Citation:
Amit Sheth, "Semantics-empowered Smart City applications: today and tomorrow,” Keynote presented at the The 6th Workshop on Semantics for Smarter Cities (S4SC 2015), collocated with the 14th International Semantic Web Conference (ISWC2015), Bethlehem, PA, USA. Oct 11-12, 2015.
http://kat.ee.surrey.ac.uk/wssc/index.html
Abstract: There has been a massive growth in potentially relevant physical (sensor/IoT)- cyber (Web)- social data related to activities and operations of cities and citizens. As part of our participation in smart city projects, including the EU-funded CityPulse project, we have analyzed a large number of of use cases with inputs from city administrations and end users, and developed a few early applications. In this talk, I will present some exciting smart city applications possible today and venture to speculate on some future ones where Big Data technologies and semantic computing, including the use of domain knowledge, play a critical role.
Transforming Big Data into Smart Data for Smart Energy: Deriving Value via ha...Amit Sheth
Keynote at the Workshop on Building Research Collaboration: Electricity Systems. Purdue University, West Lafayette, IN. Aug 28-29, 2013.
Abstract:
Big Data has captured much interest in research and industry, with anticipation of better decisions, efficient organizations, and many new jobs. Much of the emphasis is on technology that handles volume, including storage and computational techniques to support analysis (Hadoop, NoSQL, MapReduce, etc), and the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity. However, the most important feature of data, the raison d'etre, is neither volume, variety, velocity, nor veracity -- but value. In this talk, I will emphasize the significance of Smart Data, and discuss how it is can be realized by extracting value from Big Data. Accomplishing this task requires organized ways to harness and overcome the original four V-challenges; and while the technologies currently touted may provide some necessary infrastructure-- they are far from sufficient. In particular, we will need to utilize metadata, employ semantics and intelligent processing, and leverage some of the extensive work that predates Big Data.
For achieving energy sustainability, Smart Grids are known to transform the way we generate, distribute, and consume power. Unprecedented amount of data is being collected from smart meters, smart devices, and sensors all throughout the power grid. I will discuss the central question of deriving Value from the entire smart grid data deluge by discussing novel algorithms and techniques such as Semantic Perception for dealing with Velocity, use of ontologies and vocabularies for dealing with Variety, and Continuous Semantics for dealing with Velocity. I will discuss scenarios that exemplify the process of deriving Value from Big Data in the context of Smart Grid.
Additional background is at: http://wiki.knoesis.org/index.php/Smart_Data
A previous version of this talk with more technical details but not focused on energy: http://j.mp/SmatData
Physical Cyber Social Computing: An early 21st century approach to Computing ...Amit Sheth
Keynote given at WiMS 2013 Conference, June 12-14 2013, Madrid, Spain. http://aida.ii.uam.es/wims13/keynotes.php
Video of this talk at: http://videolectures.net/wims2013_sheth_physical_cyber_social_computing/
More information at: More at: http://wiki.knoesis.org/index.php/PCS
and http://knoesis.org/projects/ssw/
Replacing earlier versions: http://www.slideshare.net/apsheth/physical-cyber-social-computing & http://www.slideshare.net/apsheth/semantics-empowered-physicalcybersocial-systems-for-earthcube
Abstract: The proper role of technology to improve human experience has been discussed by visionaries and scientists from the early days of computing and electronic communication. Technology now plays an increasingly important role in facilitating and improving personal and social activities and engagements, decision making, interaction with physical and social worlds, generating insights, and just about anything that an intelligent human seeks to do. I have used the term Computing for Human Experience (CHE) [1] to capture this essential role of technology in a human centric vision. CHE emphasizes the unobtrusive, supportive and assistive role of technology in improving human experience, so that technology “takes into account the human world and allows computers themselves to disappear in the background” (Mark Weiser [2]).
In this talk, I will portray physical-cyber-social (PCS) computing that takes ideas from, and goes significantly beyond, the current progress in cyber-physical systems, socio-technical systems and cyber-social systems to support CHE [3]. I will exemplify future PCS application scenarios in healthcare and traffic management that are supported by (a) a deeper and richer semantic interdependence and interplay between sensors and devices at physical layers, (b) rich technology mediated social interactions, and (c) the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning in order to bridge machine and human perceptions. I will share an example of PCS computing using semantic perception [4], which converts low-level, heterogeneous, multimodal and contextually relevant data into high-level abstractions that can provide insights and assist humans in making complex decisions. The key proposition is to explain that PCS computing will need to move away from traditional data processing to multi-tier computation along data-information-knowledge-wisdom dimension that supports reasoning to convert data into abstractions that humans are adept at using.
[1] A. Sheth, Computing for Human Experience
[2] M. Weiser, The Computer for 21st Century
[3] A. Sheth, Semantics empowered Cyber-Physical-Social Systems
[4] C. Henson, A. Sheth, K. Thirunarayan, Semantic Perception: Converting Sensory Observations to Abstractions
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Smart Data and real-world semantic web applications (2004)Amit Sheth
Probably the first recorded use of "smart data" for achieving the Semantic Web and for realizing productivity, efficiency, and effectiveness gains by using semantics to transform raw data into Smart Data.
