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
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
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
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.
"Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web" Keynote at On the Move Federated Conferences, Crete, Greece, October 18, 2011.
http://www.onthemove-conferences.org/
Details: http://wiki.knoesis.org/index.php/Computi
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
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.
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
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
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
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.
"Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web" Keynote at On the Move Federated Conferences, Crete, Greece, October 18, 2011.
http://www.onthemove-conferences.org/
Details: http://wiki.knoesis.org/index.php/Computi
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
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.
Semantics-empowered Smart City applications: today and tomorrowAmit Sheth
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.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
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
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
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
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
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
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
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
GIM encompasses the management, leadership, structures and practices required for the successful operation of GIS within an entity, nationally, regionally or globally.
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
Data Science For Social Good: Tackling the Challenge of HomelessnessAnita Luthra
A talk presented at the Champions Leadership Conference Series - leveraging data provided by New York City’s Department of Homeless Services, software vendor Tibco partnered with SumAll.Org to help tackle the societal challenge of homelessness in New York City.
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Open Data Analytical Model for Human Development Index to Support Government ...Andry Alamsyah
The transparency nature of Open Data is beneficial for citizens to evaluate government work performance. In Indonesia, each government bodies or ministry have their own standard operation procedure on data treatment resulting in incoherent information between agent and likely to miss valuable insight. Therefore, our motivation is to show the advantage of Open Data movement to support unified government decision making. We use dataset from data.go.id which publish official data from each government bodies. The idea is by using those official but limited data, we can find important pattern. The case study is on Human Development Index value prediction and its clustered nature. We explore the data pattern using two important data analytics methods classification and clustering procedure. Data analytics is the collection of activities to reveal unknown data pattern. Specifically, we use Artificial Neural Network classification and K-means clustering. The classification objective is to categorize different level of Human Development Index of cities or region in Indonesia based on Gross Domestic Product, Number of Population in Poverty, Number of Internet User, Number of Labors and Number of Population indicators data. We determined which city belongs to four categories of Human Development stated by UNDP standard. The clustering objective is to find the group characteristics between Human Development Index and Gross Domestic Product.
The Internet of Things, or the IoT is a vision for a ubiquitous society wherein people and “Things” are connected in an immersively networked computing environment, with the connected “Things” providing utility to people/enterprises and their digital shadows, through intelligent social and commercial services. However, translating this idea to a conceivable reality is a work in progress for close to two decades; mostly, due to assumptions favoured more towards a “Things”-centric rather than a “Human”-centric approach coupled with the evolution/deployment ecosystem of IoT technologies.
Estimates on the spread and economic impact of IoT over the next few years are in the neighborhood of 50 billion or more connected “Things” with a market exceeding $350 billion through smarter cities and infrastructure, intelligent appliances, and healthier lifestyles. While many of these potential benefits from IoT are real and achievable, the road to accomplish these may need an rethink.
In the last few years, there has been a realization that an effective architecture for IoT (particularly, for emerging nations with limited technology penetration at the national scale) that is both affordable and sustainable should be based on tangible technology advances in the present, ubiquitous capabilities of the present/future, and practical application scenarios of social and entrepreneurial value. Hence, there is a revitalized interest to rethink the above assumptions, and this exercise has led to a more plausible set of scenarios wherein humans along with data, communication and devices play key roles.
In this presentation, an attempt is made to disaggregate these core problems; and offer a trajectory with a set of design paradigms for a renewed IoT ecosystem.
Semantics-empowered Smart City applications: today and tomorrowAmit Sheth
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.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
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
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
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
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
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
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
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
GIM encompasses the management, leadership, structures and practices required for the successful operation of GIS within an entity, nationally, regionally or globally.
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
Data Science For Social Good: Tackling the Challenge of HomelessnessAnita Luthra
A talk presented at the Champions Leadership Conference Series - leveraging data provided by New York City’s Department of Homeless Services, software vendor Tibco partnered with SumAll.Org to help tackle the societal challenge of homelessness in New York City.
