The social media subcommittee has been active again on day four of WCCI 2014, tweeting sessions and making notes for today's post.
The first event was a panel session on Big Data and Computational Intelligence, chaired by Jerry Mendel. Jerry gave an overview of big data, and called for innovative approaches to solve big data problems.
Jose Lazano made the point that big data problems are the same as we have been solving in computational intelligence for years, but that the approaches have to be different. He described the characteristics of big data as the three Vs: Volume, as in the scale of the data; Velocity, the speed the data arrives; and Variety, the wide scope of what the data represents.
Nitesh Chawla added a fourth V, Veracity. How much confidence do we have in the data and its value? He also noted that while companies like Facebook have no problems getting big data sets, it is difficult for academics. Tim Havens echoed this, adding that there is a need for good benchmark data sets for big data. He also pointed out that there are always trade-offs how you validate algorithms for big data.
Xiaodong Li gave a brief overview of the computational intelligence techniques for big data. He especially listed deep learning, parallelized machines and robustness techniques for dealing with volume, and online learning methods for dealing with velocity. He also gave an excellent definition of big data: if you can fit it into memory, it's not big data.
The last speaker was Yaochu Jin, who pointed out that due to its volume and variety, biological data like microarray data and gene regulatory networks is also big data.
Janusz Kacprzyk gave an invited lecture on 'Fuzzy dynamic programming: a step towards cognitive dynamic programming'. Janusz presented fuzzy dynamic systems modelling of government regional planning over multiple years for improving cognitive perceptions of socio-economic problems and quality of life. Janusz's passion for this shone through as he stated clearly that this is a real fuzzy model, for a real and important problem, for real end users, and for real money. The model contained fuzzy goals and fuzzy constraints that are objective, such as government limits, but also domain knowledge from experts that are subjective, such as seven life quality indicators.
Huaguang Zhang gave an invited lecture on 'Fuzzy Real-time Leakage Supervisory System for Fluid Transportation Pipeline Networks: New Methods and Applications'. Huaguang research focused on identifying weak leakage in long-distance petroleum pipelines the transient flow produces a drop in pressure at the leakage point of 1%, which is a challenging task. The importance of identifying weak leakages was demonstrated with recent loss of life and economic loses estimated to be 4.4 billion yuan RMB. The first stage of Huanguang's system filters noise signals but not the leakage signals. The second stage validated each characteristic of a chaotic system with statistical analysis. The third stage modelled sections of pipe and raised alarms when differences in sections over time met a threshold. The fourth stage modelled the operating model with the generalised fuzzy hyperbolic tangent model.