increased Urbanization What? All aspects of economy: Agricultural + Manufacturing + Service + Knowledge How? • Next 10
  11. 11. Enablers of Economic Developments Economic de Civilizations on river banks velopment on trade routes Economic development now increasingly rely on digital infrastructure 11 Image credit: http://www.rcet.org/twd/students/socialstudies/ss_extensions_1intro.html Image credit: http://www.shutterstock.com/pic-157118819/stock-vector-conceptual-tag-cloud-containing-words-related-to-smart-city-digital-city-infrastructure-ict.html
  12. 12. General Economic Trends Over 340 million people live in cities of India in 2008 and it is expected to grow to 590 million by 2030 leading to rapid urbanization1 We are increasingly moving from Agriculture  Industry  Services The next growth should be toward Knowledge Economy 1http://www.mckinsey.com/insights/urbanization/urban_awakening_in_india 12
  13. 13. One aspect of characterizing a City: All its functions Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html 13
  14. 14. 14
  15. 15. Five Key Elements of Smart City* Utility Services Transportation Services Social Infrastructure Safety & health Services Recycling Services * By Indian Urban Development Ministry 15
  16. 16. Unprecedented Digital Data Growth • Every thing is becoming data driven • Many types of data: Physical, Cyber, and Social • Effective collection and use of this Big Data has to be a core part of designing Smart Cities http://www.tribalcafe.co.uk/big-data-infographic/ 16
  17. 17. Understanding wealth of data • Increased citizen participation (Social) • Increase monitoring using sensors (Physical) • Increase Digital Government (eGov) data (Cyber) Let’s not develop future applications with constraints of the past India ranks 8th in civic engagement! http://www.informationweek.com/government/leadership/digital-civic-engagement-us-lags/d/d-id/1113938 17
  18. 18. What do we need for developing Smart 18 City Applications? Physical Amit Sheth, Pramod Anantharam, Cory Henson, 'Physical-Cyber-Social Computing: An Early 21st Century Approach,' IEEE Intelligent Systems, vol. 28, no. 1, pp. 78-82, Jan.-Feb., 2013. http://doi.ieeecomputersociety.org/10.1109/MIS.2013.20 http://wiki.knoesis.org/index.php/PCS Cyber Social* Developers need to Consider observations from Physical-Cyber-Social systems in Building Smart City applications *http://www.ichangemycity.com/
  19. 19. 19 Physical: Sensors monitoring physical world - Programmable devices - Off-the-shelf gadgets/tools Thanks to Dr. Payam Barnaghi for sharing the slide
  20. 20. 20 Cyber: Observations pushed to the cyber world Thanks to Dr. Payam Barnaghi for sharing the slide
  21. 21. 21 Social: People interacting with the physical world ECG sensor Motion sensor Motion sensor Motion sensor World Wide Web Road block, A3 Road block, A3 Thanks to Dr. Payam Barnaghi for sharing the slide
  22. 22. Scope of this talk • Smart City application in Indian Context • Smart City Use Cases in Developed World – Smart City application in European Context – Smart City application in US context 22
  23. 23. • Smart City application in Indian Context • Smart City Use Cases in Developed World – Smart City application in European Context – Smart City application in US context 23
  24. 24. Dynamic schedule update of Public Transport vehicles in A CITY Lacking Traffic Instrumentation* Pramod Anantharam Joint work with Biplav Srivastava and Raj Gupta, IBM IRL Aug 31, 2012 *Work done as part of internship at IBM Research 24
  25. 25. Motivation By 2001 over 285 million Indians lived in cities, more than in all North American cities combined (Office of the Registrar General of India 2001)1 1The Crisis of Public Transport in India 2IBM Smarter Traffic Texas Transportation Institute (TTI) Congestion report in U.S. Modes of transportation in Indian Cities 25
  26. 26. Motivation: Why SMS for Events? • Prevalence – In India, 11 cities provide notifications to citizens using SMS – SMS based alerts common for business transactions – Low-cost phones constitute 95% of all phones (~930 million mobile connections in India2) • Social media (Facebook, Twitter) and SMS – Commuters prefer dynamic updates such as SMS verses any other form of traffic updates1. 26 1Caulfield et al. Factors Which Influence the Preferences for real-time Public Transport Information, Association of European Transport and contributors 2007 2http://en.wikipedia.org/wiki/Communications_in_India