2013 retake on this is discussed at: http://wiki.knoesis.org/index.php/Smart_Data
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Presented at SW2012 @ ISWC2012.
http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html
This is an old version of this talk, for more recent information on this topic (eg talks, papers, events), see: http://wiki.knoesis.org/index.php/PCS
Presented at the Panel on
Sensor, Data, Analytics and Integration in Advanced Manufacturing, at the Connected Manufacturing track of Bosch-USA organized "Leveraging Public-Private Partnerships for Regional Growth Summit". Panel statement: Sensors, data and analytics are the core of any smart manufacturing system. What are the main challenges to create actionable outputs, replicate systems and scale efficiency gains across industries?
Moderator: Thomas Stiedl, Bosch
Panelists:
1. Amit Sheth, Wright State University
2. Howie Choset, Carnegie Melon University
3. Nagi Gebraeel, Georgia Institute of Technology
4. Brian Anthony, Massachusetts Institute of Technology
5. Yarom Polosky, Oak Ridget National Laboratory
For in-depth look:
Smart IoT: IoT as a human agent, human extension, and human complement
http://amitsheth.blogspot.com/2015/03/smart-iot-iot-as-human-agent-human.html
Semantic Gateway: http://knoesis.org/library/resource.php?id=2154
SSN Ontology: http://knoesis.org/library/resource.php?id=1659
Applications of Multimodal Physical (IoT), Cyber and Social Data for Reliable and Actionable Insights: http://knoesis.org/library/resource.php?id=2018
Smart Data: Transforming Big Data into Smart Data...: http://wiki.knoesis.org/index.php/Smart_Data
Historic use of the term Smart Data (2004): http://www.scribd.com/doc/186588820
Transforming Big Data into Smart Data for Smart Energy: Deriving Value via ha...Amit Sheth
Keynote at the Workshop on Building Research Collaboration: Electricity Systems. Purdue University, West Lafayette, IN. Aug 28-29, 2013.
Abstract:
Big Data has captured much interest in research and industry, with anticipation of better decisions, efficient organizations, and many new jobs. Much of the emphasis is on technology that handles volume, including storage and computational techniques to support analysis (Hadoop, NoSQL, MapReduce, etc), and the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity. However, the most important feature of data, the raison d'etre, is neither volume, variety, velocity, nor veracity -- but value. In this talk, I will emphasize the significance of Smart Data, and discuss how it is can be realized by extracting value from Big Data. Accomplishing this task requires organized ways to harness and overcome the original four V-challenges; and while the technologies currently touted may provide some necessary infrastructure-- they are far from sufficient. In particular, we will need to utilize metadata, employ semantics and intelligent processing, and leverage some of the extensive work that predates Big Data.
For achieving energy sustainability, Smart Grids are known to transform the way we generate, distribute, and consume power. Unprecedented amount of data is being collected from smart meters, smart devices, and sensors all throughout the power grid. I will discuss the central question of deriving Value from the entire smart grid data deluge by discussing novel algorithms and techniques such as Semantic Perception for dealing with Velocity, use of ontologies and vocabularies for dealing with Variety, and Continuous Semantics for dealing with Velocity. I will discuss scenarios that exemplify the process of deriving Value from Big Data in the context of Smart Grid.
Additional background is at: http://wiki.knoesis.org/index.php/Smart_Data
A previous version of this talk with more technical details but not focused on energy: http://j.mp/SmatData
Physical Cyber Social Computing: An early 21st century approach to Computing ...Amit Sheth
Keynote given at WiMS 2013 Conference, June 12-14 2013, Madrid, Spain. http://aida.ii.uam.es/wims13/keynotes.php
Video of this talk at: http://videolectures.net/wims2013_sheth_physical_cyber_social_computing/
More information at: More at: http://wiki.knoesis.org/index.php/PCS
and http://knoesis.org/projects/ssw/
Replacing earlier versions: http://www.slideshare.net/apsheth/physical-cyber-social-computing & http://www.slideshare.net/apsheth/semantics-empowered-physicalcybersocial-systems-for-earthcube
Abstract: The proper role of technology to improve human experience has been discussed by visionaries and scientists from the early days of computing and electronic communication. Technology now plays an increasingly important role in facilitating and improving personal and social activities and engagements, decision making, interaction with physical and social worlds, generating insights, and just about anything that an intelligent human seeks to do. I have used the term Computing for Human Experience (CHE) [1] to capture this essential role of technology in a human centric vision. CHE emphasizes the unobtrusive, supportive and assistive role of technology in improving human experience, so that technology “takes into account the human world and allows computers themselves to disappear in the background” (Mark Weiser [2]).
