• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Applied Biological Chemistry
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• v.10
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• pp.69-100
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• 1968

1. In order to investigate on the microflora and enzyme activity of mold wheat 'Nuruk' , the major source of microorganisms for the brewing of Takju (a Korean Sake), two samples of Nuruk, one prepared at the College of Agriculture, Chung Nam University (S) and the other perchased at a market (T), were taken for the study. The molds, aerobic bacteria, lactic acid bacteria, and yeasts were examined and counted. The yeasts were classified by the treatment with TTC (2, 3, 5 triphenyltetrazolium chloride) agar that yields a varied shade of color. The amylase and protease activities of Nuruk were measured. The results were as the followings. a) In the Nuruk S found were: Aspergillus oryzae group, $204{\times}10^5$; Black Aspergilli, $163{\times}10^5$; Rhizogus, $20{\times}10^5$; Penicillia, $134{\times}10^5$; Areobic bacteria, $9{\times}10^6-2{\times}10^7$; Lactic acid bacteria, $3{\times}10^4$ In the Nuruk T found were: Aspergillus oryzae group, $836{\times}10^5$; Black Aspergilli, $286{\times}10^5$; Rhizopus, $623{\times}10^5$; Penicillia, $264{\times}10^5$; Aerobic bacteria, $5{\times}10^6-9{\times}10^6$; Lactic acid bacteria, $3{\times}10^4$ b) Eighty to ninety percent of the aerobic bacteria in Nuruk S appeared to belong to Bacillus subtilis while about 70% of those in Nuruk T seemed to be spherical bacteria. In both Nuruks about 80% of lactic acid bacteria were observed as spherical ones. c) The population of yeasts in 1g. of Nuruk S was about $6{\times}10^5$, 56.5% of which were TTC pink yeasts, 16% of which were TTC red pink yeasts, 8% of which were TTC red yeasts, 19.5% of which were TTC white yeasts. In Nuruk T(1g) the number of yeasts accounted for $14{\times}10^4$ and constituted of 42% TTC pink. 21% TTC red pink 28% TTC red and 9% TTC white. d) The enzyme activity of 1g Nuruk S was: Liquefying type Amylase, $D^{40}/_{30},=256$ W.V. Saccharifying type Amylase, 43.32 A.U. Acid protease, 181 C.F.U. Alkaline protease, 240C.F.U. The enzyme activity of 1g Nuruk T was: Liquefying type Amylase $D^{40}/_{30},=32$ W.V. Saccharifying type amylase $^{30}34.92$ A.U. Acid protease, 138 C.F.U. Alkaline protease 31 C.F.U. 2. During the fermentation of 'Takju' employing the Nuruks S and T the microflora and enzyme activity throughout the brewing were observed in 12 hour intervals. TTC pink and red yeasts considered to be the major yeasts were isolated and cultured. The strains ($1{\times}10^6/ml$) were added to the mashes S and T in which pH was adjusted to 4.2 and the change of microflora was examined during the fermentation. The results were: a) The molds disappeared from each sample plot since 2 to 3 days after mashing while the population of aerobic bacteria was found to be $10{\times}10^7-35{\times}10^7/ml$ inS plots and $8.2{\times}10^7-12{\times}10^7$ in plots. Among them the coccus propagated substantially until some 30 hours elasped in the S and T plots treated with lactic acid but decreased abruptly thereafter. In the plots of SP. SR. TP. and TR the coccus had not appeared from the beginning while the bacillus showed up and down changes in number and diminished by 1/5-1/10 the original at the end stage. b) The lactic acid bacteria observed in the S plot were about $7.4{\times}10^7$ in number per ml of the mash in 24 hours and increased up to around $2{\times}10^8$ until 3-4 days since. After this period the population decreased rapidly and reached about $4{\times}10^5$ at the end, In the plot T the lactic acid becteria found were about $3{\times}10^8$ at the period of 24 fours, about $3{\times}10$ in 3 days and about $2{\times}10^5$ at the end in number. In the plots SP. SR. TP, and TR the lactic acid bacteria observed were as less as $4{\times}10^5$ at the stage of 24 hours and after this period the organisms either remained unchanged in population or ceased to exist. c) The maiority of lactic acid bacteria found in each mash were spherical and the change in number displayed a tendency in accordance with the amount of lactic acid and alcohol produced in the mash. d) The yeasts had showed a marked propagation since the period of 24 hours when the number was about $2{\times}10^8$ ㎖ mash in the plot S. $4{\times}10^8$ in 48 hours and $5-7{\times}10^8$ in the end period were observed. In the plot T the number was $4{\times}10^8$ in 24 hours and thereafter changed up and down maintaining $2-5{\times}10^8$ in the range. e) Over 90% of the yeasts found in the mashes of