• Journal of the Korean Geographical Society
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• v.48 no.5
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• pp.667-685
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• 2013

This paper provides an empirical analysis of the effects of trust determinants on firm performance in the buyer-supplier relationships by considering warehousing firms in Busan, South Korea. We employed AVAS transformation regressions to address the limitations of linear regressions and found nonlinear relationships between firm performance and trust determinants. Specifically, "longterm and repeated interactions," "geographical proximity," and "cultures and norms of firms and formal institutions" had positive linear relationships with firm performance, and "information sharing and reciprocity" induced an increasing pattern in firm performance. Finally, "interdependence and asset specificity" and "uncertainty removal" led to a decreasing pattern in firm performance. These results suggest that the relationship between trust and firm performance is contingent on the trust determinants that are important source of trust in buyer-supplier relationships and the influence of trust determinants on firm performance varied according to their levels.

• Journal of Korea Port Economic Association
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• v.34 no.2
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• pp.51-68
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• 2018

This paper compares the differences in management performance in the logistics market and analyzes the differences in business characteristics depending on the industry types. In addition, the effects of industry types and business characteristics on management performance are examined. The analysis method used is ANOVA and K-means clustering. The implication of the study are as follows. First, in the logistics market in Japan, there was a difference in management performance among the types of industry. The warehousing service type had the highest profitability and stability among all the industry types. Second, differences in business characteristics by industry types were tested. It was found that offshore cargo transportation type was more capital intensive than the other types. In addition, warehousing service type had higher business leadership and credit transaction than others. Third, industry types and clusters based on business characteristics had a significant impact on management performance through interaction effects. For the profitability variables in detail, other clusters had a significant effect between transportation types(onshore and offshore cargo) and warehousing service type. On the other hand, in stability variables, one cluster was effective in all types, which is a characteristic that lowers both capital intensity and business leadership.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.28-28
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• 1993

• IE interfaces
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• v.16 no.2
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• pp.229-239
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• 2003

The On-Line Analytical Processing (OLAP) tools access data from the data warehouse for complex data analysis, such as multidimensional data analysis, and decision support activities. Current research has lead to new developments in all aspects of data warehousing, however, there are still a number of problems that need to be solved for making data warehousing effective. View maintenance, one of them, is to maintain view in response to updates in source data. Keeping the view consistent with updates to the base relations, however, can be expensive, since it may involve querying external sources where the base relations reside. In order to reduce maintenance costs, it is possible to maintain the views using information that is strictly local to the data warehouse. This process is usually referred to as "self-maintenance of views". A number of algorithm have been proposed for self maintenance of views where they keep some additional information in data warehouse in the form of auxiliary views. But those algorithms did not consider a consistency of materialized views using view self-maintenance. The purpose of this paper is to research consistency problem when self-maintenance of views is implemented. The proposed "conveyor algorithm" will resolved a complete consistency of materialized view using self-maintenance with considering network delay. The rationale for conveyor algorithm and performance characteristics are described in detail.

• ETRI Journal
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• v.22 no.3
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• pp.41-51
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• 2000

An important use of data warehousing is to provide temporal views over the history of source data. It is significant that nearly all data warehouses are dependent on relational database technology, yet relational databases provide little or no real support for temporal data. Therefore, in is difficult to obtain accurate information for time-varying data. In this paper, we are going to design a temporal data warehouse to support time-varying data efficiently. For this purpose, we present a method to support temporal query by combining a temporal query process layer with the relational database which is used as a source database in an existing data warehouse. We introduce the Temporal Aggregate Tree Strategy (TATS), and suggest its algorithm for the way to aggregate the time-varying data that is changed by the time when the temporal view is created. In addition, The TATS and the materialized view creation method of the existing data warehouse have been evaluated. As a result, the TATS reduces the size of the fact table and it shows a good performance for the comparison factor in case of processing the query for time-varying data.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.1-20
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• 2013

