Improving the quality of life in our cities is a challenge for the next years. To analyze, interpret and understand society’s trends and make decisions we need to develop new algorithms and visualization techniques and we need to reinforce the interdisciplinary nature of many problems of data analytics. During our International Conference on Big Data and Analytics for Smart Cities, Prof. Cercone will be presenting how a renewed effort on big data analytics and visualization methods can affect Smart Cities-focused activities. In the following interview, he talks to us about the challenges related to Big Data Analytics.
Read more about the Smart City 360 Summit taking place on 13-16 October 2015 in Toronto/Bratislava and the first International Conference on Big Data and Analytics for Smart Cities taking place on 13 October 2015 in Toronto.
BIGDasc 2015, the first International Conference on Big Data and Analytics for Smart Cities, will take place on October 13. As General Chair, what do you expect from the event?
I expect to see an interplay between various stakeholders, academic, public sector, private sector and government representatives, to discuss issues about how to make our cities (including the GTA soon to be a megacity of 10 million) move livable, more serviceable, less dangerous, accessible, and generally improve quality of life for its residents. This is the first of hopefully many important venues on this topic and hopefully BIGDasc2015 will pave the way for successful venues in the future.
The research area related to Big Data analysis requires huge efforts, due to the inadequacy of the traditional data processing applications. Could you give us an overview of the present status of the progresses in the field?
There is renewed effort on big data analytics and visualization methods undergoing research and development in almost all aspects of our society that affect our lives. Some examples abound with streaming data – data that accumulates so fast that it cannot be analyzed by traditional data mining algorithms (such is the case for most transaction data). Thus data from mobile medical devices, from traffic signals and cameras, from genomics, social media, energy data, etc. require new algorithms and visualization techniques to analyze, interpret and understand trends, data utility, and make decisions.
In your opinion, what are the key-challenges to deal with in the next future, in order to positively address the research and industry sectors involved in smart cities-focused activities?
The interdisciplinary nature of many problems of data analytics needs to be reinforced. Teams of researchers and practitioners working on related problems but often speaking a different language may provide breakthroughs that individual researchers may miss. For example, statisticians, computer scientists, engineers, mathematicians and subject area specialists could form a formidable team to work on many types of big data analytics problems. Working with industry (including forming new start-ups) is imperative; I have always believed that academics can provide advanced prototypes but that product development, marketing, business intelligence etc. are best left to the private sector.