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International Conference on Big Data Analytics

BDA 2021: Big-Data-Analytics in Astronomy, Science, and Engineering pp 3–15Cite as

Big Data Management for Policy Support in Sustainable Development

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13167))

Abstract

Achievement of targets and indicators for Sustainable Development Goals (SDGs) are of key interest to policy-makers worldwide. Sustainable development requires a holistic perspective of several dimensions of human society and addressing them together. With the increasing proliferation of Big Data, Machine Learning, and Artificial Intelligence, there is increasing interest in designing Policy Support Systems (PSS) for supporting policy formulation and decision-making. This paper formulates an architecture for a PSS, based on combining data from several sources. These datasets are subject to cleaning and semantic resolution and are then used as inputs to support the building of semantic models based on Bayesian Networks. A set of models built for different SDG sub-goals and indicators is used to create a “Policy Enunciator” in the form of data stories. Policy formulation is supported by modeling interventions and counter-factual reasoning on the models and assessing their impact on the data stories. Two kinds of impacts are observed: (a) downstream impacts that track expected outcomes from a given intervention, and (b) lateral impacts, that provide insights into possible side-effects of any given policy intervention.

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Notes

  1. 1.

    UN Sustainable Development Goals https://sdgs.un.org/.

  2. 2.

    Transforming our world: the 2030 Agenda for Sustainable Development https://sdgs.un.org/2030agenda.

  3. 3.

    No ‘one size fits all’ approach for localisation of SDGs: India at UN https://www.hindustantimes.com/india-news/no-one-size-fits-all-approach-for-localisation-of-sdgs-india-at-un-101625802791716.html.

  4. 4.

    SDG 6 Policy Support System: https://sdgpss.net/en/.

  5. 5.

    UN SDG Goal 6: https://sdgs.un.org/goals/goal6.

  6. 6.

    CostingNature: http://www.policysupport.org/costingnature.

  7. 7.

    OGD: https://data.gov.in.

  8. 8.

    ENAM: https://enam.gov.in/.

  9. 9.

    NAQI: https://app.cpcbccr.com/AQI_India/.

  10. 10.

    UN statistics portal: http://data.un.org/.

  11. 11.

    https://lod-cloud.net/.

  12. 12.

    FAOSTAT: https://www.fao.org/faostat/.

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Authors and Affiliations

  1. International Institute of Information Technology, 26/C, Electronics City Phase 1, Bangalore, Karnataka, India

    Pooja Bassin, Niharika Sri Parasa, Srinath Srinivasa & Sridhar Mandyam

Authors
  1. Pooja Bassin
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  2. Niharika Sri Parasa
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  3. Srinath Srinivasa
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  4. Sridhar Mandyam
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Corresponding author

Correspondence to Srinath Srinivasa .

Editor information

Editors and Affiliations

  1. National Institute of Technology, Delhi, India

    Shelly Sachdeva

  2. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Yutaka Watanobe

  3. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Subhash Bhalla

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Bassin, P., Parasa, N.S., Srinivasa, S., Mandyam, S. (2022). Big Data Management for Policy Support in Sustainable Development. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big-Data-Analytics in Astronomy, Science, and Engineering. BDA 2021. Lecture Notes in Computer Science(), vol 13167. Springer, Cham. https://doi.org/10.1007/978-3-030-96600-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-96600-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96599-0

  • Online ISBN: 978-3-030-96600-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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