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Open Data Analytical Model for Human Development Index to Support Government ...Andry Alamsyah
The transparency nature of Open Data is beneficial for citizens to evaluate government work performance. In Indonesia, each government bodies or ministry have their own standard operation procedure on data treatment resulting in incoherent information between agent and likely to miss valuable insight. Therefore, our motivation is to show the advantage of Open Data movement to support unified government decision making. We use dataset from data.go.id which publish official data from each government bodies. The idea is by using those official but limited data, we can find important pattern. The case study is on Human Development Index value prediction and its clustered nature. We explore the data pattern using two important data analytics methods classification and clustering procedure. Data analytics is the collection of activities to reveal unknown data pattern. Specifically, we use Artificial Neural Network classification and K-means clustering. The classification objective is to categorize different level of Human Development Index of cities or region in Indonesia based on Gross Domestic Product, Number of Population in Poverty, Number of Internet User, Number of Labors and Number of Population indicators data. We determined which city belongs to four categories of Human Development stated by UNDP standard. The clustering objective is to find the group characteristics between Human Development Index and Gross Domestic Product.
The Internet of Things, or the IoT is a vision for a ubiquitous society wherein people and “Things” are connected in an immersively networked computing environment, with the connected “Things” providing utility to people/enterprises and their digital shadows, through intelligent social and commercial services. However, translating this idea to a conceivable reality is a work in progress for close to two decades; mostly, due to assumptions favoured more towards a “Things”-centric rather than a “Human”-centric approach coupled with the evolution/deployment ecosystem of IoT technologies.
Estimates on the spread and economic impact of IoT over the next few years are in the neighborhood of 50 billion or more connected “Things” with a market exceeding $350 billion through smarter cities and infrastructure, intelligent appliances, and healthier lifestyles. While many of these potential benefits from IoT are real and achievable, the road to accomplish these may need an rethink.
In the last few years, there has been a realization that an effective architecture for IoT (particularly, for emerging nations with limited technology penetration at the national scale) that is both affordable and sustainable should be based on tangible technology advances in the present, ubiquitous capabilities of the present/future, and practical application scenarios of social and entrepreneurial value. Hence, there is a revitalized interest to rethink the above assumptions, and this exercise has led to a more plausible set of scenarios wherein humans along with data, communication and devices play key roles.
In this presentation, an attempt is made to disaggregate these core problems; and offer a trajectory with a set of design paradigms for a renewed IoT ecosystem.
Slideshare lost the previous upload which had nearly 70K views. Re-uploading. http://knoesis.org/?q=node/2633
With the explosion in social media (1B+ Facebook users, 500M+ Twitter users) and ubiquitous mobile access (6B+ mobile phone subscribers) sharing their observations and opinions, we have unprecedented opportunities to extract social signals, create spatio-temporal mappings, perform analytics on social data, and support applications that vary from situational awareness during crisis response, preparedness and rebuilding phases to advanced analytics on social data, and gaining valuable insights to support improved decision making.This tutorial weaves three themes and corresponding relevant topics- a.) citizen sensing and crisis mapping, b.) technical challenges and recent research for leveraging citizen sensing to improve crisis response coordination, and c.) experiences in building robust and scalable platforms/systems. It will couple technical insights with identification of computational techniques and algorithms along with real-world examples. We will also do exemplary demos of the features in the Sahana, CrowdMap (Ushahidi's version) and Twitris platforms while elaborating on the practical issues and pitfalls of the development and operation of these large-scale platforms, especially during the real-time crisis response
Computing for Human Experience: Sensors, Perception, Semantics, Social Comput...Amit Sheth
Keynote at the 3rd Asian Semantic Web Conference (ASWC2008), Bangkok, Thailand, Feb 2-5, 2009. http://aswc2008.ait.ac.th/invitedspeaker2.html
More details: http://wiki.knoesis.org/index.php/Computing_For_Human_Experience
Big Data for the Social Sciences - David De Roure - Jisc Digital Festival 2014Jisc
The analysis of government data, data held by business, the web, social science survey data will support new research directions and findings. Big Data is one of David Willetts’ 8 great technologies, and in order to secure the UK’s competitive advantage new investments have been made by the Economic Social Science Research Council ( ESRC) in Big Data, for example the Business Datasafe and Understanding Populations investments. In this session the benefits of the use of Big Data in social science , and the ESRCs Big Data strategy will be explained by Professor David De Roure.of the Oxford e-Research Centre and advisor to the ESRC.
Disasters Happen. We need to manage them to minimize the loss to life and property. Disaster management has been received much attention, but has not been touched much by the latest technology. This paper presents an approach to manage disasters using latest and popular technology. We are interested in building a community of researchers who are interested in developing such tools.
The presentatio offers an overview on big data in/for global development - i.e. how big data & data science are being developed in emerging and developing regions.