In this talk, I will portray physical-cyber-social (PCS) computing that takes ideas from, and goes significantly beyond, the current progress in cyber-physical systems, socio-technical systems and cyber-social systems to support CHE [3]. I will exemplify future PCS application scenarios in healthcare and traffic management that are supported by (a) a deeper and richer semantic interdependence and interplay between sensors and devices at physical layers, (b) rich technology mediated social interactions, and (c) the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning in order to bridge machine and human perceptions. I will share an example of PCS computing using semantic perception [4], which converts low-level, heterogeneous, multimodal and contextually relevant data into high-level abstractions that can provide insights and assist humans in making complex decisions. The key proposition is to explain that PCS computing will need to move away from traditional data processing to multi-tier computation along data-information-knowledge-wisdom dimension that supports reasoning to convert data into abstractions that humans are adept at using.
[1] A. Sheth, Computing for Human Experience
[2] M. Weiser, The Computer for 21st Century
[3] A. Sheth, Semantics empowered Cyber-Physical-Social Systems
[4] C. Henson, A. Sheth, K. Thirunarayan, Semantic Perception: Converting Sensory Observations to Abstractions
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Smart Data and real-world semantic web applications (2004)Amit Sheth
Probably the first recorded use of "smart data" for achieving the Semantic Web and for realizing productivity, efficiency, and effectiveness gains by using semantics to transform raw data into Smart Data.
2013 retake on this is discussed at: http://wiki.knoesis.org/index.php/Smart_Data
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Presented at SW2012 @ ISWC2012.
http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html
This is an old version of this talk, for more recent information on this topic (eg talks, papers, events), see: http://wiki.knoesis.org/index.php/PCS
Presented at the Panel on
Sensor, Data, Analytics and Integration in Advanced Manufacturing, at the Connected Manufacturing track of Bosch-USA organized "Leveraging Public-Private Partnerships for Regional Growth Summit". Panel statement: Sensors, data and analytics are the core of any smart manufacturing system. What are the main challenges to create actionable outputs, replicate systems and scale efficiency gains across industries?
Moderator: Thomas Stiedl, Bosch
Panelists:
1. Amit Sheth, Wright State University
2. Howie Choset, Carnegie Melon University
3. Nagi Gebraeel, Georgia Institute of Technology
4. Brian Anthony, Massachusetts Institute of Technology
5. Yarom Polosky, Oak Ridget National Laboratory
For in-depth look:
Smart IoT: IoT as a human agent, human extension, and human complement
http://amitsheth.blogspot.com/2015/03/smart-iot-iot-as-human-agent-human.html
Semantic Gateway: http://knoesis.org/library/resource.php?id=2154
SSN Ontology: http://knoesis.org/library/resource.php?id=1659
Applications of Multimodal Physical (IoT), Cyber and Social Data for Reliable and Actionable Insights: http://knoesis.org/library/resource.php?id=2018
Smart Data: Transforming Big Data into Smart Data...: http://wiki.knoesis.org/index.php/Smart_Data
Historic use of the term Smart Data (2004): http://www.scribd.com/doc/186588820
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, ...Amit Sheth
Keynote given at ICDE2014, April 2014. Details at: http://ieee-icde2014.eecs.northwestern.edu/keynotes.html
A video of a version of this talk is available here: http://youtu.be/8RhpFlfpJ-A
(download to see many hidden slides).
Two versions of this talk, targeted at Smart Energy and Personalized Digital Health domains/apps at: http://wiki.knoesis.org/index.php/Smart_Data
Previous (older) version replaced by this version: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
invited talk at iPHEM16, Innovation in Pre-hospital Emergency Medicine, Kent Surrey and Sussex Air Ambulance Trust, July 2016, Brighton, United Kingdom
Large scale data analytics for smart cities and related use casesPayamBarnaghi
Invited talk, Large scale data analytics for smart cities and related use cases, The 5th EU-Japan Symposium on ICT Research and Innovation, October 2014, European Commission, Brussels, Belgium.
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Semantic, Cognitive, and Perceptual Computing – three intertwined strands of ...Amit Sheth
Keynote at Web Intelligence 2017: http://webintelligence2017.com/program/keynotes/
Video: https://youtu.be/EIbhcqakgvA Paper: http://knoesis.org/node/2698
Abstract: While Bill Gates, Stephen Hawking, Elon Musk, Peter Thiel, and others engage in OpenAI discussions of whether or not AI, robots, and machines will replace humans, proponents of human-centric computing continue to extend work in which humans and machine partner in contextualized and personalized processing of multimodal data to derive actionable information.
