大数据时代为科技情报研究与服务带来了重大的机遇和挑战，迫切需要发展新的数据驱动型情报研究模式来变革数据治理和工作流程，提高情报研究和咨询服务的质量。本文概述了传统的人力驱动型科技情报工作模式，分析了存在的问题和局限性；综述了海量异构数据集成、数据管理与分析方法和工具的开发进展；提出了建设数据驱动型科技情报研究模式的整体架构，展望了未来研究的重点。
The era of big data brings both opportunities and challenges to the scientific information analysis (SIA) and the intelligent information services. It is an urgent task developing a new SIA framework to reform the data governance and the workflow, and to improve the quality of the information services. This paper describes the traditional SIA mode currently applied. It also reviews the progresses in the fields of the massive heterogeneous data integration, the data management and the analytics methods, and their applications. An integrationbased conceptual framework for the SIA is proposed through an examination of the limitations of the traditional mode. The new framework is characterized by the development of an Intelligent Decision-making Support System based on the Big Data that can store, organize, process, and visualize heterogeneous data. Next, the functions and the characteristics of the proposed framework are explained. The paper concludes with a discussion of the future research.
[1] Honavar V. The promise and potential of big data:A case for discovery informatics[J]. Review of Policy Research, 2014, 31(4):326-330.
[2] Hey T, Tansley S, Tolle K. The fourth paradigm:Data-intensive scientific discovery[M]. Redmond, Washington:Microsoft Research, 2009.
[3] 张志强. 论科技情报研究新范式[J]. 情报学报, 2012, 31(8):788-797. Zhang Zhiqiang. New paradigm for S & T intelligence studies[J]. Journal of the China Society for Scientific and Technical Information, 2012, 31(8):788-797.
[4] Clarivate Analytics. Derwent data analyzer[EB/OL].[2018-05-06]. https://clarivate.com/products/derwent-data-analyzer.
[5] Chen Chaomei. CiteSpace[EB/OL]. (2016-10-03)[2018-05-06]. http://cluster.cis.drexel.edu/~cchen/citespace.
[6] Provost F, Fawcett T. Data science and its relationship to big data and data-driven decision making[J]. Big Data, 2013, 1(1):51-59.
[7] Lee S, Mortara L, Kerr C, et al. Analysis of document-mining techniques and tools for technology intelligence:Discovering knowledge from technical documents[J]. International Journal of Technology Management, 2012, 60(1/2):130-156.
[8] IDC. Big data analytics:Future architectures, skills and roadmaps for the CIO[EB/OL].[2018-04-05]. http://triangleinformationmanagement.com/wp-content/uploads/2013/12/bigdata-idcwp.pdf.
[9] IBM. Extracting business value from the 4 V's of big data[EB/OL].[2017-10-26]. http://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data.
[10] National Institute of Standards and Technology (NIST). NIST big data interoperability framework:Volume 1, definitions[EB/OL]. (2015-10-22)[2017-09-26]. https://bigdatawg.nist.gov/_uploadfiles/NIST.SP.1500-1.pdf.
[11] Power D. Using "Big Data" for analytics and decision support[J]. Journal of Decision Systems, 2014, 23(2):222-228.
[12] Hilbert M. Big data for development:A review of promises and challenges[J]. Development Policy Review, 2016, 34(1):135-174.
[13] Hendler J. Data integration for heterogeneous datasets[J]. Big Data, 2014, 2(4):205-215.
[14] Gartner. Magic quadrant for data integration tools[EB/OL].[2017-10-20]. https://www.gartner.com/doc/3393017/magicquadrant-data-integration-tools.
[15] Walsh C, Rodrigue B, Mummadi Y. Data and analytics:Open source data integration tool comparison[EB/OL].[2017-10-20]. https://www.excella.com/wp-content/uploads/2016/03/Open-Source-DI-Tool-Comparison_March2016.pdf.
[16] Hassani P. Best open source data integration tools[EB/OL]. (2017-03-25)[2017-10-08]. https://blogs.systweak.com/2017/03/best-open-source-data-integration-tools.
[17] Pentaho Corporation. Data integration-kettle[EB/OL].[2017-10-12]. http://community.pentaho.com/projects/data-integration.
[18] Talend. Talend open studio for data integration[EB/OL].[2017-10-12]. https://www.talend.com/download/talend-openstudio/#t4.
[19] Stanford University. DeepDive[EB/OL].[2017-10-16]. http://deepdive.stanford.edu.
[20] Zhang C, Ré C, Cafarella M, et al. DeepDive:Declarative knowledge base construction[J]. Communications of the ACM, 2017, 60(5):93-102.
[21] The University of Arizona. Data infrastructure buildings blocks (DIBBs) for intelligence + security informatics (ISI) research and community[EB/OL].[2017-10-20]. https://ai.arizona.edu/research/dibbs#introduction.
[22] Ma B, Jiang T, Zhou X, et al. A novel data integration framework based on unified concept model[J]. IEEE Access, 2017, 5:5713-5722.
[23] Daraio C, Lenzerini M, Leporelli C, et al. Data integration for research and innovation policy:An ontology-based data management approach[J]. Scientometrics, 2016, 106(2):857-871.
[24] 孟小峰, 杜治娟. 大数据融合研究:问题与挑战[J]. 计算机研究与发展, 2016, 53(2):231-246. Meng Xiaofeng, Du Zhijuan. Research on the big data fusion:Issues and challenges[J]. Journal of Computer Research and Development, 2016, 53(2):231-246.
[25] Williams K, Wu J, Choudhury S, et al. Scholarly big data information extraction and integration in the CiteSeerχ digital library[C]//Proceeding of IEEE 30th International Conference on Data Engineering (ICDE). Piscataway NJ:IEEE 2014:68-73.
[26] Hu H, Wen Y, Chua T, et al. Toward scalable systems for big data analytics:A technology tutorial[J]. IEEE Access, 2014, 2:652-687.
[27] Chen M, Mao S, Liu Y. Big data:A survey[J]. Mobile Networks and Applications, 2014, 19(2):171-209.
[28] Kambatla K, Kollias G, Kumar V, et al. Trends in big data analytics[J]. Journal of Parallel and Distributed Computing, 2014, 74(7):2561-2573.
[29] Yaqoob I, Hashem I A T, Gani A, et al. Big data:From beginning to future[J]. International Journal of Information Management, 2016, 36(6):1231-1247.
[30] Wlodarczyk T, Hacker T. Current trends in predictive analytics of big data[J]. International Journal of Big Data Intelligence, 2014, 1(3):172-180.