It is divided in three main sections:
(1) what is big data (as of today) & what is big data in/for development?
(2) Who is actually doing «big data for development»? Who are the main intrnational actors/stakeholders? What are main experiences?
(3) Why are we doing this? - i.e. are we doing this right? What are the main access, capacity / interpretation / ethical issues?
Engines of Order. Social Media and the Rise of Algorithmic Knowing.Bernhard Rieder
Talk given at the Social Media and the Transformation of Public Space Conference on June 19 at the University of Amsterdam. References and comments are in the notes section.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
Big data-analytics-changing-way-organizations-conducting-businessAmit Bhargava
Hi Friends ,
There is an interesting post on how to leveraging Big data analytics in an Integrated GRC Environment in an Organize to have visibility in core enterprises issues on real time basis . This presentation is from Metric stream -an international and Global GRC soloutioning providers in association with Dr. Kirk. D. Borne - Big data consultant and Adviser .Hope you like it and enjoy as well.
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Similar to Building Social Life Networks 130818 (20)
Self Health 231006 presented at HKPoly.pptxRamesh Jain
As technology stands poised to transform our lives, Self Health emerges as a vital innovation for the future, especially in key areas like chronic diseases, mental, and geriatric health care. Utilizing natural language processing and empathy, it provides trusted, perpetual health information and guidance tailored to each individual. Amidst a backdrop of modern disinformation, this conversational approach becomes a reliable source, considering genetic, lifestyle, and psychological factors. It revolutionizes chronic and geriatric disease management while enhancing mental well-being. By empowering individuals to take proactive health measures, Self Health not only elevates personal lives but also contributes to global health improvements. It signifies a future where healthcare is personalized, trusted, empathetic, and universally impactful.
Homeostasis is nature’s engineering behind the most complex autonomic system that exists: the human body. Homeostasis is a self-regulating process by which biological systems tend to maintain stability while adjusting to conditions that are optimal for survival. Disruption in homeostasis results in malfunctioning of natural autonomic system causing chronic diseases. Chronic diseases have been the leading cause of death and human suffering in the last 50 years. They also have resulted in highest financial burden for individuals and countries. This can be corrected using external augmentation of the homeostasis loop. Recent progress in artificial pancreas for Type 1 Diabetes is a compelling example for such augmentation. In this presentation we discuss emerging multimodal approaches for such augmentation in the context of chronic diseases. We show that multimodal sensing and fundamental technology developed for multimedia computing may offer powerful augmentation of natural homeostasis to assist in management of chronic diseases.
Food is the most important component of the planet, human society, and every individual. However, our current thinking about food is filled with disinformation and siloed thinking. Can we use technology to unify the silos and counter disinformation?
Homeostasis is nature’s engineering behind the most complex autonomic system that exists: the human body. Homeostasis is a self-regulating process by which biological systems tend to maintain stability while adjusting to conditions that are optimal for survival. Disruption in homeostasis results in malfunctioning of natural autonomic system causing chronic diseases. Chronic diseases have been the leading cause of death and human suffering in the last 50 years. They also have resulted in highest financial burden for individuals and countries. This can be corrected using external augmentation of the homeostasis loop. Recent progress in artificial pancreas for Type 1 Diabetes is a compelling example for such augmentation. In this paper we discuss emerging multimodal approaches for such augmentation in the context of chronical diseases. We show that multimodal sensing and fundamental technology developed by multimedia computing community may offer powerful augmentation of natural homeostasis to assist in management of chronic diseases.
Food is the most important element in determining quality of life. It is a source of enjoyment. It is also source of energy and nourishment to keep your body healthy. Bit, there is a serious tension: What I like to eat is not necessarily what my body wants me to eat. To enjoy food one must know individual tastes and effects of food on individual body. One should also know attributes of food related to taste and nutrition. In this talk this issue is addresses and an approach to recommend enjoyable healthy food is proposed.
What if an app could guide you to better health, similar to how GPS navigation directs you to your desired destination? What if the app could use real-time information to redirect you around a disease, just as you’re rerouted to avoid traffic? What if the app could provide step-by-step directions to get you to your optimal health state, whether you’re a professional athlete or retired school teacher? We discuss how this navigational approach to healthcare could become a reality by combining emerging technology with well-established cybernetic principles.