In this talk, we discuss how maturing towards the emerging paradigms of semantic computing (SC), cognitive computing (CC), and perceptual computing (PC) provides a continuum through which to exploit the ever-increasing and growing diversity of data that could enhance people’s daily lives. SC and CC sift through raw data to personalize it according to context and individual users, creating abstractions that move the data closer to what humans can readily understand and apply in decision-making. PC, which interacts with the surrounding environment to collect data that is relevant and useful in understanding the outside world, is characterized by interpretative and exploratory activities that are supported by the use of prior/background knowledge. Using the examples of personalized digital health and a smart city, we will demonstrate how the trio of these computing paradigms form complementary capabilities that will enable the development of the next generation of intelligent systems. For background: http://bit.ly/PCSComputing
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
Knowledge Will Propel Machine Understanding of Big DataAmit Sheth
Preview video: https://youtu.be/4e0dtV7CTWM
CCKS Keynote, August 2017: http://www.ccks2017.com/?page_id=358
SEAS Summer School, July 2017
https://sites.google.com/view/seasschool2017/talks
Related paper: http://knoesis.org/node/2835
CCKS Conf had over 500 attendees- some photos: https://photos.app.goo.gl/5CdlfAX1uYwvgqsQ2
GIM encompasses the management, leadership, structures and practices required for the successful operation of GIS within an entity, nationally, regionally or globally.
Computer Science is an ever-changing field with new inventions each day. Here are the latest trends in the field of computer science which are making their mark in this era of digitization.
Source: http://www.techsparks.co.in
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
What is data-driven government for public safety?IBM Analytics
How can governments become data-driven and capitalize on the ton of valuable insight hidden in the flood of data we generate every day? Where has this already been implemented, and what are the effects? Get the big picture on public safety and incident and emergency management at http://ibm.co/saferplanet
Introduction: Technological and methodical pillars for Smarter Environment Enablement
Part I: Smarter Environments Theoretical Grounding
What is a Smart Environment?
Technological enablers: IoT, Web of Data and Persuasive Technologies
Technology mediated Human Collaboration: need for co-creation
Killer application domains: Open Government & Age-friendly cities
Part II: Review of core enablers for Smarter Environments
Co-creation methodologies: Design for Thinking
Internet of Things and Web of Things
Web of Data: Linked Data, Crowdsourcing & Big Data
Part III: WeLive Case Study
WeLive as Open Government enabling methodology and platform
Reflections on the need for collaboration among stakeholders to realize Smarter Cities
Conclusions and practical implications
Rob Kitchin Smart Cities 08th March 2016 (Smart Dublin)Mainard Gallagher
Rob Kitchin is a Professor and ERC Advanced Investigator in the National Institute of Regional and Spatial Analysis at Maynooth University, for which he was director between 2002 and 2013. He is one of Ireland's leading social scientists and was the 2013 recipient of the Royal Irish Academy's Gold Medal for the Social Sciences and received the Association of American Geographers ‘Meridian Book Award’ for the outstanding book in the discipline in 2011.
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, ...Amit Sheth
Keynote given at ICDE2014, April 2014. Details at: http://ieee-icde2014.eecs.northwestern.edu/keynotes.html
A video of a version of this talk is available here: http://youtu.be/8RhpFlfpJ-A
(download to see many hidden slides).
Two versions of this talk, targeted at Smart Energy and Personalized Digital Health domains/apps at: http://wiki.knoesis.org/index.php/Smart_Data
Previous (older) version replaced by this version: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
invited talk at iPHEM16, Innovation in Pre-hospital Emergency Medicine, Kent Surrey and Sussex Air Ambulance Trust, July 2016, Brighton, United Kingdom
Large scale data analytics for smart cities and related use casesPayamBarnaghi
Invited talk, Large scale data analytics for smart cities and related use cases, The 5th EU-Japan Symposium on ICT Research and Innovation, October 2014, European Commission, Brussels, Belgium.
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Semantic, Cognitive, and Perceptual Computing – three intertwined strands of ...Amit Sheth
Keynote at Web Intelligence 2017: http://webintelligence2017.com/program/keynotes/
Video: https://youtu.be/EIbhcqakgvA Paper: http://knoesis.org/node/2698
Abstract: While Bill Gates, Stephen Hawking, Elon Musk, Peter Thiel, and others engage in OpenAI discussions of whether or not AI, robots, and machines will replace humans, proponents of human-centric computing continue to extend work in which humans and machine partner in contextualized and personalized processing of multimodal data to derive actionable information.
In this talk, we discuss how maturing towards the emerging paradigms of semantic computing (SC), cognitive computing (CC), and perceptual computing (PC) provides a continuum through which to exploit the ever-increasing and growing diversity of data that could enhance people’s daily lives. SC and CC sift through raw data to personalize it according to context and individual users, creating abstractions that move the data closer to what humans can readily understand and apply in decision-making. PC, which interacts with the surrounding environment to collect data that is relevant and useful in understanding the outside world, is characterized by interpretative and exploratory activities that are supported by the use of prior/background knowledge. Using the examples of personalized digital health and a smart city, we will demonstrate how the trio of these computing paradigms form complementary capabilities that will enable the development of the next generation of intelligent systems. For background: http://bit.ly/PCSComputing
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
Knowledge Will Propel Machine Understanding of Big DataAmit Sheth
Preview video: https://youtu.be/4e0dtV7CTWM
CCKS Keynote, August 2017: http://www.ccks2017.com/?page_id=358
SEAS Summer School, July 2017
https://sites.google.com/view/seasschool2017/talks
Related paper: http://knoesis.org/node/2835
CCKS Conf had over 500 attendees- some photos: https://photos.app.goo.gl/5CdlfAX1uYwvgqsQ2
GIM encompasses the management, leadership, structures and practices required for the successful operation of GIS within an entity, nationally, regionally or globally.