S and T plots were TTC pink type while both TTC red pink and TTC red types held range of $2{\times}10-3{\times}10^7$ throughout the entire fermentation. f) The population of TTC pink yeasts in the plot SP was as $5{\times}10^8$ much as that is, twice of that of S plot at the period of 24 hours. The predominance in number continued until the middle and later stages but the order of number became about the same at the end. g) Total number of the yeasts observed in the plot SR showed little difference from that of the plot SP. The TTC red yeasts added appeared considerably in the early stage but days after the change in number was about the same as that of the plot S. In the plot TR the population of TTC red yeasts was predominant over the T plot in the early stage which there was no difference between two plots there after. For this reason even in the plot w hers TTC red yeasts were added TTC pink yeasts were predominant. TTC red yeasts observed in the present experiment showed continuing growth until the later stage but the rate was low. h) In the plot TP TTC pink yeasts were found to be about $5{\times}10^8$ in number at the period of 2 days and inclined to decrease thereafter. Compared with the plot T the number of TTC pink yeasts in the plot TP was predominant until the middle stage but became at the later stage. i) The productivity of alcohol in the mash was measured. The plot where TTC pink yeasts were added showed somewhat better yield in the earely stage but at and after the middle stage the difference between the yeast-added and the intact mashes was not recognizable. And the production of alcohol was not proportional to the total number of yeasts present. j) Activity of the liquefying amylase was the highest until 12 hours after mashing, somewhat lowered once after that, and again increased around 36-48 hours after mashing. Then the activity had decreased continuously. Activity of saccharifying amylase also decreased at the period of 24 hours and then increased until 48 hours when it reached the maximum. Since, the activity had gradually decreased until 72 hours and rapidly so did thereafter. k) Activity of alkaline protease during the fermentation of mash showed a tendency to decrease continusously although somewhat irregular. Activity of acid protease increased until hours at the maximum, then decreased rapidly, and again increased, the vigor of acid protease showed better shape than that of alkaline protease throughout. 3. TTC pink yeasts that were predominant in number, two strains of TTC red pink yeasts that appeared throughout the brewing, and TTC red yeasts were identified and the physiological characters examined. The results were as described below. a) TTC pinkyeasts (B-50P) and two strains of TTC red pink yeasts (B-54 RP & B-60 RP) w ere identified as the type of Saccharomyces cerevisiae and TTC pink red yeasts CB-53 R) were as the type of Hansenula subpelliculosa. b) The fermentability of four strains above mentioned were measured as follows. Two strains of TTC red pink yeasts were the highest, TTC pink yeasts were the lowest in the fermantability. The former three strains were active in the early stage of fermentation and found to be suitable for manufacturing 'Takju' TTC red yeasts were found to play an important role in Takju brewing due to its strong ability to produce esters although its fermentability was low. c) The tolerance against nitrous acid of strains of yeast was marked. That against lactic acid was only 3% in Koji extract, and TTC red yeasts showed somewhat stronger resistance. The tolerance against alcohol of TTC pink and red pink yeasts in the Hayduck solution was 7% while that in the malt extract was 13%. However, that of TTC red yeasts was much weaker than others. Liguefying activity of gelatin by those four strains of yeast was not recognized even in 40 days. 4. Fermentability during Takju brewing was shown in the first two days as much as 70-80% of total fermentation and around 90% of fermentation proceeded in 3-4 days. The main fermentation appeared to be completed during :his period. Productivity of alcohol during Takju brewing was found to be apporximately 65% of the total amount of starch put in mashing. 5. The reason that Saccharomyces coreanuss found be Saito in the mash of Takju was not detected in the present experiment is considered due to the facts that Aspergillus oryzae has been inoculated in the mold wheat (Nuruk) since around 1930 and also that Koji has been used in Takju brewing, consequently causing they complete change in microflora in the Takju brewing. This consideration will be supported by the fact that the original flavor and taste have now been remarkably changed.