Data nowadays comes from various sources including log files, transactional applications, the Web, social media, scientific experiments, and many others. In recent years, various analyses of these data have proven useful to aid companies in engaging and serving their users and defining their corporate strategy, help political candidates win elections, and transform the process of scientific discovery. However, these successes are just the tip of the iceberg: Every day, new, more complex analysis techniques are devised and larger, more varied datasets are accumulated. Tackling the complexity of both the data itself and its analysis remains an open challenge.
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Speculative Approximations for Terascale Distributed Gradient Descent Optimization
Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch ...
Caffe con Troll: Shallow Ideas to Speed Up Deep Learning
We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural ...
The Vision of BigBench 2.0
Data is one of the most important resources for modern enterprises. Better analytics allow for a better understanding of customer requirements and market dynamics. The more data is collected, the more information can be extracted. However, information ...
High-Performance Main-Memory Database Systems and Modern Virtualization: Friends or Foes?
Virtualization owes its popularity mainly to its ability to consolidate software systems from many servers into a single server without sacrificing the desirable isolation between applications. This not only reduces the total cost of ownership, but also ...
Index Terms
- Proceedings of the Fourth Workshop on Data analytics in the Cloud