Difference between revisions of "Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest"

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<b>BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest</b> is a NSF funded project involving a collaboration between [http://aiisc.ai AI Institute, University of South Carolina] and [https://www.osu.edu/ Ohio State University].
 
<b>BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest</b> is a NSF funded project involving a collaboration between [http://aiisc.ai AI Institute, University of South Carolina] and [https://www.osu.edu/ Ohio State University].
 
=Overview=
 
=Overview=
Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g., Haiti, Japan), a hurricane (e.g., Katrina, Sandy), or a flood (e.g., Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project aims to radically reform decision support systems for managing rapidly changing disaster situations by the integration of social, physical and hazard models. The researcher team will serve as a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences, and the social sciences for research, education, and training of undergraduate and graduate students, including those from under-represented groups.
+
The opioid crisis ravaging Ohio and the Midwest disproportionally affects small and rural communities. Harnessing and deploying data holds promise for developing a response to this crisis by policymakers, healthcare providers, and citizens of the communities. Currently, there are many barriers to getting data into the hands of individuals on the frontlines. Crucial data are siloed across law enforcement, public health departments, hospitals and clinics, and county administrations; data often are inaccurate or collected in non-standard ways across different agencies and departments; the stigma of drug abuse limits accurate reporting of drug-related deaths; and information is not shared with the community and other stakeholders because of the lack of a privacy and security framework. Such barriers, for example, prevent individuals with addictions or their families and friends from locating available treatment centers or obtaining other important information in a timely way. Similarly, it is difficult for first responders and healthcare providers to obtain critical up-to-date information. In predominantly rural counties, these challenges are especially daunting because there is often poor connectivity and communication infrastructure. This Big Data Spoke project involves developing scalable, flexible, and connectivity-rich data-driven approaches to address the opioid epidemic. The cyberinfrastructure framework, OpenOD, will be initially designed and deployed in small and rural communities in Appalachia Ohio and the Midwest, where the need for data and connection are greatest. Based upon significant community input, OpenOD will also create end-user applications or enterprise solutions to support stakeholders and communities to mount a response they feel will be most efficient and beneficial at the local level. As a Spoke to NSF?s Midwest Big Data Hub, our efforts can be efficiently scaled, disseminated, and applied to the opioid and other societal problems such as infant mortality, crime, and natural disasters. This project fits within NSF's mission to promote the progress of science (contribute to the science and engineering of large socially relevant cyberinfrastructures) to advance the health and welfare of US citizens (by linking data sources in new and useful ways to empower communities to address societal problems; establishing sustainable partnerships between academia, industry, government and communities; increasing data literacy and community engagement with data science; and enhancing research and education via development/adaptation of training modules and courses in data analytics).
  
The team seeks to design novel, multi-dimensional, cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. They use data from social and physical sensors as input into an integrated model, from which they are designing a new methodology to predict and prioritize the consequences of damage; they are including both temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g., bridges, traffic signals). They are developing innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification correlation, and integration of relevant data across multiple sources and modalities (social, physical and Web). They use novel coupling of socio-linguistic and network analysis to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and the resulting impact on hazard models (e.g. storm surge) and flood mapping. They are developing domain-grounded mechanisms to address pervasive trustworthiness and reliability concerns. Exemplar outcomes include specific tools for first-responders and recovery teams to aid in the prioritization of relief and repair efforts as well as improved flood response, urban mapping, and dynamic storm surge models. They also are providing interdisciplinary training of students, leveraging research in pedagogy in conjunction with Ohio State University's new undergraduate major in data analytics and Wright State University's Big and Smart Data graduate certificate program.  
+
The main goal of this project is to help small and rural communities in the Midwest address the opioid epidemic via BIGDATA (BD) technology. While no communities have been spared, small and rural communities face unique challenges in confronting the opioid epidemic: knowledge and data exist in siloes across multiple organizations with varying jurisdictional boundaries; efforts to collect, link, and analyze data are hampered by a lack of infrastructure and tools; rural areas are plagued by "dead zones" in cellular connectivity; communities lack capacity for data collection, and analytics; needs and resources across effected communities are not uniform and require BD approaches that are flexible, open, leverage significant community input, and can be dutifully validated. Our proposed solution is OpenOD, a framework that provides uniform, relevant and timely access to data. Working integrally with the Midwest Big Data Hub (MBDH) and our partners, our three main objectives are to: (1) Work with local communities to understand strengths and gaps in cyberinfrastructure, data availability, and need for data analytics workforce skills. (2) Assemble flexible cyberinfrastructure that includes a data commons, stakeholder-usable and cloud-amenable data analytics and visualization tools, and internet connectivity with both mobile and non-mobile capabilities. (3) Validate, evaluate, and disseminate cyberinfrastructure and data analytics tools to stakeholder groups throughout the region while fostering new partnerships. OpenOD will create approaches that will allow governing units to deploy openly available tools rather than rely on proprietary tools. In this way, existing disparities in data access and ensuing responses are effectively addressed. The potential contributions of the project are to: (1) Increase BD and STEM literacy and community engagement in underrepresented groups given the operating milieu of OpenOD in rural areas where the population is indigent and lacks adequate skills to join the modern workforce. (2) Improve well-being of individuals in society by linking data sources in new and useful ways to empower communities to address the opioid crisis; improved connectivity and timely delivery of critical information will accelerate community responsiveness and improve preventive strategies. (3) Provide infrastructure for research and education will be improved given that project activities will deliver linked, curated data sets to community stakeholders, researchers and educators. Training modules and courses adapted and developed and shared with local/regional educators and will remain with the communities after the funding period has ended. In addition, new and established partnerships will allow sustainability of the project in the communities for the long-term.
  
