We propose a parameter server framework for distributed machine learning problems. Both
data and workloads are distributed over worker nodes, while the server nodes maintain …

The initial design of Apache Hadoop [1] was tightly focused on running massive,
MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has …

The broad success of Hadoop has led to a fast-evolving and diverse ecosystem of
application engines that are building upon the YARN resource management layer. The open …

Datacenter-scale computing for analytics workloads is increasingly common. High
operational costs force heterogeneous applications to share cluster resources for achieving …

This paper introduces Trill--a new query processor for analytics. Trill fulfills a combination of
three requirements for a query processor to serve the diverse big data analytics space:(1) …

Declarative large-scale machine learning (ML) aims at flexible specification of ML algorithms
and automatic generation of hybrid runtime plans ranging from single node, in-memory …

Due to the rapid growth of data and the ever increasing model complexity, which often
manifests itself in the large number of model parameters, today, many important machine …

Can we model how fraudsters work to distinguish them from normal users? Can we predict
not just which movie a person will like, but also why? How can we find when a student will …

Large-scale machine learning (ML) systems are becoming widely used. Typically, these ML
systems run on fixed resources, but it is difficult to find their optimal configurations (eg, how …

To solve the limitation of Hadoop on scalability, resource sharing, and application support,
the open‐source community proposes the next generation of Hadoop's compute platform …