One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques
Abstract-The ever increasing number of cores per chip will be accompanied by a pervasive data deluge...
Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big...
Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in m...
One of the main challenges in data analytics is that discovering structures and patterns in complex ...
Data mining is applied in business to find new market opportunities from data stored in operational...
Advances in hardware and software technology enable us to collect, store and distribute large quanti...
Technological advances in the past decade, hardware and software alike, have made access to high-per...
In the present work we apply High-Performance Computing techniques to two Big Data problems. The frs...
This thesis explains Big Data Phenomenon, which is characterised by rapid growth of volume, variety ...
Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present nu...
“Data mining” for “knowledge discovery in databases” and associated computational operations first i...
Analyzing massive-data sets and streams is computationally very challenging. Data sets in systems bi...
© 2018 The Authors. We describe our Multiscale Computing Patterns software for High Performance Mult...
This work examines the challenges and opportunities of Machine Learning (ML) for Monitoring and Oper...
The Data Science domain has expanded monumentally in both research and industry communities during t...
Abstract-The ever increasing number of cores per chip will be accompanied by a pervasive data deluge...
Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big...
Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in m...
One of the main challenges in data analytics is that discovering structures and patterns in complex ...
Data mining is applied in business to find new market opportunities from data stored in operational...
Advances in hardware and software technology enable us to collect, store and distribute large quanti...
Technological advances in the past decade, hardware and software alike, have made access to high-per...
In the present work we apply High-Performance Computing techniques to two Big Data problems. The frs...
This thesis explains Big Data Phenomenon, which is characterised by rapid growth of volume, variety ...
Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present nu...
“Data mining” for “knowledge discovery in databases” and associated computational operations first i...
Analyzing massive-data sets and streams is computationally very challenging. Data sets in systems bi...
© 2018 The Authors. We describe our Multiscale Computing Patterns software for High Performance Mult...
This work examines the challenges and opportunities of Machine Learning (ML) for Monitoring and Oper...
The Data Science domain has expanded monumentally in both research and industry communities during t...
Abstract-The ever increasing number of cores per chip will be accompanied by a pervasive data deluge...
Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big...
Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in m...