To meet the challenges of massive data, many big data platforms have been used in
practice. In these data processing platforms, there are many correlated parameters that have …

Apache Spark is one of the most popular big data frameworks that abstract the underlying
distributed computation details. However, even though Spark provides various abstractions …

Many decision-making queries are based on aggregating massive amounts of data, where
sampling is an important approximation technique for reducing execution times. It is …

Scheduling of dynamic and multitasking workloads for big-data analytics is a challenging
issue, as it requires a significant amount of parameter sweeping and iterations. Therefore …

To support different application scenarios, big data frameworks usually provide a large
number of performance-related configuration parameters. Online auto-tuning these …

Apache Spark is an open-source cluster computing framework. It has emerged as the next
generation big data processing engine, overtaking Hadoop MapReduce which helped ignite …

With large data sets, it can be time consuming to run gradient based optimization, for
example to minimize the log-likelihood for maximum entropy models. Distributed methods …

The field of data analytics is currently going through a renaissance as a result of ever-
increasing dataset sizes, the value of the models that can be trained from those datasets …

Per-instance algorithm selection seeks to recommend, for a given problem instance and a
given performance criterion, one or several suitable algorithms that are expected to perform …