Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.
This study investigated research trends of JDS that KODISA published and gave implications to elevate quality of scholarly journals. In other words, the study classified scientific system of distribution area to investigate research trends and to compare it with other scholarly journals of distribution and to give implications for higher level of JDS. KODISA published JDS Vol.1 No.1 for the first time in 1999 followed by Vol.8 No.3 in September 2010 to show 109 theses in total. KODISA investigated subjects, research institutions, number of participants, methodology, frequency of theses in both the Korean language and English, frequency of participation of not only the Koreans but also foreigners and use of references, etc. And, the study investigated JDR of KODIA, JKDM(The Journal of Korean Distribution & Management) and JDA that researched distribution, so that it found out development ways. To investigate research trends of JDS that KODISA publishes, main category was made based on the national science and technology standard classification system of MEST (Ministry Of Education, Science And Technology), table of classification of research areas of NRF(National Research Foundation of Korea), research classification system of both KOREADIMA and KLRA(Korea Logistics Research Association) and distribution science and others that KODISA is looking for, and distribution economy area was divided into general distribution, distribution economy, distribution, distribution information and others, and distribution management was divided into distribution management, marketing, MD and purchasing, consumer behavior and others. The findings were as follow: Firstly, main category occupied 47 theses (43.1%) of distribution economy and 62 theses (56.9%) of distribution management among 109 theses in total. Active research area of distribution economy consisted of 14 theses (12.8%) of distribution information and 9 theses (8.3%) of distribution economy to research distribution as well as distribution information positively every year. The distribution management consisted of 25 theses (22.9%) of distribution management and 20 theses (18.3%) of marketing, These days, research on distribution management, marketing, distribution, distribution information and others is increasing. Secondly, researchers published theses as follow: 55 theses (50.5%) by professor by himself or herself, 12 theses (11.0%) of joint research by professors and businesses, Professors/students published 9 theses (8.3%) followed by 5 theses (4.6%) of researchers, 5 theses (4.6%) of businesses, 4 theses (3.7%) of professors, researchers and businesses and 2 theses (1.8%) of students. Professors published theses less, while businesses, research institutions and graduate school students did more continuously. The number of researchers occupied single researcher (43 theses, 39.5%), two researchers (42 theses, 38.5%) and three researchers or more (24 theses, 22.0%). Thirdly, professors published theses the most at most of areas. Researchers of main category of distribution economy consisted of professors (25 theses, 53.2%), professors and businesses (7 theses, 14.9%), professors and businesses (7 theses, 14.9%), professors and researchers (6 theses, 12.8%) and professors and students (3 theses, 6.3%). And, researchers of main category of distribution management consisted of professors (30 theses, 48.4%), professors and businesses (10 theses, 16.1%), and professors and researchers as well as professors and students (6 theses, 9.7%). Researchers of distribution management consisted of professors, professors and businesses, professors and researchers, researchers and businesses, etc to have various types. Professors mainly researched marketing, MD and purchasing, and consumer behavior, etc to demand active participation of businesses and researchers. Fourthly, research methodology was: Literature research occupied 45 theses (41.3%) the most followed by empirical research based on questionnaire survey (44 theses, 40.4%). General distribution, distribution economy, distribution and distribution management, etc mostly adopted literature research, while marketing did empirical research based on questionnaire survey the most. Fifthly, theses in the Korean language occupied 92.7% (101 theses), while those in English did 7.3% (8 theses). No more than one thesis in English was published until 2006, and 7 theses (11.9%) were published after 2007 to increase. The theses in English were published more to be affirmative. Foreigner researcher published one thesis (0.9%) and both Korean researchers and foreigner researchers jointly published two theses (1.8%) to have very much low participation of foreigner researchers. Sixthly, one thesis of JDS had 27.5 references in average that consisted of 11.1 local references and 16.4 foreign references. And, cited times was 0.4 thesis in average to be low. The distribution economy cited 24.2 references in average (9.4 local references and 14.8 foreign references and JDS had 0.6 cited reference. The distribution management had 30.0 references in average (12.1 local references and 17.9 foreign references) and had 0.3 reference of JDS itself. Seventhly, similar type of scholarly journal had theses in the Korean language and English: JDR( Journal of Distribution Research) of KODIA(Korea Distribution Association) published 92 theses in the Korean language (96.8%) and 3 theses in English (3.2%), that is to say, 95 theses in total. JKDM of KOREADIMA published 132 theses in total that consisted of 93 theses in the Korean language (70.5%) and 39 theses in English (29.5%). Since 2008, JKDM has published scholarly journal in English one time every year. JDS published 52 theses in the Korean language (88.1%) and 7 theses in English (11.9%), that is to say, 59 theses in total. Sixthly, similar type of scholarly journals and research methodology were: JDR's research methodology had 65 empirical researches based on questionnaire survey (68.4%), followed by 17 literature researches (17.9%) and 11 quantitative analyses (11.6%). JKDM made use of various kinds of research methodologies to have 60 questionnaire surveys (45.5%), followed by 40 literature researches (30.3%), 21 quantitative analyses (15.9%), 6 system analyses (4.5%) and 5 case studies (3.8%). And, JDS made use of 30 questionnaire surveys (50.8%), followed by 15 literature researches (25.4%), 7 case studies (11.9%) and 6 quantitative analyses (10.2%). Ninthly, similar types of scholarly journals and Korean researchers and foreigner researchers were: JDR published 93 theses (97.8%) by Korean researchers except for 1 thesis by foreigner researcher and 1 thesis by joint research of the Korean researchers and foreigner researchers. And, JKDM had no foreigner research and 13 theses (9.8%) by joint research of the Korean researchers and foreigner researchers to have more foreigner researchers as well as researchers in foreign countries than similar types of scholarly journals had. And, JDS published 56 theses (94.9%) of the Korean researchers, one thesis (1.7%) of foreigner researcher only, and 2 theses (3.4%) of joint research of both the Koreans and foreigners. Tenthly, similar type of scholarly journals and reference had citation: JDR had 42.5 literatures in average that consisted of 10.9 local literatures (25.7%) and 31.6 foreign literatures (74.3%), and cited times accounted for 1.1 thesis to decrease. JKDM cited 10.5 Korean literatures (36.3%) and 18.4 foreign literatures (63.7%), and number of self-cited literature was no more than 1.1. Number of cited times accounted for 2.9 literatures in 2008 and then decreased continuously since then. JDS cited 26,8 references in average that consisted of 10.9 local references (40.7%) and 15.9 foreign references (59.3%), and number of self-cited accounted for 0.2 reference until 2009, and it increased to be 2.1 references in 2010. The author gives implications based on JDS research trends and investigation on similar type of scholarly journals as follow: Firstly, JDS shall actively invite foreign contributors to prepare for SSCI. Secondly, ratio of theses in English shall increase greatly. Thirdly, various kinds of research methodology shall be accepted to elevate quality of scholarly journals. Fourthly, to increase cited times, Google and other web retrievals shall be reinforced to supply scholarly journals to foreign countries more. Local scholarly journals can be worldwide scholarly journal enough to be acknowledged even in foreign countries by improving the implications above.
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.