Big data analytics:
While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With...
Saved in:
| Other Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Amsterdam
Elsevier
2015
|
| Series: | Handbook of statistics (Amsterdam, Netherlands)
v. 33 |
| Subjects: | |
| Online Access: | FAW01 Volltext |
| Summary: | While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | 1 online resource (525 pages) |
| ISBN: | 9780444634979 0444634975 9780444634924 0444634924 |
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| 520 | |a While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions | ||
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| spelling | Big data analytics edited by Venu Govindaraju, Vijay Raghavan, C.R. Rao Amsterdam Elsevier 2015 1 online resource (525 pages) txt rdacontent c rdamedia cr rdacarrier Handbook of statistics (Amsterdam, Netherlands) v. 33 Includes bibliographical references and index While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions COMPUTERS / General bisacsh Big data Statistical methods Data Mining (DE-588)4428654-5 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s 2\p DE-604 Data Mining (DE-588)4428654-5 s 3\p DE-604 Govindaraju, Venugopal edt Raghavan, Vijay edt Rao, C. R. edt Erscheint auch als Druck-Ausgabe Big data analytics 9780444634924 http://www.sciencedirect.com/science/handbooks/01697161/33 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Big data analytics COMPUTERS / General bisacsh Big data Statistical methods Data Mining (DE-588)4428654-5 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4802620-7 (DE-588)4123037-1 (DE-588)4143413-4 |
| title | Big data analytics |
| title_auth | Big data analytics |
| title_exact_search | Big data analytics |
| title_full | Big data analytics edited by Venu Govindaraju, Vijay Raghavan, C.R. Rao |
| title_fullStr | Big data analytics edited by Venu Govindaraju, Vijay Raghavan, C.R. Rao |
| title_full_unstemmed | Big data analytics edited by Venu Govindaraju, Vijay Raghavan, C.R. Rao |
| title_short | Big data analytics |
| title_sort | big data analytics |
| topic | COMPUTERS / General bisacsh Big data Statistical methods Data Mining (DE-588)4428654-5 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | COMPUTERS / General Big data Statistical methods Data Mining Big Data Datenanalyse Aufsatzsammlung |
| url | http://www.sciencedirect.com/science/handbooks/01697161/33 |
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