Computer Science is an ever-changing field with new inventions each day. Here are the latest trends in the field of computer science which are making their mark in this era of digitization.
Source: http://www.techsparks.co.in
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
What is data-driven government for public safety?IBM Analytics
How can governments become data-driven and capitalize on the ton of valuable insight hidden in the flood of data we generate every day? Where has this already been implemented, and what are the effects? Get the big picture on public safety and incident and emergency management at http://ibm.co/saferplanet
Introduction: Technological and methodical pillars for Smarter Environment Enablement
Part I: Smarter Environments Theoretical Grounding
What is a Smart Environment?
Technological enablers: IoT, Web of Data and Persuasive Technologies
Technology mediated Human Collaboration: need for co-creation
Killer application domains: Open Government & Age-friendly cities
Part II: Review of core enablers for Smarter Environments
Co-creation methodologies: Design for Thinking
Internet of Things and Web of Things
Web of Data: Linked Data, Crowdsourcing & Big Data
Part III: WeLive Case Study
WeLive as Open Government enabling methodology and platform
Reflections on the need for collaboration among stakeholders to realize Smarter Cities
Conclusions and practical implications
Rob Kitchin Smart Cities 08th March 2016 (Smart Dublin)Mainard Gallagher
Rob Kitchin is a Professor and ERC Advanced Investigator in the National Institute of Regional and Spatial Analysis at Maynooth University, for which he was director between 2002 and 2013. He is one of Ireland's leading social scientists and was the 2013 recipient of the Royal Irish Academy's Gold Medal for the Social Sciences and received the Association of American Geographers ‘Meridian Book Award’ for the outstanding book in the discipline in 2011.
I developed this presentation as a member of the Union Square Redevelopment Civic Advisory Committee (CAC) and its Transportation and Infrastructure subcommittee. The presentation was made to fellow CAC members, members of the public, Somerville City Government staff, US2 (the Master Developer) staff, and other group representatives including Union Square Main Streets, Union Square Neighbors, and the Union United Coalition on 7-14-15. The purpose of the talk is to present underlying concepts, benefits, and options related to smart city infrastructure in the context of Union Square Somerville. My intent was to spark discussion and further consideration including the idea of making Union Square an urban innovation lab (to attract employers, improve civic life, and support public and private services and benefits) for the entire city and beyond.
Smart Cities Market: Advancing Towards a Connected and Resilient Futureajaykumarpmr
The concept of smart cities, leveraging technology to enhance urban living, is rapidly gaining traction worldwide. Smart cities integrate various digital technologies, data analytics, and connectivity solutions to improve infrastructure, services, and quality of life for residents. The global smart cities market is witnessing robust growth, driven by urbanization, sustainability initiatives, and the pursuit of efficient urban management. According to Persistence Market Research's projections, the smart cities market to expand at a significant CAGR of 10.3%, reaching an estimated value of US$ 1274.5 billion by 2033, up from US$ 525.8 billion in 2024.
Smart cities are driving economic competitiveness, environmental sustainability and livability. To make a city resourceful is to make it more efficient, more attractive, and more eco-friendly, all while making a real improvement to Citizens quality of life. While financing options are not evolving quite as fast as technology, they are evolving nonetheless. Lean how to fund and finance your smart city project.
Smart city simply means the use of information technology(IT) at the city level, which was first applied to the desk in 1980s and then expanded to the office or the home and the building in that order. Smart city enables citizen to make the right decision and act like an expert by moving intelligence from human to city structure. Smart city has four characteristics; self-orarnizing city, generative city, citizen-centric city, and realtime city. In order to succeed in building smart city, emphasis should be put on the city platform. Without a city-wide platform, it is impossible to combine data from different sources and to create smart services. This slide explains what is smart city, how to start smart city, and what benefits smart city will accompany.
