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CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
CityPulse - Wright State University
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CityPulse - Wright State University

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  • Citizens are central to a city, country or in the past kingdom
    Since the beginning of civilizations (or settlements) around 8000 BC, people have moved toward living in ‘cities’
    Kings and emperors realized the importance of understanding citizen moods, sentiments, and opinions in making decisions
    Qianlong emperor is one such example
    Concept applies to even today!
  • - Connection to people is the key! We always want to hear citizens talk about both good and bad in a city
    - Good will help us know what works and bad will help us prioritize and work toward making it better
    Social media such as twitter, FB, myspace, and many others gives direct access to what citizens think about a city
    Best way to tap into the problems and challenges citizens face in a city
  • Data gathered in a city by various departments
    Citizens reporting their observations of city infrastructure
    You may ask do citizens really talk about city infrastructure?
  • Twitter as a source of real-time information
    There are over 200 million users generating 500 million tweets / day
    Twitter as a source of events in a city
    Citizens use twitter to express their concerns of city infrastructure that impacts their life
  • There are some knowledge bases from IBM Smart Planet initiative that can help us for city events
  • Categorization of severity based on weather conditions. Actionable information is contextually dependent.
  • Source: Purohit et. al 2013 (https://docs.google.com/a/knoesis.org/document/d/1aBJ2egHICUwaWxR8jOoTIUfEYj1QAnUt0q7haIKoYGY/edit# , http://www.knoesis.org/library/resource.php?id=1865)
  • Explain about continuous semantics
  • Transcript