 
=People=
 
=People=
*Principal Investigators:  [http://aiisc.ai/amit Amit P. Sheth (AIISC, UofSC)]
+
'''Principal Investigators:''' [http://aiisc.ai/amit Prof. Amit P. Sheth (AIISC, UofSC)]
*Postdoctoral Researcher: [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu]
+
 
*Graduate Researchers: [http://knoesis.org/researchers/usha Usha Lokala], [https://manasgaur.github.io Manas Gaur], and [Vedant Khandelwal].
+
'''Postdoctoral Researchers:''' [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu]
 +
 
 +
'''Graduate Researchers:''' [http://knoesis.org/researchers/usha Usha Lokala], [https://manasgaur.github.io Manas Gaur], and Vedant Khandelwal.
  
 
=Funding=
 
=Funding=
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* NSF Award#: [https://nsf.gov/awardsearch/showAward?AWD_ID=1761931 OAC 1761931]
 
* NSF Award#: [https://nsf.gov/awardsearch/showAward?AWD_ID=1761931 OAC 1761931]
 
* BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
 
* BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
* Timeline: September 1, 2018 - November 30, 2019
+
* Timeline: September 1, 2018 - August 31, 2021
 
* Award Amount: $120,000.00
 
* Award Amount: $120,000.00
 
|-
 
|-
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=Publications=
 
=Publications=
#  
+
# Lamy, Francois R., Raminta Daniulaityte, Monica J. Barratt, [http://knoesis.org/researchers/lokala/ Usha Lokala], [http://aiisc.ai/amit Amit Sheth], and Robert G. Carlson. "Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket." Drug and Alcohol Dependence (2020): 108115.
# [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [http://www.knoesis.org/people/manas/ Manas Gaur],[http://knoesis.org/researchers/lokala/ Usha Lokala],[http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan],[http://knoesis.wright.edu/amit Amit Sheth] and I. Budak Arpinar. [http://knoesis.org/node/2891 "Predictive Analysis on Twitter: Techniques and Applications"]. Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2019.
+
# Kumar, Ramnath, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [http://knoesis.org/researchers/lokala/ Usha Lokala], and [http://aiisc.ai/amit Amit Sheth]. "eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection." In Proceedings of The Web Conference 2020, pp. 1955-1965. 2020.
 
+
# [http://knoesis.org/researchers/lokala/ Usha Lokala], Francois R. Lamy, Raminta Daniulaityte, [http://aiisc.ai/amit Amit Sheth], Ramzi W. Nahhas, Jason I. Roden, Shweta Yadav, and Robert G. Carlson. "Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio." Computational and Mathematical Organization Theory 25, no. 1 (2019): 48-59.
 +
# [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [http://www.knoesis.org/people/manas/ Manas Gaur], [http://knoesis.org/researchers/lokala/ Usha Lokala], [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], [http://aiisc.ai/amit Amit Sheth] and I. Budak Arpinar. [http://knoesis.org/node/2891 "Predictive Analysis on Twitter: Techniques and Applications"]. Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2019.
 +
# [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [http://www.knoesis.org/people/manas/ Manas Gaur], [http://knoesis.org/researchers/lokala/ Usha Lokala], Anurag Illendula, [http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan], Raminta Daniulaityte, [http://aiisc.ai/amit Amit Sheth], and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.
  
 
=Tutorials=
 
=Tutorials=

Revision as of 22:05, 25 November 2020

BD Spokes
Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
200px
Project Overview
Motto To help small and rural communities in the Midwest address the opioid epidemic via BIGDATA (BD) technology
Timeline September 1, 2018 - November 30, 2019
Project Funding
Funding Agency National Science Foundation
Award Amount $120,000.00
Award Number OAC 1761931

BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest is a NSF funded project involving a collaboration between AI Institute, University of South Carolina and Ohio State University.