Smart Cities vs. Civic Tech: an analysis (Annette Jezierska and German Dector...mysociety
This was presented by Réka Solymosi from University College London at the Impacts of Civic Technology Conference (TICTeC 2018) in Lisbon on 18th April 2018. You can find out more information about the conference here: http://tictec.mysociety.org/2018
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Semantics-empowered Smart City applications: today and tomorrow
1. Semantics-empowered Smart City
Applications: Today and Tomorrow
Keynote at
The 6th Workshop on Semantics for Smarter Cities (S4SC 2015), October 11-12, 2015
Prof. Amit Sheth
LexisNexis Ohio Eminent Scholar; Executive Director, Kno.e.sis
Wright State University
Special Thanks: Pramod Anantharam
http://www.ict-citypulse.eu/
2. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
3. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
4. 4
Source LAT Times, http://documents.latimes.com/la-2013/
Future Cities: A View from 1998
Thanks to Dr. Payam Barnaghi for sharing the slide
6. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
8. 8
Smart Cities: Significance and Impact
Image credit: https://commons.wikimedia.org/wiki/File:Narendra_Damodardas_Modi.jpg
9. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
10. 10
Smart Cities: A Historical Perspective
Economic development on trade routesCivilizations on river banks
Economic development now increasingly rely on digital infrastructure
10
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
11. 11
Smart City Applications: Proliferation of Digital Infrastructure
http://postscapes.com/internet-of-things-award/2014/smart-city-application.html
12. 12
Smart City Applications: Proliferation of Digital Infrastructure
http://postscapes.com/internet-of-things-award/2014/smart-city-application.html
13. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
14. 14
Industrial SIE, ERIC
SME AI,
Higher
Education
UNIS, NUIG,
UASO, WSU
City BR, AA
Partners:
Duration: 36 months
CityPulse: Large-scale Data Analytics for Smart Cities
21. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
22. 22
- Programmable devices
- Off-the-shelf gadgets/tools
Thanks to Dr. Payam Barnaghi for sharing the slide
Physical: Sensors Monitoring the Physical World
23. 23
Thanks to Dr. Payam Barnaghi for sharing the slide
Cyber: Observations Pushed to the Cyber World
24. 24
Motion sensor
Motion sensor
Motion sensor
ECG sensor
World Wide Web
Road block, A3
Road block, A3
Thanks to Dr. Payam Barnaghi for sharing the slide
Social: People Interacting with the Physical World
25. 25
http://wiki.knoesis.org/index.php/PCS
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
Physical
Cyber
Social*
Developers need to Consider observations from Physical-Cyber-Social
systems in building future Smart City applications
*http://www.ichangemycity.com/
Key to Develop Future Robust Smart City Applications
26. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
28. Future of Smart Cities
29
Public Safety Urban planning Gov. & agency
admin.
Energy &
water
Environmental Transportation Social Programs Healthcare Education
• Increased use of multimodal observations and knowledge for enhanced
explanation and prediction of city events to enable data driven policy
making
• Innovative smart city solutions for situations with low/no
instrumentation through seamless citizen participation
29. Increased use of multimodal observations and knowledge for enhanced
explanation and prediction of city events to enable data driven policy making
Multimodal observations and knowledge
Enhanced explanation and prediction
Data driven policy decisions
Public Safety Urban planning
Gov. & agency
admin.
Energy &
water
Environmental Transportation
Social Programs Healthcare
Education
https://www.oracle.com/applications/enterprise-resource-planning/roles/chief-financial-officer.html
32. • Why?
– Explain/Interpret average speed and link travel time data using event
schedule provided by city authorities and real-time traffic events
shared on Twitter
– Past work: Predict congestion using single modality such as sensor
data
• What?
– Combine
• 511.org data about Bay Area Road Network Traffic
– E.g., Average speed and link travel time data stream
– E.g., (Happened or planned) event reports
• Tweets that report events including ad hoc ones
33
Integrating Multimodal Observations: Transportation Domain
Thanks to Dr. Krishnaprasad Thirunarayan for sharing the slide
33. • How?
– Extract events from textual tweets stream
– Build statistical models of normalcy, and thereby anomaly, from
numerical sensor data streams
– Correlate multimodal streams, using spatio-temporal information, to
annotate “anomalies” in sensor data time series with textual events
34
Integrating Multimodal Observations: Transportation Domain
Thanks to Dr. Krishnaprasad Thirunarayan for sharing the slide
34. • How?
– Extract events from textual tweets stream
– Build statistical models of normalcy, and thereby anomaly, from
numerical sensor data streams
– Correlate multimodal streams, using spatio-temporal information, to
annotate “anomalies” in sensor data time series with textual events
35
Integrating Multimodal Observations: Transportation Domain
35. • Are people talking about city traffic events on
twitter?
• Can we extract city traffic related events from
twitter?
• How can we leverage event and location knowledge
bases for event extraction?
• How well can we extract city events?
Research Questions
36
36. 37
Twitter as a Source of City Events
Public Safety
Urban planning
Gov. & agency
admin.
Energy & water
Environmental
TransportationSocial Programs
Healthcare
Education
37. 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
38
38. 39
Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events from Social Streams.
ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317 http://doi.acm.org/10.1145/2717317
Extracting City Events from Textual Data
39. • 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
First Evaluation
40
40. 41
Distribution of Extracted Events Over Locations
• Evaluation Metric For Comparing Events with Ground Truth
– Complementary Events
• Additional information e.g., slow traffic from sensor data and
accident from textual data
– Corroborative Events
• Additional confidence e.g., accident event supporting a accident
report from ground truth
– Timeliness
• Early detection e.g., knowing poor visibility before its formal
report
44. • How?
