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Visualization investigation on the marine data with multivariate statistical analysis methods

Li Y., Lu Z., Wang M.

Polish Maritime Research

2017

S 2

89--94

Marine information is an important way for us to know and study more about the ocean. Marine data makes the basic of marine information. Because of the huge quantity and diversity of marine data, and at the same time marine data is polyatomic variable, we start with statistical analysis methods to search for the regularity of the marine data. On one hand, we get the aggregate variation functions of the marine data by factor analyzing in aspect of the spatiality. Then we visually describe the marine status of the studied sea area with pre variogram function and post variogram function. On the other hand, we used cluster analysis method to get the verifying rule in time and make visible graphs of the marine data. In this way, we can also supply with the suggestions in classifying the sea seawater quality. The data processing result shows that the suggested methods in this article are both operable and effective. At the same time some reasonable suggestions are given in the article.

Detection and quantification of self- similarity in data traffic for prediction of performance of marine data file transfer

Konorski J.

Biuletyn Instytutu Morskiego w Gdańsku

1999

Vol. 26, No. 2

29--48

The time-sensitivity of large marine data files over a communication network necessitates accurate simulative prediction of the file transfer performance. Careful data traffic modelling is required to fit the actual traffic characteristics for subsequent generation of synthetic traffic traces and feeding them into a simulation model. Classical models have recently proved inadequate due to the discovery of self-similarity (fractal behaviour) in data traffic. This paper attempts to systematise the mathematical background of self-similarity and the ways it manifests itself in stochastic processes modelling data traffic. Relevance of self-similarity to traffic description and measurements is discussed. Results of a research effort at the Department of Marine Electronics of the Maritime Institute in Gdansk are described, which focus on the development of a software tool for detection and quantification of self- similarity in observed or synthetically generated data traffic.