  27. 27. Problem • Input: – Traffic related text alerts, domain knowledge, public transport routes, and historical data. • Output – Events in desired form – Impact of events on public transport routes (e.g. probability of delay given location + event) • Challenges – No instrumentation (sensors) leading to sparse and imprecise information, event extraction from free text. 27
  28. 28. Solution Components As events are reported to MDU (Multi-modal Dynamic Update): • Traffic event detection from SMS alerts – event <Type, Time (Reported, Published), Location (From, To, On), Description> • Reasoning over traffic events for delay assessment – Find stops in the region affected by event (Qualitative) – Estimate delay at stops (Quantitative) • Consider time of day and history of such events • Have an attenuation function based on event types – Propagate delay estimates to neighboring stops • Account for time, schedule and direction of travel 28
  29. 29. eventtype = BreakDown eventdescription = “Traffic movement is slow from Sanjay point towards Vasant Vihar due to break down of an HTV in front of Signal Enclave.msg@10.15am,210612.” eventstartloc = Sanjay Point eventendloc = Vasant vihar eventonloc = Signal Enclave eventtime = June 21, 2012, 10:15am c.p.w.d.cly. vasant vihar vasant vihar depot. paschim marg vasant vihar vasant vihar(t) Signal Enclave Vasant Vihar vasant vihar model school c.p.w.d.cly. vasant vihar paschim marg vasant vihar vasant vihar(t) vasant vihar depot. “Traffic movement is slow from Sanjay point towards Vasant Vihar due to break down of an HTV in front of Signal Enclave.msg@10.15am,210612.” Illustration from New Delhi (India) 29
  30. 30. Evaluation: Event Extraction •Run for ~50 messages in Delhi •Accurate extraction of location from, to and type. Sample 30
  31. 31. Bayesian Model: Impact of Events on Delay The probability of having a delay at a stop , Si, given events observed at the stop, is given by 31
  32. 32. Impact (Delay) Propagation Across Stops Vehicle moves from S1 towards S4 Actual steps are by loopy belief propagation algorithm Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Tech-niques. MIT Press (2009) Assumption: delay at a node is directly influenced by delay at the next node. 32
  33. 33. Application: adaptive route recommendations in IRL Transit 33
  34. 34. 1-Slide Summary: Multi-Mode Commuting Recommender in Delhi And Bangalore Highlights • Published data of multiple authorities used; repeatable process •Multiple modes searched • Preference over modes, time, hops and number of choices supported; more extensions, like fare possible • Integration of results with map as future work; already done as part of other projects, viz. SCRIBE-STAT IRL – Transit on March 2012 First Version 34
  35. 35. IRL – Transit in Aug 2012 Key Points •SMS message from city • Event and location identified • Impact assessed • Impact used in search 35
  36. 36. Matching Stop Names to OSM Location 36 • 3931 multi-modal stops in Delhi • Matching algorithm involves chucking of stop names, distance metrics, and voting to select the best match. • Matches categorized as confident (>50% of techniques resulted in the match), possible (=50%), and uncertain (<50%). • 1496 (confident matches) stops mapped to OSM locations. OSM names
  37. 37. Event Information VasantVihar StartLoc = Sanjay Point EndLoc = VasantVihar OnLoc = Signal Enclave Lat-lon = 28.5561025,77.1645187 textual observation IRL-Transit routes (ordered) Background knowledge GIS Location Assess impact Observations parameterize Fetch text message Extract Locations StartLoc, EndLoc, and OnLoc Is location present? Extract Event Information Extract GIS location using Open Street Maps No Yes Is event present? Domain knowledge of categorization of events Text message with metadata Yes No Traffic movement is slow from Sanjay point towards VasantVihar due to break down of an HTV in front of Signal Enclave.msg@10.15am,210612. STOPID STOPNAME 321 c.p.w.d. cly. vasantvihar 369 vasantvihar (t) 814 vasantvihar model school 956 vasantviharcpwdcly. 957 paschimmargvasantvihar 1274 vasantvihar depot STOPID STOPNAME 957 paschimmargvasantvihar 814 vasantvihar model school 321 c.p.w.d. cly. vasantvihar 956 vasantviharcpwdcly. 1274 vasantvihar depot 369 vasantvihar (t) StartLoc = Sanjay Point EndLoc = VasantVihar OnLoc = Signal Enclave Event = break down of an HTV StartLoc = Sanjay Point EndLoc = VasantVihar OnLoc = Signal Enclave Event = break down of an HTV Event Type = BreakDown IRL-Transit routes (unordered) Signal Enclave 37
  38. 38. Evaluation: Reasoning over traffic events • Traffic alerts collected for 10 cities in India for two years. • Prior probability of events computed using these alerts. • Probability of having a delay given an event type at ten locations in Delhi is summarized: 38
