Design and Implementation of MongoDB-based Unstructured Log Processing System over Cloud Computing Environment (클라우드 환경에서 MongoDB 기반의 비정형 로그 처리 시스템 설계 및 구현)
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- Journal of Internet Computing and Services
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- v.14 no.6
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- pp.71-84
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- 2013
Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.
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.
I. Fffects of nitrogen supplying level and culture condition on the top growth aod tubers formation of Ipomoea Batatas. 1) The low level nitrogen (A plot) 3 Milliequivalent per liter of nutrient solution stimulated tuber formation while the high level nitrogen (
Previous studies of reward programs have generally focused on designing the best programs for consumers and suggested that consumers' perception of the value of reward programs can vary according to the type of reward program (e.g., hedonic vs. utilitarian and direct vs. indirect) and its timing (e.g., immediate vs. delayed). These studies have typically assumed that consumers' preference for reward programs has a positive effect on brand loyalty. However, Dowling and Uncles (1997) pointed out that this preference does not necessarily foster brand loyalty. In this regard, the present study verifies this assumption by examining the effects of consumers' perception of the value of reward programs on their brand loyalty. Although reward programs are widely used by online shopping malls, most studies have examined the conditions under which consumers are most likely to value loyalty programs in the context of offline shopping. In the context of online shopping, however, consumers' preferences may have little effect on their brand loyalty because they have more opportunities for comparing diverse reward programs offered by many online shopping malls. That is, in online shopping, finding attractive reward programs may require little effort on the part of consumers, who are likely to switch to other online shopping malls. Accordingly, this study empirically examines whether consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. Meanwhile, consumers seek utilitarian and/or hedonic value from their online shopping activity(Jones et al., 2006; Barbin et al., 1994). They visit online shopping malls to buy something necessary (utilitarian value) and/or enjoy the process of shopping itself (hedonic value). In this sense, reward programs may reinforce utilitarian as well as hedonic value, and their effect may vary according to the type of reward (utilitarian vs. hedonic). According to Chaudhuri and Holbrook (2001), consumers' perception of the value of a brand can influence their brand loyalty through brand trust and affect. Utilitarian value influences brand loyalty through brand trust, whereas hedonic value influences it through brand affect. This indicates that the effect of this perception on brand trust or affect may be moderated by the type of reward program. Specifically, this perception may have a greater effect on brand trust for utilitarian reward programs than for hedonic ones, whereas the opposite may be true for brand affect. Given the above discussion, the present study is conducted with three objectives in order to provide practical implications for online shopping malls to strategically use reward program for establishing profitable relationship with customers. First, the present study examines whether reward programs can be an effective marketing tool for increasing brand loyalty in the context of online shopping. Second, it investigates the paths through which consumers' perception of the value of reward programs influences their brand loyalty. Third, it analyzes the effects of this perception on brand trust and affect by considering the type of reward program as a moderator. This study suggests and empirically analyzes a new research model for examining how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. The model postulates the following 10 hypotheses about the structural relationships between five constructs: (H1) Consumers' perception of the value of reward programs has a positive effect on their program loyalty; (H2) Program loyalty has a positive effect on brand loyalty; (H3) Consumers' perception of the value of reward programs has a positive effect on their brand trust; (H4) Consumers' perception of the value of reward programs has a positive effect on their brand affect; (H5) Brand trust has a positive effect on program loyalty; (H6) Brand affect has a positive effect on program loyalty; (H7) Brand trust has a positive effect on brand loyalty; (H8) Brand affect has a positive effect on brand loyalty; (H9) Consumers' perception of the value of reward programs is more likely to influence their brand trust for utilitarian reward programs than for hedonic ones; and (H10) Consumers' perception of the value of reward programs is more likely to influence their brand affect for hedonic reward programs than for utilitarian ones. To test the hypotheses, we considered a sample of 220 undergraduate students in Korea (male:113). We randomly assigned these participants to one of two groups based on the type of reward program (utilitarian: transportation card, hedonic: movie ticket). We instructed the participants to imagine that they were offered these reward programs while visiting an online shopping mall. We then asked them to answer some questions about their perception of the value of the reward programs, program loyalty, brand loyalty, brand trust, and brand affect, in that order. We also asked some questions about their demographic backgrounds and then debriefed them. We employed the structural equation modeling (SEM) method with AMOS 18.0. The results provide support for some hypotheses (H1, H3, H4, H7, H8, and H9) while providing no support for others (H2, H5, H6, H10) (see Figure 1). Noteworthy is that the path proposed by previous studies, "value perception → program loyalty → brand loyalty," was not significant in the context of online shopping, whereas this study's proposed path, "value perception → brand trust/brand affect → brand loyalty," was significant. In addition, the results indicate that the type of reward program moderated the relationship between consumers' value perception and brand trust but not the relationship between their value perception and brand affect. These results have some important implications. First, this study is one of the first to examine how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. In particular, the results indicate that the proposed path, "value perception → brand trust/brand affect → brand loyalty," can better explain the effects of reward programs on brand loyalty than existing paths. Furthermore, these results suggest that online shopping malls should place greater emphasis on the type of reward program when devising reward programs. To foster brand loyalty, they should reinforce the type of shopping value that consumers emphasize by providing them with appropriate reward programs. If consumers prefer utilitarian value to hedonic value, then online shopping malls should offer utilitarian reward programs and vice versa.
With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.