• Journal of KIISE:Databases
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• v.33 no.6
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• pp.600-619
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• 2006

According to recent technical advances on sensors and mobile devices, processing of data streams generated by the devices is becoming an important research issue. The data stream of real values obtained at continuous time points is called streaming time-series. Due to the unique features of streaming time-series that are different from those of traditional time-series, similarity matching problem on the streaming time-series should be solved in a new way. In this paper, we propose an efficient algorithm for streaming time- series matching problem that supports normalization transform. While the existing algorithms compare streaming time-series without any transform, the algorithm proposed in the paper compares them after they are normalization-transformed. The normalization transform is useful for finding time-series that have similar fluctuation trends even though they consist of distant element values. The major contributions of this paper are as follows. (1) By using a theorem presented in the context of subsequence matching that supports normalization transform[4], we propose a simple algorithm for solving the problem. (2) For improving search performance, we extend the simple algorithm to use $k\;({\geq}\;1)$ indexes. (3) For a given k, for achieving optimal search performance of the extended algorithm, we present an approximation method for choosing k window sizes to construct k indexes. (4) Based on the notion of continuity[8] on streaming time-series, we further extend our algorithm so that it can simultaneously obtain the search results for $m\;({\geq}\;1)$ time points from present $t_0$ to a time point $(t_0+m-1)$ in the near future by retrieving the index only once. (5) Through a series of experiments, we compare search performances of the algorithms proposed in this paper, and show their performance trends according to k and m values. To the best of our knowledge, since there has been no algorithm that solves the same problem presented in this paper, we compare search performances of our algorithms with the sequential scan algorithm. The experiment result showed that our algorithms outperformed the sequential scan algorithm by up to 13.2 times. The performances of our algorithms should be more improved, as k is increased.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.2
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• pp.67-75
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• 2012

Purpose: The study is to check the specialty of radiotherapists working in the department of radiation oncology and find job satisfaction, organizational commitment and job commitment having an effect on professional parts. After making analysis of the mutual relation, it is to provide radiotechnologists with making progress in the future. Materials and Methods: From March 2 to March 30, we had carried out a survey with email. It is possible to have 272 questionnaires answered in the survey. We make use of SPSS 13.0 for Windows to analyze the data collected for study. Frequency and a percentage are meant to show general characteristics, and t-test and ANOVA to do the difference between general properties and professionalism. Pearson's correlation coefficient also is meant to do the correlation of professionalism, organizational job commitment and job satisfaction, and multiple regression analysis to do the factor for a relevant variable to affect professionalism. Results: There are subdivisions in the professionalism informing us of the self-regulation $17.74{\pm}2.32/3.55{\pm}.46$, a sense of calling $17.58{\pm}2.63/3.52{\pm}.53$, reference of the professional $17.14{\pm}2.39/3.43{\pm}.48$, service to the public $15.97{\pm}2.48/3.19{\pm}50$, and autonomy $15.68{\pm}2.28/3.14{\pm}46$. Grand mean turns out to be $83.89{\pm}7.63$(Summation of items)/$3.37{\pm}0.49$ (Numbers of items). When it comes to a statistical relation between general characteristics and professionalism, the statistics have it that these come within age (P<.001), period of employment (P<.001), education status (P<.05), a monthly income (P<.001), radiotherapists who get a special license (P<.001), the position (P<.001), and an opportunity for developing (P<.001). As a result of organizational commitment, job commitment, and job satisfaction, grand mean in organizational commitment proves to be $80.10{\pm}8.15/3.34{\pm}.34$. There are subvisions showing affective commitment $28.64{\pm}4.61$/3.58, continuance commitment $27.54{\pm}4.22/3.44{\pm}.53$, and normative commitment $23.95{\pm}2.94/2.99{\pm}.37$ in order of precedence. The average grade in job commitment is $32.47{\pm}5.77/3.30{\pm}.60$ and that in job satisfaction is $63.39{\pm}10.16/3.17{\pm}.51$, respectively. We find the positive relationship between professionalism and organizational commitment (r=.522, P<.05), between professionalism and job commitment (r=.444, P<.05), and between professionalism and job satisfaction (r=.507, P<.05). And we also get the positive relationship between organizational commitment and job commitment (r=.549, P<.05), between organizational commitment and job satisfaction (r=.433, P<.05), and between job commitment and job satisfaction (r=.462, P<.05). To catch the factors influencing the professionalism of radiotherapists, we used multiple regression analysis. According to the final model, it appears affective commitment (B=.755, P<.05), normative commitment (B=.305, P<.05), job satisfaction (B=.092, P<.05), an opportunity for developing (B=-1.505, P<.05), and the position (B=-1.155, P<.05) in order of precedence. It seems that explaining influece on $R^2$ is 0.504. Conclusion: The results of the factors that influence professionalism working as radiotherapists in the department of radiation oncology have it that the more affective commitment, normative commitment, and job satisfaction we feel, the more professionalism we recognize. We think that the focus of professionalism is increased if getting the chances for radiotherapists to have little to do with developing opportunities given.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.