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A Big Data Knowledge Computing Platform for Intelligence Studies
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Journal of Computer Science & Systems Biology

ISSN: 0974-7230

Open Access

Value Added Abstracts - (2020) Volume 0, Issue 0

A Big Data Knowledge Computing Platform for Intelligence Studies

Wen Yi
Chinese Academy of Sciences, China

Abstract

Intelligence studies is a method of using modern information technology and soft science research methods to form valuable information products by collecting, selecting, evaluating and synthesizing information resources. With the advent of the era of big data, the core work of information analysis with data is facing enormous opportunities and challenges. How to make good use of big data in an effort to solve the problem of big data, optimize and improve the traditional intelligence studies methods and tools, innovation and research based on big data are the key issues that need to be studied and solved in current intelligence studies work.

Through the analysis of intelligence studies methods and common tools under the background of big data, we sort out the processes and requirements of the intelligence studies work under big data environment, design and implement a universal knowledge computing platform for intelligence studies, which enables intelligence analysts to easily use all kinds of big data analysis algorithms without writing programs (http://www.zhiyun.ac.cn). Our platform is built upon the open source big data system of Hadoop and Spark. All the data are stored in the distributed file system HDFS and data management system of Hive. All of the computational resources are managed with Yarn and each of the submitted task is scheduled with the workflow scheduler system Oozie. The core of the platform consists of three modules: data management, data calculation and data visualization.

The data management module is used to store and manage the relevant data of intelligence studies, which consists of four parts: metadata management, data connection, data integration and data management. The platform supports the import and management of multi-source heterogeneous data, including papers, patents from ISI, PubMed, etc., and also supports the data import with API of MySQL, Hive and other database systems. The platform has more than 20 kinds of data cleaning and updating rules, such as search and replace, regular cleaning, null filling, etc., and also supports users to customize and edit the cleaning rules.

The data calculation module is used to store and manage the big data analysis algorithm and intelligence analysis process, and provides a user-friendly GUI for users to create customized intelligence analysis process, and the packaged process can be submitted to the platform for calculation and obtain the calculation results of each step. In the system, a task is formulated as a directed acyclic graph (DAG) in which the source data flows into the root nodes. Each node makes operations on the data, generates new data, and sends the generated data to its descendant nodes for conducting further operations. Finally, the results flow out from the leaf nodes. The data visualization module is used to visualize the results of intelligence analysis and calculation, including more than ten kinds of visualization charts such as line chart, histogram chart, radar chart and word cloud chart. Practice has proved that the platform can well meet the requirements of intelligence studies in various fields in the era of big data, and promote the application of data mining and knowledge discovery in the field of intelligence studies.

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Biography:

Wen Yi, professor of Chengdu Library and Information Center, Chinese Academy of Sciences, holds a Master’s degree in Information Science from Sichuan University. He specialized in big data analysis and knowledge discovery information system and has published more than 30 papers about these fields. He is the head of the project-“the construction of Intellectual Property Network of CAS” and several other projects. His research has gained the “Sichuan Province Science and Technology Progress Third Award

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Speaker Publications:

1. C. K. Reddy, "A survey of platforms for big data analytics," Journal of Big Data (Springer), vol. 1, no. 8, pp. 1-20, 2014.

2. C. P. Chen and C.-Y. Zhang, "Data-intensive applications, challenges, techniques and technologies: A survey on Big Data," Information Sciences, vol. 275 , p. 314–347, 2014.

3. Tableau, "Top 8 Trends for 2016: Big Data," 2015. [Online]. Available: www.tableau.com/Big-Data.

4. A. McAfee and E. Brynjolfsson, "Big data: The management revolution," Harvard Business Review. October, pp. 61-68, 2012.

5. S. Roche, "IDC Reveals 53% of Organizations in the APEJ Region Consider Big Data and Analytics Important for Business," 21 4 2016.

7th International Conference on Big Data Analysis and Data Mining July 17-18, 2020 Webinar.

Abstract Citation:

Wen Yi, A Big Data Knowledge Computing Platform for Intelligence Studies, Data Mining 2020, 7th International Conference on Big Data Analysis and Data Mining – July 17-18, 2020 Webinar.

(https://datamining.expertconferences.org/speaker/2020/wen-yi-chinese-academy-of-sciences-china)

Google scholar citation report
Citations: 1932

Journal of Computer Science & Systems Biology received 1932 citations as per google scholar report

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