Abstract
The retrieval of biological data is quite trending nowadays as a significant amount of research is being carried out in this area. There are numerous algorithms being proposed for analyzing biological data based on pattern matching based approach. Several new pattern matching based algorithms ranging from brute force approach to most recent algorithms are being developed. As it is well understood that for retrieval of data, the retrieval algorithm must be fast in terms of execution, very less attention has been paid towards the factors which might affect the execution time of an algorithm. Factors like pattern length, type of datasets, input size and other related factors can affect the execution of an algorithm, but how much is really unknown and unaddressed. Hence, this paper has addressed this problem by utilizing factorial design 2k. The factorial technique is designed and implemented in such a way, which will give new insight to researchers while proposing or developing algorithms for retrieving biological data. The study shows for the algorithm to be efficient, the main motivating factor is pattern length. Pattern length is having a 38.5% effect on the execution time of an algorithm followed by the type of dataset with the impact of 18%.
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This research is supported by FRGS FP003A-2017, Faculty of Computer Science and Information Technology, University of Malaya.
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Parvez, H.M.S., Hakak, S., Gilkar, G.A., Abdur Rahman, M. (2020). Factorial Analysis of Biological Datasets. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_1
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DOI: https://doi.org/10.1007/978-981-13-7564-4_1
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