  39. 39. Number of SMS messages for bus stops in Delhi for 2 years (Aug 2010 – Aug 2012) • 344 stops with updates • 3931 total stops 39
  40. 40. • Smart City application in India Context • Smart City Use Cases in Developed World – Smart City application in European Context – Smart City application in US context 40
  41. 41. 41 CityPulse Consortium Partners: Industrial SIE, ERIC SME AI, Higher Education UNIS, NUIG, UASO, WSU City BR, AA Duration: 36 months
  42. 42. CityPulse 42
  43. 43. 43 Exposure APIs Analytics Toolbox Context-aware Decision Support, Visualisation Knowledge-based Stream Processing Real-Time Monitoring & Testing Accuracy & Trust Modelling Semantic Integration On Demand Data Federation Open Reference Data Sets Real-Time IoT Information Extraction IoT Stream Processing Federation of Heterogenous Data Streams Design-Time Run-Time Testing
  44. 44. In summary 44 Data:
  45. 45. Use cases 45
  46. 46. Scenario ranking 46
  47. 47. 101 Scenarios 47
  48. 48. 101 Scenarios • http://www.ict-citypulse. eu/page/content/smart-city- use-cases-and-requirements 48
  49. 49. Public parking space availability prediction • Finding parking space in a city can be challenging • Predicting the probability of parking given various input variables such as scheduled events, time of day & location. • Reduced emission and frustration for citizens http://www.ict-citypulse.eu/scenarios/scenarios 49
  50. 50. • Smart City application in India Context • Smart City Use Cases in Developed World – Smart City application in European Context – Smart City application in US context 50
  51. 51. Extracting City Events from Social Streams Toward a Citizen Centered Smart City Pramod Anantharam1 1Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA http://www.ict-citypulse.eu/page/ Mentor/Supervisor: Dr. Payam Barnaghi 51
  52. 52. Pulse of a City (CityPulse) Public Safety Urban planning Gov. & agency admin. Energy & water Environmental Transportation Social Programs Healthcare Education Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html 52
  53. 53. Research Questions • Are people talking about city infrastructure on twitter? • Can we extract city infrastructure related events from twitter? • How can we leverage event and location knowledge bases for event extraction? • How well can we extract city events? 53
  54. 54. Are People Talking About City Infrastructure on Twitter? 54
  55. 55. Some Challenges in Extracting Events from Tweets • No well accepted definition of ‘events related to a city’ • Tweets are short (140 characters) and its informal nature make it hard to analyze – Entity, location, time, and type of the event • Multiple reports of the same event and sparse report of some events (biased sample) – Numbers don’t necessarily indicate intensity • Validation of the solution is hard due to the open domain nature of the problem 55
  56. 56. Open Domain [Kumaran and Allan 2004] [Roitman et al. 2012] [Ritter et al. 2012] [Wang et al. 2012] Formal Text Informal Text Closed Domain [Lampos and Cristianini 2012] [Becker et al. 2011] Related Work on Event Extraction 56
  57. 57. Tweets from a city City Infrastructure POS Tagging Hybrid NER+ Event term extraction Impact Assessment Temporal Estimation Event Aggregation Geohashing OSM Locations SCRIBE ontology 511.org hierarchy City Event Extraction City Event Extraction Solution Architecture City Event Annotation 57
  58. 58. Evaluation • City Event Annotation – Automated creation of training data – Annotation task (our CRF model vs. baseline CRF model) • City Event Extraction – Use aggregation algorithm for event extraction – Extracted events AND ground truth • Dataset (Aug – Nov 2013) ~ 8 GB of data on disk – Over 8 million tweets – Over 162 million sensor data points – 311 active events and 170 scheduled events 58
  59. 59. Ground Truth Data (only incident reports) -- City Event Extraction We have around 162 million data records from sensors monitoring over 3,700 links in San Franciso Bay Area <link_id, link_speed, link_volume, link_travel_time,time_stamp>  a data record GREEN – Active Events YELLOW – Scheduled Events 311 active events and 170 scheduled events 59
  60. 60. Evaluation – Extracted Events AND Ground Truth 60
  61. 61. Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases Pramod Anantharam, T. K. Prasad, Amit Sheth Ohio Center of Excellence in Knowledge-enabled Computing (kno.e.sis) Wright State University, Dayton, Ohio 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) 61