– Extract events from textual tweets stream
– Build statistical models of normalcy, and thereby anomaly, from
numerical sensor data streams
– Correlate multimodal streams, using spatio-temporal information, to
annotate “anomalies” in sensor data time series with textual events
45
Traffic Domain Use-case (Open Data)
46. • Causes of non-linearity in sensor data streams
– Temporal landmarks : peak hour vs off-peak traffic vs
weekend traffic
– Effect of location
– Scheduled events such as road construction, baseball
game, or music concert
– Unexpected events such as accidents or heavy rains
– Random variations (viz., stochasticity)
47
Traffic Dependencies
Thanks to Dr. Krishnaprasad Thirunarayan for sharing the slide
47. • Disclaimer
"All models are wrong, but some are useful.” - George Box
• Normalcy Models
– Gaussian Mixture Model (GMM)
• Captures multiple co-existing events and its impact on traffic
• Doesn’t capture temporal dependencies
– Auto Regressive (AR) Models
• Captures temporal dependencies in traffic dynamics
• Doesn’t capture hidden aspects of the domain (e.g., volume of traffic)
– Linear Dynamical System (LDS)
• Captures temporal dependencies and hidden aspects of a domain
• Anomaly Model
– Cf. Box and Whisker plots
48
Abstracting Traffic Behavior: Traffic Data Model
Thanks to Dr. Krishnaprasad Thirunarayan for sharing the slide
49. Histogram of speed values
collected from June 1st 12:00 AM to June 2nd 12:00 AM
Histogram of travel time values
collected from June 1st 12:00 AM to June 2nd 12:00 AM
50
Traffic Data: First Peek
50. Most of the drivers tend to
go 5 km/h over the posted speed limit
There are relatively few drivers who
go more than 10 km/h over the
posted speed limit
There are situations in a day where the
drivers are going (forced) below the
speed limit e.g., rush hour traffic
Do these histograms resemble any probability distribution?
51
Traffic Data: Possible Explanation
52. Assume Normalcy to be uninterrupted traffic flow
July 2014 has no events so, we
hypothesize higher log-likelihood
score
June 2014 has many events so, we
hypothesize lower log-likelihood
score
-115655.8
(Closer to Normalcy)
-125974.3
53
Golden Gate Fields: Comparing Months with Varying Event Occurrences
56. • How?
– Extract events from textual tweets stream
– Build statistical models of normalcy, and thereby anomaly, from
numerical sensor data streams
– Correlate multimodal streams, using spatio-temporal information, to
annotate “anomalies” in sensor data time series with textual events
57
Traffic Domain Use-case (Open Data)
57. • If an anomaly is detected on a link L and during time
period [tst, tet], then the anomaly is explained by an event
if the event occurred in the vicinity within 0.5km radius
and during [tst-1, tet+1].
• CAVEAT: An anomaly may not be explained because of
missing data.
58
Spatio-temporal Co-occurrence Criteria
Thanks to Dr. Krishnaprasad Thirunarayan for sharing the slide
58. • Data collected from San Francisco Bay Area between May 2014 to May
2015
– 511.org:
• 1,638 traffic incident reports
• 1.4 billion speed and travel time observations
– Twitter Data: 39,208 traffic related incidents extracted from over 20 million
tweets1
• Naïve implementation for learning normalcy models for 2,534 links
resulted in 40 minutes per link (~ 2 months of processing time for our
data)
– 2.66 GHz, Intel Core 2 Duo with 8 GB main memory
• Scalable implementation by exploiting the nature of the problem resulted
in learning normalcy models within 24 hours
– The Apache Spark cluster used in our evaluation has 864 cores and 17TB main
memory.
59
1Anantharam, P. 2014. Extracting city traffic events from social streams. https://osf.io/b4q2t/wiki/home/
Experimental Data Statistics and Infrastructure
59. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
60. Innovative smart city solutions for situations with low/no instrumentation through
enhanced citizen participation
Coordination during Disasters
61. • May lead to second disaster to be managed:
– Under-supply of required demands
– Over-supply of not required resources
• Hurricane Sandy example,
“Thanks, but no thanks”,
NPR, Jan 12 2013
Story link:
http://www.npr.org/2013/01/09/168946170/thanks-
but-no-thanks-when-post-disaster-donations-
overwhelm
Uncoordinated Engagement
62. 63
Image: http://www.gizmodo.com.au/2012/04/how-we-identify-single-
voices-in-a-crowd/
BIG QUESTION: Can these needles be identified in the
haystack of massive datasets?
Me and @CeceVancePR are
coordinating a clothing/food drive for
families affected by Hurricane Sandy. If
you would like to donate, DM us
Does anyone know how to donate
clothes to hurricane #Sandy victims?
[REQUEST/DEMAND]
[OFFER/SUPPLY]
Coordination teams
want to hear!
