Big Data Mining Future Challenges


There are many future important challenges in Big Data management and analytics, that arise from the nature of data: large, diverse, and evolving. These are some of the challenges that researchers and practitioners will have to deal with in the years to come:

  • Analytics Architecture. It is not clear yet how an optimal architecture of an analytics systems should be constructed to deal with historic data and with real- time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in realtime by decomposing the problem into three layers: the batch layer, the serving layer, and the speed layer. It combines in the same system as Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, extensible, allows ad hoc queries, minimal maintenance, and debuggable.
  • Evaluation. It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once. Also, it will be important to avoid the trap of a focus on error or speed as Kiri Wagstaff discusses in her paper “Machine Learning that Matters”.
  • Distributed mining. Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practi- cal and theoretical analysis to provide new methods.
  • Time evolving data. Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.
  • Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t lose anything, or sampling where we choose data that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are losing information, but the gains in space may be in orders of magnitude. For example Feldman et al. use coresets to reduce the complexity of Big Data problems. Coresets are small sets that provably approximate the original data for a given problem. Using merge-reduce the small sets can then be used for solving hard machine learning problems in parallel.
  • Visualization. A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for example the photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.
  • Hidden Big Data. Large quantities of useful data are getting lost since new data is largely untagged file- based and unstructured data. The 2012 IDC study on Big Data  explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.

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