Overview

The opioid crisis ravaging Ohio and the Midwest disproportionally affects small and rural communities. Harnessing and deploying data holds promise for developing a response to this crisis by policymakers, healthcare providers, and citizens of the communities. Currently, there are many barriers to getting data into the hands of individuals on the frontlines. Crucial data are siloed across law enforcement, public health departments, hospitals and clinics, and county administrations; data often are inaccurate or collected in non-standard ways across different agencies and departments; the stigma of drug abuse limits accurate reporting of drug-related deaths; and information is not shared with the community and other stakeholders because of the lack of a privacy and security framework. Such barriers, for example, prevent individuals with addictions or their families and friends from locating available treatment centers or obtaining other important information in a timely way. Similarly, it is difficult for first responders and healthcare providers to obtain critical up-to-date information. In predominantly rural counties, these challenges are especially daunting because there is often poor connectivity and communication infrastructure. This Big Data Spoke project involves developing scalable, flexible, and connectivity-rich data-driven approaches to address the opioid epidemic. The cyberinfrastructure framework, OpenOD, will be initially designed and deployed in small and rural communities in Appalachia Ohio and the Midwest, where the need for data and connection are greatest. Based upon significant community input, OpenOD will also create end-user applications or enterprise solutions to support stakeholders and communities to mount a response they feel will be most efficient and beneficial at the local level. As a Spoke to NSF?s Midwest Big Data Hub, our efforts can be efficiently scaled, disseminated, and applied to the opioid and other societal problems such as infant mortality, crime, and natural disasters. This project fits within NSF's mission to promote the progress of science (contribute to the science and engineering of large socially relevant cyberinfrastructures) to advance the health and welfare of US citizens (by linking data sources in new and useful ways to empower communities to address societal problems; establishing sustainable partnerships between academia, industry, government and communities; increasing data literacy and community engagement with data science; and enhancing research and education via development/adaptation of training modules and courses in data analytics).

The main goal of this project is to help small and rural communities in the Midwest address the opioid epidemic via BIGDATA (BD) technology. While no communities have been spared, small and rural communities face unique challenges in confronting the opioid epidemic: knowledge and data exist in siloes across multiple organizations with varying jurisdictional boundaries; efforts to collect, link, and analyze data are hampered by a lack of infrastructure and tools; rural areas are plagued by "dead zones" in cellular connectivity; communities lack capacity for data collection, and analytics; needs and resources across effected communities are not uniform and require BD approaches that are flexible, open, leverage significant community input, and can be dutifully validated. Our proposed solution is OpenOD, a framework that provides uniform, relevant and timely access to data. Working integrally with the Midwest Big Data Hub (MBDH) and our partners, our three main objectives are to: (1) Work with local communities to understand strengths and gaps in cyberinfrastructure, data availability, and need for data analytics workforce skills. (2) Assemble flexible cyberinfrastructure that includes a data commons, stakeholder-usable and cloud-amenable data analytics and visualization tools, and internet connectivity with both mobile and non-mobile capabilities. (3) Validate, evaluate, and disseminate cyberinfrastructure and data analytics tools to stakeholder groups throughout the region while fostering new partnerships. OpenOD will create approaches that will allow governing units to deploy openly available tools rather than rely on proprietary tools. In this way, existing disparities in data access and ensuing responses are effectively addressed. The potential contributions of the project are to: (1) Increase BD and STEM literacy and community engagement in underrepresented groups given the operating milieu of OpenOD in rural areas where the population is indigent and lacks adequate skills to join the modern workforce. (2) Improve well-being of individuals in society by linking data sources in new and useful ways to empower communities to address the opioid crisis; improved connectivity and timely delivery of critical information will accelerate community responsiveness and improve preventive strategies. (3) Provide infrastructure for research and education will be improved given that project activities will deliver linked, curated data sets to community stakeholders, researchers and educators. Training modules and courses adapted and developed and shared with local/regional educators and will remain with the communities after the funding period has ended. In addition, new and established partnerships will allow sustainability of the project in the communities for the long-term.

People

Principal Investigators: Prof. Amit P. Sheth (AIISC, UofSC)

Postdoctoral Researchers: Dr. Ugur Kursuncu

Graduate Researchers: Usha Lokala, Manas Gaur, and Vedant Khandelwal.

Funding

Nsf.jpg
  • NSF Award#: OAC 1761931
  • BD Spokes: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
  • Timeline: September 1, 2018 - August 31, 2021
  • Award Amount: $120,000.00

Publications

  1. Lamy, Francois R., Raminta Daniulaityte, Monica J. Barratt, Usha Lokala, Amit Sheth, and Robert G. Carlson. "Listed for sale: analyzing data on fentanyl, fentanyl analogs and other novel synthetic opioids on one cryptomarket." Drug and Alcohol Dependence (2020): 108115.
  2. Kumar, Ramnath, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth. "eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection." In Proceedings of The Web Conference 2020, pp. 1955-1965. 2020.
  3. Usha Lokala, Francois R. Lamy, Raminta Daniulaityte, Amit Sheth, Ramzi W. Nahhas, Jason I. Roden, Shweta Yadav, and Robert G. Carlson. "Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio." Computational and Mathematical Organization Theory 25, no. 1 (2019): 48-59.
  4. Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth and I. Budak Arpinar. "Predictive Analysis on Twitter: Techniques and Applications". Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2019.
  5. Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.

Tutorials


Related Projects

Concurrent Projects