  62. 62. Slow moving traffic Link Description Scheduled Event Scheduled Event 511.org 511.org Schedule Information 511.org 62
  63. 63. Uncertainty in the Real-world • Observation: Slow Moving Traffic • Multiple Causes (Uncertain about the cause): – Scheduled Events: music events, fair, theatre events, concerts, road work, repairs, etc. – Active Events: accidents, disabled vehicles, break down of roads/bridges, fire, bad weather, etc. – Peak hour: e.g. 7 am – 9 am OR 4 pm – 6 pm • Each of these events may have a varying impact on traffic 63
  64. 64. Why Probabilistic Graphical Models? “As far as the laws of mathematics refer to reality, they are not certain, as far as they are certain, they do not refer to reality” -- Albert Einstein, 1921. “Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering -- uncertainty and complexity …” -- Michael Jordan, UC Berkley, 1998. 64
  65. 65. Graphical Models – Bayesian Network Example Cold T 0.33 F 0.67 IcyRoad PoorVisibilit SlowTraffic y Random variable cold T F 0.75 0.05 0.25 0.95 Edge between random variables which is indicative of conditional independence IcyRoad T F cold T F 0.85 0.40 0.15 0.60 PoorVisibility T F cold T F IcyRoad PoorVisibility T F T F 0.85 0.4 0.9 0.2 0.15 0.6 0.1 0.8 SlowTraffic T F Conditional Probability Table A graphical model has structure (nodes and edges) (CPT) and parameters; CPD – continuous variables, CPT – discrete variables 65
  66. 66. How do we get nodes and edges? Domain Experts Declarative domain knowledge ColdWeather PoorVisibility SlowTraffic Variables and relationships IcyRoad Causal knowledge Linked Open Data Domain Observations ColdWeather(YES/NO) IcyRoad (ON/OFF) PoorVisibility (YES/NO) SlowTraffic (YES/NO) 1 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 Domain Knowledge Structure and parameters 66 WinterSeaso n
  67. 67. Domain Knowledge • Declarative knowledge about various domains are increasingly being published on the web1,2. • Declarative knowledge describes concepts and relationships in a domain (structure). • Linked Open Data may be used to derive priors probability of events (parameters). • In this work, we focus only on use of declarative knowledge for structure using ConceptNet 5. 1http://conceptnet5.media.mit.edu/ 2http://linkeddata.org/ 67
  68. 68. ConceptNet 5 ScheduledEvent http://conceptnet5.media.mit.edu/web/c/en/traffic_jam Delay go to baseball game traffic jam traffic accident traffic jam ActiveEvent Causes Causes traffic jam traffic jam CapableOf slow traffic CapableOf occur twice each day Causes is_a bad weather CapableOf slow traffic road ice Causes accident TimeOfDay go to concert HasSubevent car crash accident RelatedTo car crash BadWeather Causes Causes is_a is_a is_a is_a is_a is_a is_a 68
  69. 69. Key Idea • Probabilistic Graphical Models (PGM) use statistical approaches to uncover correlations. • Declarative knowledge curated by humans provide richer relationships including causal knowledge. • Goal: Utilizing declarative knowledge with PGM structure learning algorithms to build richer (quality and coverage) models. 69
  70. 70. Complementing graphical model structure extraction traffic jam CapableOfoccur twice each day traffic jam CapableOf slow traffic Traffic jam Link Description Add missing random variables Scheduled Event baseball game traffic jam slow traffic slow traffic slow traffic Time of day bad weather CapableOf slow traffic bad weather Traffic data from sensors deployed on road network in San Francisco Bay Area time of day baseball game traffic jam time of day Add missing links bad weather baseball game traffic jam time of day Add link direction bad weather baseball game traffic jam time of day go to baseball game Causes traffic jam Knowledge from ConceptNet5 70
  71. 71. Smart Cities: Opportunities • empower citizens • provide more business opportunities for companies (and SMEs) and private sector services • create better governance of our cities and better public services • provide smarter monitoring and control • improve energy efficiency, create greener environments… • create better healthcare, elderly-care… Thanks to Dr. Payam Barnaghi for sharing the slide 71
  72. 72. Smart Cities: Challenges • Adherence to open data standards by all the city authorities • Sufficient guidance and support for city authorities in managing their data • Reliability and quality of citizen reporting of city events • Privacy and Security issues in event reporting 72
  73. 73. Thank you  73

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