[BIG] Ad-hoc Community with Varying but [FEW] Important Intents
63. Really sparse Signal to Noise:
• 2M tweets during the first 48 hrs. of #Oklahoma-tornado-2013
- 1.3% as the precise resource donation requests to help
- 0.02% as the precise resource donation offers to help
64
• Anyone know how to get involved to help the
tornado victims in Oklahoma??#tornado
#oklahomacity (OFFER)
• I want to donate to the Oklahoma cause
shoes clothes even food if I can (OFFER)
Disaster Response Coordination:
Finding Actionable Nuggets for Responders to act
• Text REDCROSS to 909-99 to donate to
those impacted by the Moore tornado!
http://t.co/oQMljkicPs (REQUEST)
• Please donate to Oklahoma disaster
relief efforts.: http://t.co/crRvLAaHtk
(REQUEST)
For responders, most important information is the scarcity and
availability of resources
Blog by our colleague Patrick Meier on this analysis: http://irevolution.net/2013/05/29/analyzing-tweets-tornado/
64. Want to help animals in
#Oklahoma? @ASPCA
tells how you can help:
http://t.co/mt8l9PwzmO
x
RESPONSE TEAMS
(including humanitarian
org. and ‘pseudo’
responders)
VICTIM SITE
Where do I go
to help out for
volunteer work
around
Moore?
Anyone know?
Anyone know
where to donate to
help the animals
from the Oklahoma
disaster? #oklaho
ma #dogs
Matchable
Matchable
If you would like
to volunteer
today, help is
desperately
needed in
Shawnee. Call
273-5331 for
more info
65
CITIZEN SENSORS
DEMAND SUPPLY
Match-making: Assisting Coordination
Image: http://offthewallsocial.com/tag/social-media/
65. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
• Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
67. Proposed Ecosystem
- Kno.e.sis Center
- Manav Sadhna
- eMoksha
- Government
My son was on
Cloraquine for 2 days and
is not showing any
improvements on malaria
symptoms.
Our resources will not last
long if the malaria cases
increase in a few days. We
are in need of medications
and volunteers.
Small water pools
around the
neighborhood are
creating mosquito
problems.
User 1
User 2
User 3
Chloraquine is not a
suggested solution for
malaria in India. Please
see a provider ASAP. The
closest healthcare facility
is on street X.
Received several
comments
from this area regarding
malaria symptoms.
Please send your
volunteer to check.
Query from User 1: classified
as an “active care” type query.
Response needs to be sent to
the user. Further analysis
showed similar queries from
same region.
Query from User 2
and 3: classified as a
“preventive care”
query. The message
needs to be sent to
an NGO.
Water pool is
breeding site for
Anopheles
mosquitos, so
preventive
measures need
to be taken.
SMS, e-mails,
tweets, Web
People from various
locations
Ontology/Knowledg
e Base
68. • Smart Cities as envisioned in the past and current state
• Smart City initiatives from governments
• Significance of digital infrastructure for Smart Cities
– Variety of smart city projects creating the digital infrastructure
• CityPulse: Large-scale data analytics for smart cities
• Key to Develop Future Robust Smart City Applications
• Future of smart cities
– P1: Increased use of multimodal observations and knowledge for enhanced explanation and
prediction of city events to enable data driven policy making
– P2: Innovative smart city solutions for situations with low/no instrumentation through
seamless citizen participation
• ComSmart Cities: opportunities and challenges
– P1: Integrating Multimodal Observations: Transportation Domain
– Combining textual traffic events with traffic sensor data
– P2: Smart city solutions for situations with low/no instrumentation
• Coordination during Disasters
• Smart Health for Situations with Low/No Instrumentation
• Smart Cities: opportunities and challenges
Outline
69. • Utilizing multimodal and heterogeneous observations for
enhanced understanding and prediction of city events
• Create better governance of our cities and better public
services through data driven policy making
• Empower citizens for active participation in shaping the
development of a city
• Provide more business opportunities for companies (and
SMEs) and private sector services
• Improve energy efficiency, create greener environments…
• Create better healthcare, elderly-care…
Thanks to Dr. Payam Barnaghi for sharing the slide
70
Smart Cities: Opportunities
70. • Dealing with massive heterogeneity in observations from a
city spanning physical, cyber, and social domains
• Dealing with missing, sparse, and noisy observations from
machine sensors and people
• Seamless integration of citizens in shaping city policies
(reliability and quality of citizen reporting of city events)
• Reliability and dependability of the massive infrastructure of
connected devices, services, and people
• Transparency and data management issues (privacy, security,
trust, …)
71
Smart Cities: Challenges
71. Thank You
http://knoesis.org/amit, http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/
@amit_p, @pbarnaghi
amit@knoesis.org, p.barnaghi@surrey.ac.uk
Acknowledgement: CityPulse Consortium
http://www.ict-citypulse.eu
Annual Report:
http://www.ict-citypulse.eu/page/sites/default/files/citypulse_annual_report.pdf
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA