• Knowledge Management Research
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• v.10 no.2
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.6
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• pp.1127-1136
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• 1997

A vector timestamp is used to satisfy message ordering in a group communications. In this paper, we propose a new vector timestamp compression method which is applicable to a single process group environment where one process belongs to only one precess group. An existing compression method compares the fields of the previously sent vector timestamp with thouse of the currently updated vector timestamp, then sends only the modified fields of the vector timestamp. Unlike the previous one, a proposed compression method performs individual compression for each process using the locally maintained vector timestamp information on other processes. Also, we logicallyproved the causal ordering algorithm using the new compression method and compared the performance of the proposed method with one of the previous compression method by computer simulation. Using the proposed compression method, the message overhead required for causal ordering can be reduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.926-938
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• 1996

In this paper, we proposed a new multicast algorithm which is efficiently applicable to overlapped process group environments where one process may be involved in several process groups. Unlike the esisting algorithms, the proposed one provides an efficient group communication mechanism by generating the processgroup information in two-level tree. Using this algorithm, we improved the shortcoming of the existing algorithms by reducing the overhead in passing through unnecessary processes for message ordering. We have provided the causal ordering method as well as the total ordering method in group communication environments. As a result, we allow one process to deliver message to other processes with a short delay time, and reduced the overhead required for the message ordering. Also, we logically proved the proposed causal ordering method, and compared the performance of the proposed algorithm with ones of other existing algorithms by computer simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7A
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• pp.1036-1045
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• 1999

In this paper, we proposed a causal ordering algorithms which is efficiently applicable to overlapped process group environments where one process may belong to several process groups. The ones is proposed to choose with topology of the network. We proposed receiver select algorithm in broadcast network, sender select algorithm in point-to-point network. Each algorithms removes unnecessary vector timestampes to reduce the message overhead required for the causual ordering. And, compressed vector timestamps using the locally maintained vector timestamp information of other processes and other groups to minimize the message overhead. Also, we logically proved the proposed causal ordering method, and compared the performance of the proposed algorithm with ones of other existing algorithms by computer simulation.

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.137-144
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• 2005

Many reliable group communication algorithms were presented to satisfy predetermined message ordering properties in small or medium-scale distributed systems. However, the previous algorithms with their strong reliability properties may be unappropriate for large-scale systems. To address this issue, some epidemic-style group communication algorithms were proposed for considerably improving scalability while guaranteeing the reasonably weaker reliability property than the existing ones. The algorithms are all designed for ensuring the atomic order message delivery property. But, some distributed applications such as multimedia systems and collaborative work, may require only the weaker message ordering property, i.e., causal order delivery. This paper proposes an efficient epidemic-style group communication algorithm ensuring causal order delivery to provide the indigenous scalability of the epidemic-style approach.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.215-221
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• 2011

Communication infrastructure of wireless sensor networks significantly tends to depend on application requirements. Gossip communication is becoming one of the promising solutions for addressing its scalability problems in providing information propagation functionality based on the P (publish)/S (subscribe) paradigm. In particular, despite the importance of both guaranteeing message delivery order required and supporting overlapping groups in sensor networks, there exist little research works on development of gossip-style dissemination protocols to satisfy all these requirements. In this paper, we present the latest causal information transmission protocol between publishers and subscribers for overlapping groups. In this protocol, sensor leaders as publishers might guarantee consistently causally ordered message delivery among themselves by aggregating causality information. On the other hand, only the latest causal information piggybacked on each multicast message is transmitted from publishers to subscribers through gossip-style dissemination. Its scalability feature might be highly suitable for the area of the applications requiring only the minimum meaningful information.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.267-272
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• 2007

In the construction industry government is not only an owner but also a consumer. Accordingly it should develop the construction industry and this role is important. Among them, the law of the ordering method is standards of selecting a successful bidder and have powerful influence over the construction industry and owners. Therefore this study said how the lowest bid method, which has taken effect still more, has influence on the construction industry and the public sector with the Causal Loop Diagram. According to analyzing this construction law, we can grasp the point of the strength and weakness of this law also examine a trend of how the present law is changed. While we studied, we tried to adhere to neutrality and form a new opinion about it because the previous studies were opposed to each other. As a result, to improve the relation between the government and private company, which were opposed to each other about arguments for and against the lowest bid method, this study suggest the method of making better the relation. The final goal of it is researching the inefficient factors and improving the weakness of this law.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.117-140
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• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.29-40
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• 2000

Process planning (selection and ordering of processes) and material selection for product manufacturing are two key things determined before taking full-scale manufacturing. Knowledge on product design. material characteristics, processes, time and cost all-together are mutually related and should be considered concurrently. Due to the complexity of problem, human experts have got only one of the feasilbe solutions with their field knowledge and experiences. We propose a hierarchical expert system framework of knowledge representation and reasoning in order to overcome the complexity. Manufacturing processes have inherently hierarchical relationships, from top level processes to bottom level operation processes. Process plan of one level is posted in process blackboard and used for lower level process planning. Process information on blackboard is also used to adjust the process plan in order to resolve the dead-end or inconsistency situation during reasoning. Decision variables for process, material, tool, time and cost are represented as object frames, and their relationships are represented as constraints and rules. Constraints are for relationship among variables such as compatibility, numerical inequality etc. Rules are for causal relationships among variables to reflect human expert\`s knowledge such as process precedence. CRSP(Constraint and Rule Satisfaction Problem) approach is adopted in order to obtain solution to satisfy both constraints and rules. The trade-off procedure gives user chances to see the impact of change of important variables such as material, cost, time and helps to determine the preferred solution. We developed the prototype system using visual C++ MFC, UNIK, and UNlK-CRSP on PC.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.