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Big Data is no fad. The world is growing at an exponential rate, and so is the size of data collected across the globe. The data is becoming more meaningful and contextually relevant, breaks new ground for machine learning and artificial intelligence (AI), and even moves them from research labs to production. That is, the problem has shifted from collecting massive amounts of data to understanding it, i.e., turning data into knowledge, conclusions, and actions. This Big AI, however, often faces poor scale-up behaviour from algorithms that have been designed based on models of computation that are no longer realistic for Big Data. This special issue constitutes an attempt to highlight the algorithmic challenges and opportunities but also the social and ethical issues of Big Data. Of specific interest and focus have been computation- and resource-efficient algorithms when searching through data to find and mine relevant or pertinent information.
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From Big Data to Big Artificial Intelligence?
Algorithmic Challenges and Opportunities of Big Data
- Publication date
- Springer Berlin Heidelberg
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