    • 1. CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications Pramod Anantharam and Amit Sheth (in collaboration with Payam Barnaghi, University of Surrey) Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA 1
    • 2. Relevance to CityPulse Activity 3.3: Data Aggregation and Abstraction (Data Fusion) (Month 7 – Month 24) Activity 3.4: Event Detection for Urban Data Streams (Month 19 – Month 30) Activity 5.1: Real-Time Adaptive Urban Reasoning (Month 4– Month 24) 4
    • 3. Activity 3.3: Data Aggregation and Abstraction (Data Fusion) (Month 7 – Month 24) Activity 3.4: Event Detection for Urban Data Streams (Month 19 – Month 30) Activity 5.1: Real-Time Adaptive Urban Reasoning (Month 4– Month 24) 5
    • 4. A Semantic Approach to Machine Perception Making sense of sensor data with Slides 9 to 23 borrowed from: Cory Henson, Researcher, Kno.e.sis http://www.slideshare.net/andrewhenson/a-semanticsbased-approach-to-machine-perception 6
    • 5. Primary challenge is to bridge the gap between data and knowledge KNOWLEDGE situation awareness useful for decision making DATA sensor observations 7
    • 6. mo re Levels of Abstraction Interpreted data Intellego (abductive) [in OWL] e.g., diagnosis 2 Interpreted data (deductive) [in OWL] SSN Ontology e.g., threshold … 3 … Elevated Blood Pressure … 1 Annotated Data [in RDF] e.g., label Systolic blood pressure of 150 mmHg les s us efu l… us efu l Hyperthyroidism 0 “150” Raw Data [in TEXT] e.g., number 8
    • 7. Ontology of Perception Low-level observed properties suggest explanatory hypotheses through abduction Explanation Explanation Observed Propertie s Perceived Features Background knowledge on the Web Focus Focus 9 An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology,
    • 8. Ontology of Percetion Explanation Explanation Observed Propertie s Perceived Features Background knowledge on the Web Focus Focus Hypotheses imply the informational value of future observations through deduction 10 An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology,
    • 9. Semantics of Explanation Abduction – or, inference to the best EXPLANATION Task •Given background knowledge of the environment (SIGMA), and •given a set of sensor observation data (RHO), •find a consistent explanation of the situation (DELTA) Σ Background knowledge ∪∆ Features (objects/events ) in the world ρ Sensor observation data 11
    • 10. Semantics of Explanation Σ Background knowledge is represented as a causal network between features (objects or events) in the world and the sensor observations they give rise to. 12
    • 11. Semantic Perception on Resource Constrained Devices Off-the-shelf OWL-DL reasoners are too resource intensive in terms of both memory and time •Runs out of resources with background knowledge >> 20 nodes •Asymptotic complexity: O(n3) O(n3)3)<<xx<<O(n4)4) O(n O(n 13
    • 12. Relevance to CityPulse Activity 3.3: Data Aggregation and Abstraction (Data Fusion) (Month 7 – Month 24) Activity 3.4: Event Detection for Urban Data Streams (Month 19 – Month 30) Activity 5.1: Real-Time Adaptive Urban Reasoning (Month 4– Month 24) 14
    • 13. A Historical Perspective on Cities and its Inhabitants A Historical Perspective on Cities and its Inhabitants “kings, emperors and other rulers benefited from being on the “kings, emperors and other rulers benefited from being on the front lines with their people when it came to making front lines with their people when it came to making decisions.”11 decisions.” Disguised as a commoner, Disguised as a commoner, Qianlong visited cities to Qianlong visited cities to understand a common man’s life understand a common man’s life This is popularly known as This is popularly known as “Management by Walking Around” “Management by Walking Around” since the 1980’s since the 1980’s Qianlong Emperor (8 October 1735 – 9 February 1796) Qing Dynasty (1644–1912) http://gicoaches.com/what-we-can-learn-from-kings-of-the-past-who-disguised-themselves-as-ordinary-men/ http://en.wikipedia.org/wiki/Qianlong_Emperor 1
    • 14. A Modern Perspective on Cities and its Inhabitants A Modern Perspective on Cities and its Inhabitants City authorities, government and other humanitarian agencies City authorities, government and other humanitarian agencies are benefited from being on the front lines with their people are benefited from being on the front lines with their people when it comes to making decisions. when it comes to making decisions. We want to be connected to We want to be connected to citizens to understand and citizens to understand and prioritize decisions prioritize decisions
    • 15. Pulse of a City (CityPulse) Pulse of a City (CityPulse) Public Safety Urban planning Gov. & agency admin. Energy & water Environmental Transportation Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html Social Programs Healthcare Education
    • 16. What are People Talking About City Infrastructure on What are People Talking About City Infrastructure on Twitter? Twitter?
    • 17. Research Questions Research Questions − What are people talking about city infrastructure on twitter? − How do 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?
    • 18. Some Challenges in Extracting Events from Tweets 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 an 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
    • 19. Mu lt i An Face al y t ed s is ntic a Sem ation cial plic So Ap Web http://twitris.knoesis.org/ Real time Insights of Im portant Events includ i ng disaster respo nse coordination http://usatoday30.usatoday.com/news/politics/twitter-election-meter 21
    • 20. Twitris: Analysis by Location Twitris: Analysis by Location How People from How People from Different parts of the Different parts of the world talked about US world talked about US Election Election Images and Images and Videos Related to Videos Related to US Election US Election 22
    • 21. Twitris: Impact of Background Knowledge Twitris: Impact of Background Knowledge The Dead People The Dead People mentioned in the mentioned in the event OWC event OWC 23
    • 22. What is Smart Data in the context of What is Smart Data in the context of Disaster Management Disaster Management ACTIONABLE: Timely ACTIONABLE: Timely delivery of right delivery of right resources and resources and information to the right information to the right people at right location! people at right location! Because everyone wants to Help, but DON’T KNOW HOW! Because everyone wants to Help, but DON’T KNOW HOW! 24
    • 23. Disaster Response Coordination Framework Disaster Response Coordination Framework Source: Purohit et. al 2013, Information Filtering and Management Model for Disaster 25
    • 24. Activity 3.3: Data Aggregation and Abstraction (Data Fusion) (Month 7 – Month 24) (UNIS, ERIC, SIE, UASO, WSU) Activity 3.4: Event Detection for Urban Data Streams (Month 19 – Month 30) (SIE, UNIS, ERIC, WSU) Activity 5.1: Real-Time Adaptive Urban Reasoning (Month 4– Month 24) (NUIG, UNIS, ERIC, SIE, WSU) 26
    • 25. Continuous Semantics Continuous Semantics 27
    • 26. Dynamic Model Creation Heliopolis is a Heliopolis is a suburb of suburb of Cairo. Cairo. 28
    • 27. Dynamic Model Creation: Events “Both Ahmadinejad & Mousavi “Both Ahmadinejad & Mousavi declare victory in Iranian declare victory in Iranian Elections.” Elections.” June 12 2009 “situation in tehran University is “situation in tehran University is so worrisome. police have so worrisome. police have attacked to girls dormitory attacked to girls dormitory #tehran #iranelection” #tehran #iranelection” June 13 2009 “Reports from Azadi Square - -44 “Reports from Azadi Square people killed by police, people people killed by police, people killed police who shot. More killed police who shot. More shots being fired shots being fired #iranelections” June 15 2009 #iranelections” Key phrases Ahmadinejad & Ahmadinejad & Tehran Tehran Azadi Square is Azadi Square is Mousavi area Mousavi is Universityarea aUniversity isin city square in apoliticians in city square politicians in University University Tehran Tehran Iran Iran Models Example of how background knowledge help understand situation described in the tweets, while also updating knowledge model also 29
    • 28. Summarizing Continuous Semantics Summarizing Continuous Semantics Keeping the Background Keeping the Background Knowledge abreast with the Knowledge abreast with the changes of the event changes of the event Smartly learning and adapting data Smartly learning and adapting data acquisition (Temporally apt Big acquisition (Temporally apt Big Data, i.e. Fast Data) Data, i.e. Fast Data) In-turn providing temporally In-turn providing temporally relevant Smart Data through relevant Smart Data through analysis analysis 30
    • 29. Thanks! 31

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