Rj imminent transformations in health shanghai 170510Ramesh Jain
Fundamental nature of health is changing. Current healthcare is legacy of caring infectious diseases, while chronic diseases are now the most prevalent in most societies. Health should be considered as a metanexus of genetics, lifestyle, environment, socio-economic situation and medical knowledge
Talk at Wearable 2016 Symposium in Lausanne.
This presentation talks about use of wearables and other sensors for quantifying lifestyle and relating it to build model of personal health.
Micro reports and Situation Recognition at social machines workshopRamesh Jain
Micro-reports are the next generation after micro-blogs, such as Twitter. Micro-reports enable more efficient citizen reporting and help in situation recognition.
Keynote talk given at Digital Health conference in Montreal.
How to use data from all sources to prepare a model of a person for analysis and prediction in context of health.
Qualitative Causality discovers potential causal relationships among the underlying phenomena for understanding, prevention, and planning using qualitative human understandable events rather than quantitative variables.
The 21st century began with a major disruption: the rapid rise of smartphones meant that capturing, storing, and sharing photos and their context became easier than using text. Photos and videos communicate directly, without the need for language or literacy. Until recently, photos were used as compelling memories. Now, photos are increasingly used to convey intent and information.related to a moment. A photo may be linked to many other photos along different dimensions. One may also create explicit links among photos or objects in photos. All photos on the Web form a Visual Web that links photos with other photos and other information elements including all documents on the WWW. This Visual Web offers opportunities to address new societal issues and solve many difficult yet unsolved problems. We discuss nature of the Visual Web, technical challenges, and some interesting opportunities in this area.
ICSC2015 KeyNote: Semantic links in visual webRamesh Jain
Photos and videos are new documents. They are independent of language and literacy. By linking photos with other photos as well as other sources of information, we can create a Web that will be a visual Web. This web may be accessible to people in every part of the world.
The nature of storytelling has been evolving. Now it is becoming more data-based in many applications. This objective storytelling is closely tied to rise of big data.
Multimedia and Big Data are closely related topic. Big data enables solving some important challenges in multimedia and basic principles of multimedia are the key issues in multimedia.
From health persona to societal health uci 131202Ramesh Jain
Personal life style plays important role in a person’s health. It is now possible to analyze and understand a person’s life style. Most people use phones with myriad sensors that continuously generate data streams related to most aspects of their life. By correlating these multi-sensory data streams, it is possible to create an accurate chronicle of a person’s life. By correlating life events with health related events, obtained using wearable sensors and other common sources of information, one can build health persona of a person. Health persona of a person is a long-term objective characterization of a person’s health. By using health persona for a large group of people, one can analyze and understand health patterns and causes of different diseases in a society. In this talk, we present a framework that collects, manages, and correlates personal data from heterogeneous data sources and detects events happening at personal level to build health persona. We use several data streams such as motion tracking, location tracking, activity level, and personal calendar data. We illustrate how recognition algorithms can be applied to Life Event detection problem and then build an objective chronicle for a person. We show how this could be combined with situation detection and help people in making decisions in their every day life. In this talk, we will present our ideas related to health persona, its impact on societal health, and its use in making decisions.
Designing intelligent social systems 121205Ramesh Jain
With emerging technologies and big data, it is now possible to design intelligent social systems. In this presentation, ideas related to designing such systems are presented
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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2. • Why am I excited about things?
• What are We doing?
• What are the challenges?
3. What is your research interest?
• Computer Vision
• Multimedia
• Machine Learning
• Social Media
• Intelligent Systems
• Big Data
• Stream Processing
Building a Better Society.
4. Events and Entities Exist in the real
world.
Events and entities result in Data and
Documents
5. What is the most Fundamental
Problem in Society?
Connecting People to Resources
Effectively, Efficiently, and Promptly
in given Situations.
Hint: Economics, Health Care, Politics, Computer Science,
Operations Research, …
18. Motivation
Location Based
Mobile Applications
Ongoing Archived
Database System
satellite
Environmental
Sensor Devices
Internet of Things
Social Media
Social Life
Network
Experts
People
Governmental
Agencies
Situations
21. • Social observations are now possible with
little latency.
• We can design social systems with feedback.
• Situation Recognition and Need-Availability
identification of resources is a major
challenge.
