• Journal of Internet Computing and Services
• /
• v.19 no.1
• /
• pp.141-154
• /
• 2018

As the sharing economic market grows, the subject of sharing is expanding to intangible resources such as time, experience, and expertise. The phenomenon that an individual temporarily participates in the platform by utilizing own intangible resources is defined as a 'gig economy'. The gig economy has a positive expectation that can create new jobs, but also has negative warnings that it is only a temporary job based on low wages. The gig economy market is growing rapidly in Korea, however there are very few academic discussions. This study examines the experiences of resource providers in the Korean gig economic platform, and then explores future job changes based on the perception of resource providers. This study selected the subject of research as a talent sharing platform and conducted in-depth interviews with 16 resource providers. The results of the research were presented through content analysis, and their experiences and perceptions were classified into six themes. This study implies academic and practical significance in exploring in depth the overall experience of resource suppliers and in suggesting proposals for desirable successful market growth.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.6
• /
• pp.199-205
• /
• 2014

We would like to study and develop the optimized WBS(Work Breakdown Structure) considering project's range, schedule, cost and human resources management with defining phased work in main task and preceding and escorting relationships for effective small and medium enterprises' informatization through standardized PSDM(Production System Development Methodology). For more systemic and effective system built-up, it introduces production information with WBS study which is appropriate for the small and medium enterprises with setting up management items for enhancing system's reliability, quality, productivity, smooth communication among the participated enterprises such as the small and medium enterprises, IT enterprises, and supervision enterprises, and operational support including maintenance of the built system. This study is purpose of helping a lot of the small and medium enterprises and IT enterprises to build up the systems more effective and reliable within planned schedule using the standardized methodology.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.6
• /
• pp.19-23
• /
• 2014

Universities in Korea are exerting every effort to make great strides through restructuring at the moment. Reconstruction accompanied by readjusting the quota and merging of academic departments inevitably require rearrangement of resources and assets of those universities. This paper presents a management method with respect to the spaces held by a university based on the public concept of space through which a system has been implemented as an example. Such attempt makes it possible for the university to adequately cope with changes in its business environment through its effective management with regards to the available spaces on campus. Spaces are all the members' shared public assets. If there is a demand for a space, the university should be able to meet the demand. If the use of a space expires, the university should be able to retrieve and reallocate it. The space classification system presented in this paper may play a huge role in fixating space chargeback system.

• Journal of Internet Computing and Services
• /
• v.18 no.4
• /
• pp.121-131
• /
• 2017

With emergence of Internet, social media, and mobile service, the consumers have actively presented their opinions and sentiment, and then it is spreading out real time as well. The user-generated text data on the Internet and social media is not only the communication text among the users but also the valuable resource to be analyzed for knowing the users' intent and sentiment. In special, economic participants have strongly asked that the social big data and its' analytics supports to recognize and forecast the economic trend in future. In this regard, the governments and the businesses are trying to apply the social big data into making the social and economic solutions. Therefore, this study aims to reveal the capability of social big data analysis for the economic use. The research proposed a social big data analysis model and an online consumer sentiment index. To test the model and index, the researchers developed an economic survey ontology, defined a sentiment dictionary for sentiment analysis, conducted classification and sentiment analysis, and calculated the online consumer sentiment index. In addition, the online consumer sentiment index was compared and validated with the composite consumer survey index of the Bank of Korea.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.12 no.3
• /
• pp.75-86
• /
• 2017

The development of web technologies and the generalization of smartphones have dramatically increased the number of social media users using the Internet. As a result, companies are perceived social media as a major marketing tool and operate a variety of SNS channels. In particular, start-ups conducting businesses with limited resources, social media is being used as an effective marketing tool to meet many potential customers at a low cost. Among them, facebook is the most used channel in the world and plays an important promotional tool not only in overseas but also in marketing activities of domestic start-ups. The purpose of this study is to analyze the relationship between satisfaction and purchase intention according to four personal characteristics of users who use social media contents and to measure the mediating effect of satisfaction on the relationship between content type and purchase intention. To this end, we classified into three types based on the previous research, and social media content is provided to 200 fans of Minbak Danawa(Minda), one of representative start-ups related to accommodation, The questionnaires were conducted for 3 weeks, and a total of 145 copies were collected. All the collected questionnaires were used for statistical analysis through SPSS 18.0. The empirical results show that all three types of content, such as task-oriented, self-oriented, and interaction-oriented, have a significant effect on the satisfaction level. Among them, it is confirmed that the satisfaction level plays a mediating role on the relationship between task-oriented contents and purchase intention. And the user 's personal characteristics showed a partially moderate effect on the satisfaction according to the content type. Therefore, social media content provided by corporations has an important effect on consumer satisfaction and purchasing, in order for start-up to prevail in the market, it is necessary to have an operational strategy to communicate with customers continuously through systematic contents analysis and planning. The result of this study suggests effective ways to build a social media marketing strategy for start-ups and suggests ways to utilize contents considering the characteristics of internet users.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.71-84
• /
• 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.