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Big data refers to large and complex data sets that, under existingapproaches, exceed the capacity and capability of current compute platforms,systems software, analytical tools and human understanding. Numerous lessons onthe scalability of big data can already be found in asymptotic analysis ofalgorithms and from the high-performance computing (HPC) and applicationscommunities. However, scale is only one aspect of current big data trends;fundamentally, current and emerging problems in big data are a result ofunprecedented complexity--in the structure of the data and how to analyze it,in dealing with unreliability and redundancy, in addressing the human factorsof comprehending complex data sets, in formulating meaningful analyses, and inmanaging the dense, power-hungry data centers that house big data.  The computer science solution to complexity is finding the rightabstractions, those that hide as much triviality as possible while revealingthe essence of the problem that is being addressed. The "big data challenge"has disrupted computer science by stressing to the very limits the familiarabstractions which define the relevant subfields in data analysis, datamanagement and the underlying parallel systems. As a result, not enough ofthese challenges are revealed by isolating abstractions in a traditionalsoftware stack or standard algorithmic and analytical techniques, and attemptsto address complexity either oversimplify or require low-level management ofdetails. The authors believe that the abstractions for big data need to berethought, and this reorganization needs to evolve and be sustained throughcontinued cross-disciplinary collaboration.

Rethinking Abstractions for Big Data: Why, Where, How, and What