Study on Library Service Mode of Teaching and Research University under Big Data Era
- DOI
- 10.2991/icence-16.2016.168How to use a DOI?
- Keywords
- Big Data Area, Teaching and Research University, Service Model
- Abstract
Big Data in Teaching Research University Library Management and Service to make teaching and research university library resources and services, service content, service environment, service methods and service models have had a profound change. Analysis of large data waiting for extended service mode conversion and data mining services and protection of library services resources on teaching and research university library services, discusses the next big data environment teaching and research university library service model . Keywords: big data; teaching and research university; library; service model With the rapid development of information technology and information society, the mobile Internet, networking, PC and various types of sensors produce huge amounts of data, people gradually notice that the data on social importance. When the solution to the collection, storage, computing, massive data analysis problems, make the massive data and computing power of the perfect combination, and all walks of life have a major impact on our society will enter the era of big data. As the library information resource center, only full use of all available resources of big data, the focus needs to establish a diversified active service mode, in order to meet the diverse needs of readers.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Guishan Zhang PY - 2016/09 DA - 2016/09 TI - Study on Library Service Mode of Teaching and Research University under Big Data Era BT - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) PB - Atlantis Press SP - 912 EP - 915 SN - 2352-538X UR - https://doi.org/10.2991/icence-16.2016.168 DO - 10.2991/icence-16.2016.168 ID - Zhang2016/09 ER -