Over the past generation, data warehousing and online analytical processing (OLAP) applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional relational DBMSs (ROLAP). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP's table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems. In this paper, we describe a storage and indexing framework that aims to provide both MOLAP like performance and ROLAP like scalability by essentially combining some of the best features from both. Based upon a combination of R-trees and bitmap indexes, the storage engine has been integrated with a robust OLAP query engine prototype that is able to fully exploit the efficiency of the proposed storage model. Specifically, it utilizes an OLAP algebra coupled with a domain specific query optimizer, to map user queries directly to the storage and indexing framework. Experimental results demonstrate that not only does the design improve upon more naive approaches, but that it does indeed offer the potential to optimize both query performance and scalability.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1772-1780
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• 2016

NCS development for the treatment of waste oils from ship was carried out through steps such as analysis on characteristics, development of competency standard, utilizing package, and validation of industry sites. The results were as follows. Firstly, duty competency was classified as levels from 2 to 6. Educational training institutions were followed by 75 graduate schools, 73 universities, 54 colleges, and 37 high schools. Secondly, developed standards were consisted of duty and competency unit. The name of duty was the treatment of waste oils from ship and competency units were consisted of 8 items as classification of waste oils from ship, pickup and transport of waste oils from ship, warehousing of waste oils from ship to marine disposal company, transport of waste oils from ship to land, warehousing of waste oils from to disposal company, determination of disposal method and plant recycling treatment, and incineration treatment. 28 competency unit elements below 8 competency units were developed. Thirdly, utilizing package was developed into 3 areas of life-long career path, training criteria, and guidelines for exam according to national competency standards in order to develop development of labor's career and perform personal management such as hiring and promotion in industry sites.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.125-160
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• 2007

Data Warehousing and OLAP (On-Line Analytical Processing) have turned into the key technology for comprehensive data analysis. Originally developed for the needs of decision support in business, data warehouses have proven to be an adequate solution for a variety of non-business applications and domains, such as government, research, and medicine. Analytical power of the OLAP technology comes from its underlying multidimensional data model, which allows users to see data from different perspectives. However, this model displays a number of deficiencies when applied to non-conventional scenarios and analysis tasks. This paper presents an attempt to systematically summarize various extensions of the original multidimensional data model that have been proposed by researchers and practitioners in the recent years. Presented concepts are arranged into a formal classification consisting of fact types, factual and fact-dimensional relationships, and dimension types, supplied with explanatory examples from real-world usage scenarios. Both the static elements of the model, such as types of fact and dimension hierarchy schemes, and dynamic features, such as support for advanced operators and derived elements. We also propose a semantically rich graphical notation called X-DFM that extends the popular Dimensional Fact Model by refining and modifying the set of constructs as to make it coherent with the formal model. An evaluation of our framework against a set of common modeling requirements summarizes the contribution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.532-534
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• 2022

At a time when the 5G era is advancing beyond commercialization, places that used to handle simple logistics warehouse tasks are transforming into smart logistics warehouses by combining IT convergence technology and platforms. Smart logistics warehouses can accurately predict demand and inventory of products with AI, deep learning, and robot technologies based on 5G, and provide information on warehousing and warehousing status in real time. As the e-commerce market grows, the smart logistics sector is also growing rapidly. This paper implements a smart logistics warehouse system and studies and proposes a method of establishing a fast and accurate logistics system by utilizing 5G-based Logistics Automation Robot.

• Korea Trade Review
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• v.46 no.4
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• pp.149-166
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• 2021

This paper aims to reduce the frequency of dangerous goods storage accidents in China. Advocating the managers of warehousing and logistics enterprises to pay attention to the operation process of dangerous goods warehousing business. Improving the safe storage and management capabilities of dangerous goods warehouses. This article first collects official data on dangerous goods storage accidents in China and conducts a general statistical analysis of the accidents. Based on the results of accident statistics and related literature research on dangerous goods storage management, establish a dangerous goods storage safety management factor system, use the analytic hierarchy process, establish a factor importance questionnaire and implement data collection. Through statistics, this paper finds that the storage accidents of dangerous goods in China in the past ten years mainly occurred in the inbound phase of dangerous goods and the storage phase of dangerous goods warehouses. Through the results of the analytic hierarchy process, it is found that the professionalism of the dangerous goods storage practitioners, the compliance of the practitioners with safety regulations, and the awareness of operational safety are the most important.