22. Smart Social Systems Architecture
Situation
Recognition
Evolving
Situations
Available
Resources
Identified
Needs
Need-
Resource
Matcher
Communicati
on/Control
Unit
Resources
People
D
a
t
a
S
o
u
r
c
e
s
Database System
satellite
Environmental
Sensor Devices
Social Media
23. Concept Recognition: Last Century
23
Environm
ents
Real world
Objects
Situations
Activities
SingleMedia
SPACE
TIME
ScenesLocation
aware
Visual
Objects
Trajectories
Visual
Events
Location
unaware
Static Dynamic
Location
aware
Location
unaware
Static Dynamic
Data = Text or Images or Video
25. Concept Recognition: This Century
25
Environm
ents
Real world
Objects
Situations
Activities
SPACE
TIME
Location
aware
Location
unaware
Static Dynamic
HeterogeneousMedia
Location
aware
Location
unaware
Static Dynamic
Data is just Data.
Medium and sources do not matter.
26. • relative position or combination of
circumstances at a certain moment.
• The combination of circumstances at a
given moment; a state of affairs.
29. • Example 1:
– A person shouting.
– 1000 people shouting.
• In a contained building
• In main parts of a city
• Example 2:
– One person complaining about flu.
– Many people from different areas of a country
complaining about flu.
30. Micro-events:
Sensors detecting and chirping
(broadcasting) events
• Billions of disparate kinds of sensors being
placed everywhere.
• Each sensor detects ‘basic events’ and
broadcasts it in a simple form.
• Develop a system to process these micro-
events and make them useful.
31. Example: Cameras in a city
• ‘Chirps’ could be of different types
• Define behaviors like:
– Heavy traffic
– Popular event going on
– People leaving X area
– Violence starting
– . . .
• Use for Macro-behvior analysis
32. From micro events
to situations
Thermodynamics
provides a framework for relating the
microscopic properties of individual
atoms and molecules to the
macroscopic or bulk properties of
materials that can be observed in
everyday life.
33. Data Types in Situation Recognition
• Static Data:
– POIs (location of hospitals)
– Population
– Technical data of hospitals
– Contact persons in committees or health organization, and
– All information on support-potentials for personnel, material and
infrastructure
• Dynamic Data:
– Current disease data
– Twitter/Google Trends
– Environmental data
– Personal Individual data
34. Two Big Challenges
• Data Ingestion to efficiently extract data from
the Web and make them available for later
computation is not-trivial.
• Stream Processing Engine to bridge the
semantic gap between high level concept of
situations and low level data streams.
35. S
Social
Networks
2-D spatial
Grid at time
T
Database
Systems
Global
Sensors
Phone
Apps Internet of
Things
(S, T, T)
S Uses
Application
semantics to
combine
different data
items.
45. 8/21/2013 45
Billions of data sources.
Environment for
Selecting, and
Combining
appropriate sources to detect situations.
Prediction for Pro-active actions
Interactions with different types of Users
Decision Makers
Individuals
46. OutputIngestor
EventSource
Parser
Data Adapter
Emage
Generator
(+resolution mapper)
Processing
EvShop Internal Storage
Query
Parser
Query
Rewriter
Event Stream Processing
Executor
ᴨ
ᴨ
Action Parser
Register EventSource Register Continuous Query
Situation
Emage
Visualization
(Dashboard)
Actuator
Communication
Action Control
Event Property &
Other Information
(e.g., spatio-temporal
pattern)
µ
Data Access Manager
Live Stream
Archived Stream
Situation Stream
Real-Time
DataSource
(e.g., sensor
streams, geo-image
streams)
Near Real-Time
DataSource
(e.g., preprocessing
data streams, social
media streams)
Raw Event
47. Input Manager
External Event
Preprocessing
(Collaboration)
47
Real-Time Sensor Streams
e.g., Cloud Satellite Images
Real-Time Sensor Streams
e.g., Wind Speed, Traffic Flow
RealTime DataSource
1D Data
Wrapper
STT to Emage
2D Data
Wrapper
Data Adapter Emage Generator
Emage
Emage
Factory
STT
Emage
Raw Social Media Streams
e.g., Twitter, News RSS Feed
NearRealTime DataSource
Event Model
Wrapper
STT to Emage
Data Adapter Emage Generator
