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http://dx.doi.org/10.9717/kmms.2018.21.2.211

Unstructured Data Analysis and Multi-pattern Storage Technique for Traffic Information Inference  

Kim, Yonghoon (Dept. of Computer Engineering, Pukyong National University)
Kim, Booil (Dept. of Electrical, Electronics and Software Engineering, Pukyong National University)
Chung, Mokdong (Dept. of Computer Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.2, 2018 , pp. 211-223 More about this Journal
Abstract
To understand the meaning of data is a common goal of research on unstructured data. Among these unstructured data, there are difficulties in analyzing the meaning of unstructured data related to corpus and sentences. In the existing researches, the researchers used LSA to select sentences with the most similar meaning to specific words of the sentences. However, it is problematic to examine many sentences continuously. In order to solve unstructured data classification problem, several search sites are available to classify the frequency of words and to serve to users. In this paper, we propose a method of classifying documents by using the frequency of similar words, and the frequency of non-relevant words to be applied as weights, and storing them in terms of a multi-pattern storage. We use Tensorflow's Softmax to the nearby sentences for machine learning, and utilize it for unstructured data analysis and the inference of traffic information.
Keywords
Unstructured Data; LSA; ITS; Multi-Pattern Storage; Machine Learning; Tensorflow;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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