• Archives of design research
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• v.19 no.2 s.64
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• pp.31-42
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• 2006

This study originates from the following critical mind; what will the office of the future be like? and what technology will we rely upon most to communicate with colleagues or to access business information. In the office environment today, new technology has compelled new work paradigm and has greatly affected the capabilities of the individual to work in a more productive and efficient manner. However, even though new computer technology has changed the business world so rapidly, it is very difficult to see the changes that have been taken place. As an aim of the study, creating a mobile tool for office workers that successfully supports their work and communication was explored, and this study explored future work environment with a 5 years technological and social perspective. As a result of this study, the bON brings new visions to the mobile professionals via various interfaces. The bON, a mobile device, is both a system of work and of communication for office workers. The bON, as an integrated tool for working and communicating, forms the basis for a mobile information gateway that is equally capable of functioning as a mobile desk. The basic underlying idea is that all formal meeting places and hallways in the office are equipped with large wall-mounted screens. The bON collaborates with these media in various ways to enhance productivity and efficiency. The main challenge for the bON to enhance both mobility and quality of information is using new technology including bendable and flexible display and soft material display and sensors. To answer for the strong needs for mobility, the whole size of the device is fairly small while the screen is rolled inside the device. For Graphical User Interface, moreover, a new technique called Multi-layering Interface was adopted to stretch user's visual limits and suggests new direction in designing mobile device, equipped with small size display.

• Information Systems Review
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• v.21 no.4
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• pp.143-156
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• 2019

Types of innovation can be categorized into simplification, information, automation, and intelligence. Intelligence is the highest level of innovation, and RPA can be seen as one of intelligence. Robotic Process Automation(RPA), a software robot with artificial intelligence, is an example of intelligence that is suited for simple, repetitive, large-scale transaction processing tasks. The RPA, which is already in operation in many companies in Korea, shows what needs to be done to naturally focus on the core tasks in a situation where the need for a strong organizational culture is increasing and the emphasis is on voluntary leadership, strong teamwork and execution, and a professional working culture. The introduction was considered naturally according to the need to find. Robotic Process Automation, or RPA, is a technology that replaces human tasks with the goal of quickly and efficiently handling structural tasks. RPA is implemented through software robots that mimic humans using software such as ERP systems or productivity tools. RPA robots are software installed on a computer and are called robots by the principle of operation. RPA is integrated throughout the IT system through the front end, unlike traditional software that communicates with other IT systems through the back end. In practice, this means that software robots use IT systems in the same way as humans, repeat the correct steps, and respond to events on the computer screen instead of communicating with the system's application programming interface(API). Designing software that mimics humans to communicate with other software can be less intuitive, but there are many advantages to this approach. First, you can integrate RPA with virtually any software you use, regardless of your openness to third-party applications. Many enterprise IT systems are proprietary because they do not have many common APIs, and their ability to communicate with other systems is severely limited, but RPA solves this problem. Second, RPA can be implemented in a very short time. Traditional software development methods, such as enterprise software integration, are relatively time consuming, but RPAs can be implemented in a relatively short period of two to four weeks. Third, automated processes through software robots can be easily modified by system users. While traditional approaches require advanced coding techniques to drastically modify how they work, RPA can be instructed by modifying relatively simple logical statements, or by modifying screen captures or graphical process charts of human-run processes. This makes RPA very versatile and flexible. This RPA is a good example of the application of digital to intelligence(D2I).

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.