Journeys in big data statistics
The realm of big data is a very wide and varied one. We discuss old, new, small and big data, with some of the important challenges including dealing with highly-structured and object-oriented data. In many applications the objective is to discern patterns and learn from large datasets of historical...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2018
|
| Online Access: | https://eprints.nottingham.ac.uk/49932/ |
| _version_ | 1848798113223933952 |
|---|---|
| author | Dryden, Ian L. Hodge, David J. |
| author_facet | Dryden, Ian L. Hodge, David J. |
| author_sort | Dryden, Ian L. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The realm of big data is a very wide and varied one. We discuss old, new, small and big data, with some of the important challenges including dealing with highly-structured and object-oriented data. In many applications the objective is to discern patterns and learn from large datasets of historical data. We shall discuss such issues in some transportation network applications in non-academic settings, which are naturally applicable to other situations. Vital aspects include dealing with logistics, coding and choosing appropriate statistical methodology, and we provide a summary and checklist for wider implementation. |
| first_indexed | 2025-11-14T20:14:36Z |
| format | Article |
| id | nottingham-49932 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:14:36Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-499322020-05-04T19:38:00Z https://eprints.nottingham.ac.uk/49932/ Journeys in big data statistics Dryden, Ian L. Hodge, David J. The realm of big data is a very wide and varied one. We discuss old, new, small and big data, with some of the important challenges including dealing with highly-structured and object-oriented data. In many applications the objective is to discern patterns and learn from large datasets of historical data. We shall discuss such issues in some transportation network applications in non-academic settings, which are naturally applicable to other situations. Vital aspects include dealing with logistics, coding and choosing appropriate statistical methodology, and we provide a summary and checklist for wider implementation. Elsevier 2018-05-30 Article PeerReviewed Dryden, Ian L. and Hodge, David J. (2018) Journeys in big data statistics. Statistics & Probability Letters, 136 . pp. 121-125. ISSN 0167-7152 https://www.sciencedirect.com/science/article/pii/S0167715218300580 doi:10.1016/j.spl.2018.02.013 doi:10.1016/j.spl.2018.02.013 |
| spellingShingle | Dryden, Ian L. Hodge, David J. Journeys in big data statistics |
| title | Journeys in big data statistics |
| title_full | Journeys in big data statistics |
| title_fullStr | Journeys in big data statistics |
| title_full_unstemmed | Journeys in big data statistics |
| title_short | Journeys in big data statistics |
| title_sort | journeys in big data statistics |
| url | https://eprints.nottingham.ac.uk/49932/ https://eprints.nottingham.ac.uk/49932/ https://eprints.nottingham.ac.uk/49932/ |