Emage
Emage
Factory
STT
Topic Event
Detection
Abnormal
Event
Detection
Raw Sensor Streams
e.g., PM2.5 data
“EventModel” Streams
e.g., suddenly change
of data trend
within time window
Emage Store
STT Store
Metadata Store
EventSource
Parser Interface
(Optional)
RealTime
Emage Streams
NearRealTime
Emage Streams
Processing
Manager
ES Descriptor
ES Control
(Start/Stop/
View ES)
Users Input
Automatic Data/Events Flow
InitialResolution
AggregationFunc
Metadata
Theme
AdapterType
SourceURL
TimeWindow
Parameters
48. 48
Stream Processing Engine
Operators Manager
Built-in
Operators
User-Defined
Operators
ᴨ
ᴨ
µ
Data
Access
ᴨ
ᴨ
µ
Data
Access
ᴨ
ᴨ
µ
Data
Access
Input Manager
(Accessing
External Data)
Event Stream Executor
Operators
Nodes
Internal Storage
(Accessing Internal
Data)
AsterixDB, SciDB,
MongoDB
Emage Store
STT Store
Metadata Store
Query Parser
Interface
Query
Descriptor
Query Control
(Start/Stop/ View)
Real-time/
near real-time
Emage Streams
Archived
Emage Streams Situation Streams
Emage
Interpolation
Function
Emage
Conversion
Final
Resolution
Parameter Operators
Operators
Store Parameters
Retrieve Parameters
Query
Rewriter
Execution Plan
59. Meeting with
development group
Yoga with
Jena
Meeting with
test group
Mina’s birthday party
9:30-11:00 12 - 1
4:00-
5:00
Transportation
walking
walking
Transportation
Transportation
Transportation
Home HomeWork Caspian
HavingBreakfast
Doingthedishes
Commuting
Commuting
Walking
Walking
Meeting
Meeting
working
Working
Exercise
Exercise
Working
Working
Working
Working
Meeting
Meeting
Walking
Commuting
Commuting
Commuting
Shopping
Shopping
Commuting
Partying
Partying
Partying
Partying
Partying
Commuting
Commuting
Commuting
Sleeping
Sleeping
WatchingTV
Cleaning
Personicle
Commute Walk Meet work Exer. work Meet Commute shop party Commute Sleep
GPSlocationtracking
Calendar
Activity
levelPersonicle
61. Research Challenges
• Situation Recognition
• Persona and Personal Context
• Chronicle Analytics and Visualization
• Massive Geo-Spatial Heterogeneous
Stream Processing
• Dynamic Need-Resource Optimization
62. Situation Recognition
• Next Frontier in Concept Recognition
• Heterogeneous Geo-spatial Dynamic Data
• Social data and IoT become a key element
• Application and domain semantics
• Model definitions
• High dimensionality
• Unification of data: Social-Cyber-Physical
63. Persona and Personal Context
• Not only Logs of Keyboard and Surfing.
• You Log and explore every thing.
– Entity resolution on TURBO
• Many new data processing and unification
challenges.
65. MicroBlogs and Twitter:
LIMITATIONS
• Very LOW Signal-to-Noise ratio: High
Noise-to-Signal ratio
• Focus on being broad platform.
• Difficult to extract SIGNAL.
• Information must be extracted from
limited text.
66. Solution: Tweeting Applications
• Develop focused Apps: Focused MicroBlogs
• Get all information from ‘motivated’ and
collaborative users.
• Help them solve their problem.
68. Chronicle Analytics
• Enterprise Warehouse were for late 20th
Century – Planetary Warehouses are defining
this century.
• Big data is important because it collects
everything that happens to build ‘Prediction
Machines’.
• Machine learning and visualization are the
key tools.
69. Massive Geo-Spatial
Heterogeneous Stream Processing
• Why does the DATA become so BIG?
• And it will keep getting BIGGER.
• We have to go beyond Batch Processing as
primary computing approach.
• Should be of great interest to Social Media
researchers.
70. Dynamic Need-Resource Optimization
• What are the fundamental problems in
Computer Science?
– Time and memory compexity
– Operating systems, networks, storage management,
algorithms, …
• What is the main concern in
– Economics?
– Healthcare?
– Politics?
– …
71. Live EventShop and Collaboration
• Live EventShop Demo
– http://auge.ics.uci.edu/eventshop/
• Current Collaborators & Plan
– Cyber-Physical Cloud Computing Project
• NICT, NIST
– SLN4MOP Project
• Sri Lanka Farmers; Prof. Ginige in Sydney leading
– Open Source EventShop by mid-year of 2013
• HCL
72. Thanks for your time and attention.
For questions: jain@ics.uci.edu