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Advancing Science in the Big Data Era: Challenges and Opportunities

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34 diapositives · 443 Views · Dec 19, 2019

Explore the impact of big data on scientific paradigms, economic sectors, and the sharing economy, with a focus on transportation and tourism industries. Discover insights into regulatory challenges in the sharing economy and the implications for government response. Learn about academic research techniques in the era of big data and the implications for graduate education.

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  1. SPEECH Computational Science and the Rise of the Fourth Paradigm THE CHALLENGE FOR RESEARCH AND GRADUATE SCHOOL Prof. Achmad Nurmandi, M.Sc. Director of JK School of Government UMY Secretary of APSPA GRADUATE SEMINAR, UNIVERSITAS MUHAMMADIYAH SUMATERA UTARA, MEDAN 01/12/2018

  2. OUTLINE 1. SCIENTIFIC PARADIGM 2.BIG DATA ERA 3. ECONOMIC SECTOR CHANGES 4. SHARING ECONOMY 5. SHARING ECONOMY IN TRANSPORTATION SECTOR IN INDONESIA, PHILIPPINES AND TAIWAN 6. TOURISM AND BIG DATA

  3. Jim Gray’s Four Scientific Paradigms

  4. Four Paradigms of Science Fourth: Now (Big data) Third: Pre-big data Second: Pre-computers First: Pre- Renaissace Adopted from Hey at al (2009)

  5. The Challenges for Scholars

  6. The Opportunities for Scholars

  7. Big Data Era? ‘Big Data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis.’’ (Gantz and Reinsel, 2011 as quoted in Chen et al., 2014)

  8. 300 Economic Scale PricewaterhouseCoopers (2015)pegged the sharing economy to growfrom $15 billion dollars in 2014 to $335 billion dollars in 2025. Uber (started in 2009) is valued atUS $68 billion, which is more than each of the three big Americanautomobilefirms of Chrysler, Ford, and General Motors (Chen, 2015).A Airbnb (launched in 2008) is valued at $30 billion, which is more thanthe Hilton hotel chain and nearly as much as the Marriott hotels(Schechner & Bensinger, 2016 ).

  9. How does government regulation on sharing economy?

  10. SHARING ECONOMY

  11. Examples of disruptive innovation creating regulatory challenges include human-robot collaboration, constant body monitoring, and driverless cars • The dynamic regulatory framework allows rulemaking to overcome its historic constraints and path dependencies that traditionally focused the rulemaking process on stable and presumptively optimal rules

  12. Big data in the form of digitized data that grows at exponential rates and can be captured and manipulated electronically draws on several core sources including the internet of things, public records, social media, and cameras, as well as satellite tracking

  13. Ex-Post Facts-Based (Trial-and-Error) Rulemaking The ex-post facts-based, trial-and-error rulemaking in the existing regulatory framework80 often produces suboptimal regulatory outcomes that may no longer be sustainable as disruptive innovation accelerates and causes unprecedented societal change

  14. Stable and Presumptively Optimal Rules The existing regulatory infrastructure, including Congress, regulatory agencies, self-regulatory bodies, and the literature on regulation, relies almost exclusively on stable and presumptively optimal rules

  15. How does Indonesian Government response to sharing economy?

  16. TAIWAN

  17. Phillippines

  18. Academic implications for research and research method technique Data-driven government science and public administration seeks to hold to the tenets of the scientific method, but is more open to using a hybrid combination of abductive, inductive and deductive approaches to advance the understanding of a phenomenon (Kichin, R, 2014) Abductive is a form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation.

  19. continued • it seeks to generate hypotheses and insights Kitchin ‘born from the data’ rather than ‘born from the theory’ (Kelling et al., 2009: 613).

  20. IMPLICATION GRADUATE EDUCATION BIG DATA

  21. Jodipan Colourful Tourism of Malang, Indonesia is one of examples the product of academic work in the Big Data Era. The innovative sciences in Big Data Era

  22. The Big Data Tools for branding product of academic works

  23. Domestic Tourist

  24. Foreign Tourist

  25. Income Change of Jodipan Tourism: Before and After

  26. Research 2: Countered Indonesia’s Swift Securitization of the Natuna IslandsHow Jakarta China’s Claims in the South China Sea.

  27. CRITICAL DISCOURCES ANALYSIS (CDA) • . CDA offers a methodological framework that considers both textual and contextual sources, and allows for a dynamic interaction between the discourse and its audiences.

  28. Research Questions 1. to provide an analysis of the Indonesian mainstream printed media involving the tensions around the NI to show that between 2013 and 2016 a swift securitization has occurred. 2. to survey popular social media outlets to assess whether Jakarta’s argument that China’s claims represent an urgent national security threat for Indonesia has been accepted by the Indonesian people

  29. THANKS