Increasingly severe I/O bottlenecks on High-End Computing machines are prompting scientists to process simulation output data while simulations are running and before placing data on disk – ”in situ” and/or ”in-transit”. There are several options in placing in-situ data analytics along the I/O path: on compute nodes, on staging nodes dedicated to analytics, or after data is stored on persistent...

These are abridged lecture notes from the Spring 2017 course offering of “Principles of Data Analytics” that I offer at Iowa State University annually. This graduate level course offers an introduction to a variety of data analysis techniques, particularly those relevant for electrical and computer engineers, from an algorithmic perspective. Topics include techniques for classification, visuali...

INTRODUCTION In recent years “big data” has become something of a buzzword in business, computer science, information studies, information systems, statistics, and many other fields. As technology continues to advance, we constantly generate an ever-increasing amount of data. This growth does not differentiate between individuals and businesses, private or public sectors, institutions of learni...

We will demonstrate the visual analytics system V istream , that supports interactive mining of complex patterns within and across live data streams and stream pattern archives. Our system is equipped with both computational pattern mining and visualization techniques, which allow it to not only efficiently discover and manage patterns but also effectively convey the mining results to human ana...

This paper analyses the difficulty of big data analytics problems and the potential of swarm intelligence solving big data analytics problems. Nowadays, the big data analytics has attracted more and more attentions, which is required to manage immense amounts of data quickly. However, current researches mainly focus on the amount of data. In this paper, the other three properties of big data an...

work? at defining big data, varying based on context, domain, and perspective. From the infrastructure’s perspective, big data has been defined as data with high volume, velocity, and variety (3V), and unpredictability. In this context, it has also been defined as data with some aspect that’s so large that current, typical methods can’t be used to process it.1,2 From the analytics’ perspective,...

Over the last years, users’ information needs over structured data expanded from seeking exact answers to precise queries, to performing database analytics tasks, such as finding entities or patterns similar to a given entity or pattern, discovering interesting patterns, or predicting novel relations and concepts. As part of its response, the research community proposed a multitude of algorithm...