Big Data 4 Development


Fernando, L., Perera, A. S., Lokanathan, S., Ghouse, A.
LIRNEasia research fellow, Dharshana Kasthurirathna, Ph.D. presented a paper, ‘Detecting Geographically Distributed Communities using Community Networks,’ at the International Workshop on Mining for Actionable Insights in Social Networks that was held in conjunction with the Tenth ACM International Web Search and Data Mining Conference in Cambridge in February 2017. The paper was co-authored by three LIRNEasia research fellows (Dharshana Kasthurirathna, Madhushi Bandara, Danaja Maldeniya) and Mahendra Piraveenan from the University of Sydney. Based on the presentation, there was an invitation to extend the paper to be submitted to a special issue of the Elsevier Information System’s journal, with a draft journal paper due in April 2017.
In July of 2016, the Global Partnership for Sustainable Development Data, announced a new multi-million dollar funding initiative to support collaborative data innovations for sustainable development. The University of Tokyo and Colombo-based LIRNEasia are among the winners in the pilot round of this initiative. Their proposal, entitled “Dynamic Census,” aims to improve the existing census approach by deriving insights from mobile operators’ call detail records (CDR). It will supplement population and housing census data by adding dynamic aspects of population distribution to changes in population distribution over time, at high frequency. More details.
Lokanathan, S., Perera-Gomez, T., Zuhyle, S.
Kasthurirathna, D., Piraveenan, M., Bandara, M. & Maldeniya, D.

Visit to University of Dhaka

Posted on February 11, 2017  /  0 Comments

Last weekend (3-4 February 2017), I along with my colleagues Shazna and Dedunu visited University of Dhaka. We were able to share our experiences in conducting policy relevant research on big data for development in Sri Lanka (see slides), with both faculty and students. The other objective was to meet with the faculty and staff associated with the Data and Design Lab at the university, which is a collaboration between Dhaka University and LIRNEasia. The lab is led by Dr. Moinul Zaber, who is a member of the faculty at Dhaka University and a Research Fellow with LIRNEasia.
The UN Data Innovation Lab invited LIRNEasia to share our experience in entering data partnerships and the challenges associated with the same, at a workshop held in Cape Town on the 19-20 January 2017. The workshop, co-hosted by UN Global Pulse, centred on designing data capacity within the UN system. The session conducted by LIRNEasia was attended by representatives from a range of UN agencies including UNICEF, UN WTO, UN Women and UNAIDS. In addition, other participants at the session included representatives from Flowminder and Facebook. I had the opportunity to share LIRNEasia’s experience in building relationships with the government and private sector data providers, particularly in terms of leveraging mobile data for urban planning and traffic management in Sri Lanka.

LIRNEasia at UN Data Forum

Posted on January 15, 2017  /  0 Comments

The UN Data Forum starts tomorrow. LIRNEasia’s Sriganesh Lokanathan will speak at the session organized by UN Global Pulse. Agenda.
We have been writing about competition issues around big data since 2014 (though I could claim 1991). Now the New York Times weighs in. The competition concerns echo those that gradually emerged in the 1990s about software and Microsoft. The worry is that as the big internet companies attract more users and advertisers, and gather more data, a powerful “network effect” effectively prevents users and advertisers from moving away from a dominant digital platform, like Google in search or Facebook in consumer social networks. Evidence of the rising importance of data can be seen from the frontiers of artificial intelligence to mainstream business software.
Data philanthropy was what UN Global Pulse came up with as a foundation for private entities donating data for public services. But now Uber has come up with another story. The site, which Uber will invite planning agencies and researchers to visit in the coming weeks, will allow outsiders to study traffic patterns and speeds across cities using data collected by tens of thousands of Uber vehicles. Users can use Movement to compare average trip times across certain points in cities and see what effect something like a baseball game might have on traffic patterns. Eventually, the company plans to make Movement available to the general public.
The Sri Lanka Association for the Advancement of Science (SLAAS) is the primary “learned society” for Sri Lankan academics. It’s a rather staid outfit where I think you need multiple nominees to support your application to join and they reject papers if they’re not in the correct font (I may be exaggerating a little because this is based on my memories from the 1980s). Anyway, Sriganesh Lokanathan, Team Leader – Big Data Research at LIRNEasia had been asked by the University of Sri Jayewardenepura to pull together a 60 mt panel discussion on big data for development. He had got an excellent panel together, Ruvan Weerasinghe from University of Colombo/Informatics Institute of Technology, Shehan Perera from University of Moratuwa, Srinath Perera from WSO2 and himself. I moderated the panel.
Europe has been the fount of data protection absolutism. Not a problem for anyone else but countries such as Thailand and Indonesia are well on the way to model their legislation on the European model. But Chancellor Merkel has seen that the absolutist approach poses dangers to European consumers and businesses as well. Europeans are famous for banning things, Merkel said. These bans are put in place for good reason, she said, but can be damaging if taken to excess.
I was competitively selected to attend the Self-Organizing Conference on Machine Learning 2016 organized by OpenAI which was held in San Francisco on October 7-8, 2016. OpenAI is a non-profit artificial intelligence (AI) research company initiated by Elon Musk and top research scientists in AI and machine learning (ML) to promote safe and friendly AI. Since its inception in late 2015 the company has attracted top researchers and scientists from both industry and academia to work on most interesting problems of AI. The Self-Organizing Conference on Machine Learning 2016 was an experimental gathering that OpenAI organized for the first time to promote collaboration amongst AI/ML researchers, overcoming the overheads of a conventional academic conference. This is how they presented the motive behind organizing this event.
I was invited to speak to the staff of the Joint Research Centre of the European Commission in Seville last Tuesday (11th October 2016). Their colleagues from Ispra, Italy joined in via video conference as well. I talked about LIRNEasia’s experiences and lessons in leveraging big data for public purposes. The slides that I used are available HERE.
We have been engaging with local universities from the start of our big data work, not just to source researchers and collaborators, but also to broaden the horizons of students. That big data can be leveraged for public purposes is not something that they had previously thought of till we arrived on the scene. This week (18th October 2016) we continued those efforts, conducting a lecture for students at the University of Sri Jayewardenepura on our ongoing big data for development research. The slides are available HERE.
LIRNEasia in partnership with the Centre for Internet and Society (CIS) convened a two-day workshop to discuss a ‘research and policy agenda on big data for sustainable development in the Global South.’ The workshop held in Madrid on the 8th and 9th of October 2016, was a side event of the International Open Data Conference 2016.  The objective of the workshop was to brainstorm ways of establishing Southern-led network to tackle some of the emerging opportunities and challenges in the use of big data in developing countries. The workshop explored a variety of issues around leveraging big data to tackle sustainable development. These include issues around representativity and marginalization, researching harms (competition, privacy, surveillance), researching solutions (legislation, regulation, ethics), and addressing challenges in relation to developing research capacity, accessing data and influencing policy.