[目的/意义]大数据时代给传统知识管理带来了变革，为适应大数据时代的到来，需要重新认识和定位知识管理过程，应用大数据技术工具构建新的知识管理模型，以应对知识管理的更新与挑战。[方法/过程]通过梳理数据到知识的形成过程，结合大数据的4V特征，了解知识管理所需的大数据技术工具，并将大数据时代的知识管理划分为知识生产、知识积累、知识交流、知识应用4个阶段，结合大数据技术工具的使用，构建新的知识管理模型，提出应对碎片整合、应用价值、硬件支撑、隐私伦理等问题的对策。[结果/结论]大数据的发展推动了知识管理过程从传统模型向技术模型的转型，大数据时代的知识管理模型与大数据技术的使用紧密相关，强调从海量碎片化数据中提炼知识价值，并更有效地辅助组织决策，为此，需要做好硬件设施支撑和信息安全保障，将大数据技术与小样本分析相结合，推动知识管理走向新的层次。
[Purpose/significance] The period of big data has brought changes to traditional knowledge management. In order to adapt to the coming of the big data, it is necessary to re-understand the process of knowledge management, and use technology tools to build new knowledge management models to overcome the challenge of knowledge management. [Method/process] After the summarization of the formation process of data to knowledge, together with 4V features of big data, and the analysis of big data technology tools needed for knowledge management, the knowledge management in the period of big data is divided into four stages: knowledge production, knowledge accumulation, knowledge exchange and knowledge application. With the help of technology tools, a new model of knowledge management is built. Solutions are proposed to the problems such as debris consolidation, application value, hardware support, and ethics on privacy. [Result/conclusion] Big data promote the development of the transformation of the knowledge management process from the traditional model. The model of knowledge management in the period of big data is closely related to the use of technology tools. It emphasizes the value of knowledge extracted from mass fragmentation data and more effectively assists organizational decision making. Therefore, hardware support, information security, and combining big data technology with small sample analysis will push knowledge management to a new level.
[1] 刘智慧,张泉灵.大数据技术研究综述[J].浙江大学学报(工学版),2014,48(6):957-972.
[2] COOPER P. Data,information,knowledge and wisdom[J]. Anaesthesia & intensive care medicine,2014,15(1):44-45.
[3] 赵蓉英,魏绪秋.聚识成智:大数据环境下的知识管理框架模型[J].情报理论与实践,2017,40(9):20-23.
[4] 孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,50(1):146-169.
[5] 许立波,潘旭伟,袁平,等.知识智能涌现创新:概念、体系与路径[J].智能系统学报,2017,12(1):47-54.
[6] 刘洁璇.高校图书馆知识管理中的数据治理[J].情报科学,2018,36(1):108-129.
[7] 刘捷.大数据环境下基于知识管理的国有企业文档管理优化研究[J].科技情报开发与经济,2014,24(17):121-123.
[8] 曾润喜,王琳,杜洪涛.基于知识管理视角的大数据研究网络与结构研究[J].情报学报,2016,35(11):1173-1184.
[9] 野中郁次郎,竹内宏高.创造知识的企业——日美企业持续创新的动力[M].北京:水利水电出版社,2006.
[10] SIMONET A,FEDAK G,RIPEANU M. Active data:a programming model for managing big data life cycle across heterogeneous systems and infrastructures[J]. Future generation computer systems,2015,53(9):25-42.
[11] 秦殿启.整合与大数据理念下的个人知识组织[J].情报理论与实践,2014,37(2):19-22.
[12] LIEW C S,WAH T Y,SHUJA J,et al. Mining personal data using smartphones and wearable devices:a survey[J]. Sensors,2015,15(2):4430-4469.
[13] 黄天恩,孙宏斌,郭庆来,等.基于电网运行仿真大数据的知识管理和超前安全预警[J].电网技术,2015,39(11):3080-3087.
[14] 何军.大数据对企业管理决策影响分析[J].科技进步与对策,2014,31(4):65-68.
[15] 叶英平,卢艳秋,肖艳红.基于网络嵌入的知识创新模型构建[J].图书情报工作,2017,61(7):102-110.
[16] 彭宇,庞景月,刘大同,等. 大数据:内涵、技术体系与展望[J].电子测量与仪器学报,2015,29(4):469-482.
[17] 程学旗,靳小龙,王元卓,等.大数据系统和分析技术综述[J].软件学报,2014,25(9):1889-1908.
[18] 金澈清,钱卫宁,周敏奇,等.数据管理系统评测基准:从传统数据库到新兴大数据[J].计算机学报,2015,38(1):18-34.
[19] 邱东.大数据时代对统计学的挑战[J].统计研究,2014,31(1):16-22.
[20] 任磊,杜一,马帅,等.大数据可视分析综述[J].软件学报,2014,25(9):1909-1936.
[21] 官思发,朝乐门.大数据时代信息分析的关键问题、挑战与对策[J].图书情报工作,2015,59(3):12-18.
[22] 任福兵.碎片化与拼图化:网络传播的扩散与整合[J].情报资料工作,2014,35(3):18-24.
[23] 张文德,程涵,刘田.面向用户决策的高校信息碎片化整合模型[J].情报理论与实践,2018,41(3):64-67.
[24] 郭熙铜,张晓飞,刘笑笑,等.数据驱动的电子健康服务管理研究:挑战与展望[J].管理科学,2017,30(1):3-14.
[25] 胡小荣,姚长青,高影繁.基于风险短语自动抽取的上市公司风险识别方法及可视化研究[J].情报学报,2017,36(7):663-668.
[26] GU B,YE Q. First step in social media measuring the influence of online management responses on customer satisfaction[J]. Production and operations management,2014,23(4):570-582.
[27] 黄国彬,郑琳.大数据信息安全风险框架及应对策略研究[J].图书馆学研究,2015,33(13):24-29.
[28] 王世伟.论大数据时代信息安全的新特点与新要求[J].图书情报工作,2016,60(6):5-14.
