Big data analytics on Apache Spark
Apache Spark has emerged as the de facto framework for big data analytics with its
advanced in-memory programming model and upper-level libraries for scalable machine …
advanced in-memory programming model and upper-level libraries for scalable machine …
A survey on platforms for big data analytics
The primary purpose of this paper is to provide an in-depth analysis of different platforms
available for performing big data analytics. This paper surveys different hardware platforms …
available for performing big data analytics. This paper surveys different hardware platforms …
Analysis of {Large-Scale}{Multi-Tenant}{GPU} clusters for {DNN} training workloads
With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …
beginning to incorporate machine learning models across a number of products. These …
A survey of data partitioning and sampling methods to support big data analysis
Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …
for big data processing and analysis. In cluster computing, data partitioning and sampling …
Deepdb: Learn from data, not from queries!
The typical approach for learned DBMS components is to capture the behavior by running a
representative set of queries and use the observations to train a machine learning model …
representative set of queries and use the observations to train a machine learning model …
Videoedge: Processing camera streams using hierarchical clusters
Organizations deploy a hierarchy of clusters-cameras, private clusters, public clouds-for
analyzing live video feeds from their cameras. Video analytics queries have many …
analyzing live video feeds from their cameras. Video analytics queries have many …
Live video analytics at scale with approximation and {Delay-Tolerance}
Video cameras are pervasively deployed for security and smart city scenarios, with millions
of them in large cities worldwide. Achieving the potential of these cameras requires …
of them in large cities worldwide. Achieving the potential of these cameras requires …
Awstream: Adaptive wide-area streaming analytics
The emerging class of wide-area streaming analytics faces the challenge of scarce and
variable WAN bandwidth. Non-adaptive applications built with TCP or UDP suffer from …
variable WAN bandwidth. Non-adaptive applications built with TCP or UDP suffer from …
Data lifecycle challenges in production machine learning: a survey
Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …
Low latency geo-distributed data analytics
Low latency analytics on geographically distributed datasets (across datacenters, edge
clusters) is an upcoming and increasingly important challenge. The dominant approach of …
clusters) is an upcoming and increasingly important challenge. The dominant approach of …