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Amos DAVID,

Université de Lorraine, Nancy, France ;

African University of Science and Technology, Abuja, Nigeria;

Laboratory DICEN-IDF, Paris, France

 

 

Nadine NDJOCK,

ESSTIC/Université de Yaoundé 2-Soa ;

Laboratory YMIS, Yaoundé, Cameroun

Sub-theme: Foundations and methods for KO

Title: Big Data, Knowledge Organization and Decision Making – Opportunities and limit

Keywords: Big data, knowledge organization, Decision-making, Information visualization, Data analysis

 

Objectives

A concept that is currently attracting much interest in the field of information science is the concept of Big Data. How does it relate to Knowledge Organization and to decision making? What are its opportunities and limits? These are some questions we try to answer in this paper. This paper has been mainly inspired by Challenges and Opportunities with Big Data: A white paper prepared for the Computing Community Consortium committee of the Computing Research Association (Agrawal, et al., 2012) and the theme of the biennial conference “Transition from Observation to Knowledge to Intelligence” started in 2014 and to be organized now by the newly created ISKO-West Africa chapter (DAVID, A., & UWADIA, C. 2016)

The Big Data pipeline is presented as below (Agrawal, et al., 2012)

Summarizing the challenges with Big Data, (Agrawal, et al., 2012) states that

“Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep and what to discard, and how to store what we keep reliably with the right metadata. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search: transforming such content into a structured format for later analysis is a major challenge. Data analysis is a clear bottleneck in many applications, both due to lack of scalability of the underlying algorithms and due to the complexity of the data that needs to be analyzed. Finally, presentation of the results and its interpretation by non-technical domain experts is crucial to extracting actionable knowledge”.

 

Methods

Within the framework of our study using the competitive intelligence approach to problem decision solving, we have also identified the problem associated with the use of normal watch technique which is closely related to the current approach employed when referring to the potentials of Big Data (DAVID, A., 2016)

In the watch approach to problem solving, information is first collected, then verified for validity and then examine the possible application for solving the problem at hand. We believe that this approach is generally not appropriate since it produces a lot of noise – gathering information that will be discarded because not applicable to the problem at hand.

CVI model for information use in decision making process

Based on the observation above, we propose that the steps be inverted, starting with the identification of the possible use of the information for solving the problem at hand, verify the relevance and validity of the information sources and then collect the relevant information

IVC model for information use in decision making process

 

To facilitate understanding of results, we propose to extend the information system by integrating visualization tools to present the information. It is from these visual presentations, also based on the indicators, which in turn are derived from the basic information, which allow a better interpretation of all the information collected (Ndjock, 2017).

Main results

Our proposals in terms of models and tools have been implemented in some systems. Added-value information through visualization has been applied in two applications, which will be developed in the full paper:

The first application was developed for the PhD thesis of Dr. Nadine NDJOCK (Ndjock, 2017), which concerns optimization of decision-making process by the visualization of information through the concept of observatory, applied to the educational system in Cameroon. The decision-maker obtains the evolution of indicators, which enables to guide a strategic decision such as the efficient management of personnel or adjustment of the training program.

The second application concerns - ISKO (International Society for Knowledge Organization) membership management system. The system is used to manage the members of the association. Not only can Executive Office and Chapter Administrators create, access and modify member profiles, they can obtain added-value information through visualization tools that allow deployment of development strategies.

 

Conclusion

Technologies have been proposed for managing Bid Data, such as Hadoop file system. Some layers on Hadoop have been proposed to enhance access and information systems integrating query and visualization techniques. However, a lot still need to be done to conceptualize the method to use before information collection in view of Big Data management for analysis and for decision making. We are currently working on the concept of observatory systems.

References

Agrawal, D., Bernstein, P., Elisa, B., Susan, D., Umeshwar, D., Franklin, M., . . . Papakonstantinou, Y. (2012). Challenges and Opportunities with Big Data: A white paper prepared for the Computing Community Consortium.

DAVID, A. (2016). From data to intelligence - Strategic decision making through information system Transition from Observation to Knowledge to Intelligence. Lagos, Nigeria: ISBN 978-2-9546760.

DAVID, A., & UWADIA, C. (2016). Transition from Observation to Knowledge to Intelligence . Lagos, Nigeria: ISBN 978-2-9546760-3-6.

Duhigg , C. (2012). The Power of Habit: Why we do what we do in life and business. Doubleday Canada, Random House Canada Ltd, pp. 190.

Ndjock, F. N. (2017). From observation to decision-making : How an information system can improve strategic decision-making. International Journal of Social Science and Technology. Vol. 2 No. 3, pp 89-99.