Loading...
Please wait, while we are loading the content...
Similar Documents
Beyond the Hype: Cloud Computing in Analytics
| Content Provider | Semantic Scholar |
|---|---|
| Author | Dejaeger, Karel Louis, Philippe Broucke, Seppe K. L. M. Vanden Eerola, Tuomas Goedhuys, Lieve Baesens, Bart |
| Copyright Year | 2012 |
| Abstract | Machine learning (ML) techniques are becoming commonplace in business and research alike. With the automatization of data collection e fforts, evermore data is being captured, rendering the task of extracting insightful patterns increasingly challenging. In addition to this 'data avalanche' becoming evermore overwhelming, the usage of more computationally intensive algorithms in predictive analysis tasks also gives rise to new issues and challenges, so that a ML approach typically entails a trade off between computational effi�ciency and predictive performance. In recent years, however, new paradigms in analytics have been proposed geared towards solving these data and computational challenges, including cloud computing, distributed computing, and parallel computing approaches. We set out to discern one of these new hypes in analytics, cloud computing, and present a case study hereof which was performed at KU Leuven. In this study, we set up a benchmarking experiment using the Microsoft Windows Azure cloud platform with Techila Technologies middleware, and compare the results with those obtained in a non-parallelized setup. The results show that significant analysis speed-ups can be gained when performing computational tasks in the cloud. |
| File Format | PDF HTM / HTML |
| DOI | 10.2139/ssrn.2165720 |
| Alternate Webpage(s) | http://www.techila.fi/wp-content/uploads/2012/08/KU-Leuven-Beyond-the-hype-Cloud-computing-in-analytics2.pdf |
| Alternate Webpage(s) | https://doi.org/10.2139/ssrn.2165720 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
