Proceedings of the 2017 International Conference on Management Science and Management Innovation (MSMI 2017)

Rough Rule-Based Systems for Sparse and Dense Data Analysis Used in Project Evaluation

Authors
Tadeusz A. Grzeszczyk
Corresponding Author
Tadeusz A. Grzeszczyk
Available Online June 2017.
DOI
https://doi.org/10.2991/msmi-17.2017.77How to use a DOI?
Keywords
Project evaluation; Sparse and dense data analysis; Rough rule-based systems
Abstract
Massive volumes of unstructured, multidimensional and heterogeneous data used in project management and evaluation processes generally rapidly increase. It causes and reinforces the need of searching for new, appropriate and efficient methods addressing the use of sparse and dense data analysis, and Big Data technology for these processes. Research on rule-based systems can lead to significant advances in project management and evaluation. The main objective of this paper is to discuss how rule-based systems can be used for sparse and dense data analysis applied in project evaluation. The obtained results indicated the great potential of such systems based on rough set theory. At the beginning of this paper, the core problems of rough rule-based systems are given. Then, sparse and dense data models build on decision tables are shortly shown. Subsequently, an exemplary of project classification using rough rule-based system based on sparse data is briefly characterized. Finally, the conclusions and recommendations which concern possible directions of project evaluation methods and systems development are presented.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Tadeusz A. Grzeszczyk
PY  - 2017/06
DA  - 2017/06
TI  - Rough Rule-Based Systems for Sparse and Dense Data Analysis Used in Project Evaluation
BT  - Proceedings of the 2017 International Conference on Management Science and Management Innovation (MSMI 2017)
PB  - Atlantis Press
SP  - 344
EP  - 347
SN  - 2352-5428
UR  - https://doi.org/10.2991/msmi-17.2017.77
DO  - https://doi.org/10.2991/msmi-17.2017.77
ID  - Grzeszczyk2017/06
ER  -