• Title, Summary, Keyword: Rule Based

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Active Rule Manager for the Mobile Agent Middleware System

  • Lee, Yon-Sik;Cheon, Eun-Hong
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.10
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    • pp.99-105
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    • 2016
  • The active rule system is a key element of the rule-based mobile agent middleware system for activeness and autonomy of the sensor network. The rule manager, which is the main components of active rule based mobile agent framework and active rule system, performs the control and management of the rule-related processes. In this paper, we design and implement the roles and functions of the rule manager in detail. The proposed rule manager plays an important role in the sensor network environment. The sensor data server loads the active rule on the mobile agent by the rule manager according to the situations, and the mobile agent migrates to the destination node and performs the designated action. This active rule-based mobile agent middleware system presents the usefulness for the various sensor network applications. Through the rule execution experiment using the rule-based mobile agent, we show the adaptability and applicability of rule-based mobile agent middleware system to the dynamic environmental changes in sensor networks.

Extraction of Expert Knowledge Based on Hybrid Data Mining Mechanism (하이브리드 데이터마이닝 메커니즘에 기반한 전문가 지식 추출)

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.764-770
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    • 2004
  • This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

A Compiler Based Rule Engine for Developing Changeable Component (가변적인 컴포넌트 개발을 위한 컴파일러 방식의 룰 엔진)

  • Lee, Yong-Hwan
    • Journal of KIISE:Computing Practices and Letters
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    • v.12 no.6
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    • pp.379-385
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    • 2006
  • To improve reusability and adaptation of variable components, rule-based component development has been used. Rule engines usually need additional script languages for rule expression and have difficulty in expressing complex business rules. In this paper, we propose a compiler-based rule engine for rich rule expression and improving performance. This rule engine uses Java programming language to express conditions and action parts of rules and that it can easily express complex business rules. It creates and executes condition and action objects at run time. In view of Performance, the rule engine is better than a script based rule engine. According to our experiments, our compiler-based nile engine shows 2.5 times better performance that script-based JSR 94 rule engine.

The method of using database technology to process rules of Rule-Based System

  • Zheng, Baowei;Yeo, Jeong-Mo
    • Journal of information and communication convergence engineering
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    • v.8 no.1
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    • pp.89-94
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    • 2010
  • The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

Rule-based Named Entity (NE) Recognition from Speech (음성 자료에 대한 규칙 기반 Named Entity 인식)

  • Kim Ji-Hwan
    • MALSORI
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    • no.58
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    • pp.45-66
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    • 2006
  • In this paper, a rule-based (transformation-based) NE recognition system is proposed. This system uses Brill's rule inference approach. The performance of the rule-based system and IdentiFinder, one of most successful stochastic systems, are compared. In the baseline case (no punctuation and no capitalisation), both systems show almost equal performance. They also have similar performance in the case of additional information such as punctuation, capitalisation and name lists. The performances of both systems degrade linearly with the number of speech recognition errors, and their rates of degradation are almost equal. These results show that automatic rule inference is a viable alternative to the HMM-based approach to NE recognition, but it retains the advantages of a rule-based approach.

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Prediction of User Preferred Cosmetic Brand Based on Unified Fuzzy Rule Inference

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.271-275
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    • 2005
  • In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this Purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between $0\∼1$. Second, RDB and SQL(Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS(Knowledge Management Systems)

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The syllable recovrey rule-based system and the application of a morphological analysis method for the post-processing of a continuous speech recognition (연속음성인식 후처리를 위한 음절 복원 rule-based 시스템과 형태소분석기법의 적용)

  • 박미성;김미진;김계성;최재혁;이상조
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.3
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    • pp.47-56
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    • 1999
  • Various phonological alteration occurs when we pronounce continuously in korean. This phonological alteration is one of the major reasons which make the speech recognition of korean difficult. This paper presents a rule-based system which converts a speech recognition character string to a text-based character string. The recovery results are morphologically analyzed and only a correct text string is generated. Recovery is executed according to four kinds of rules, i.e., a syllable boundary final-consonant initial-consonant recovery rule, a vowel-process recovery rule, a last syllable final-consonant recovery rule and a monosyllable process rule. We use a x-clustering information for an efficient recovery and use a postfix-syllable frequency information for restricting recovery candidates to enter morphological analyzer. Because this system is a rule-based system, it doesn't necessitate a large pronouncing dictionary or a phoneme dictionary and the advantage of this system is that we can use the being text based morphological analyzer.

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The Rule Case Simplification Algorithm to be used in a Rule-Based System (규칙기반 시스템에 사용되는 규칙 간소화 알고리즘)

  • Zheng, Baowei;Yeo, Jeong-Mo
    • The KIPS Transactions:PartD
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    • v.17D no.6
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    • pp.405-414
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    • 2010
  • A rule is defined as a case to determine the target values according to combination of various Business factors. The information system is used to represent enterprise's business, which includes and implements the amount of these rules to Rule-Based System. A Rule-Based System can be constructed by using the rules engine method or Relational Database technology. Because the rules engine method has some disadvantages, the Rule-Based System is mostly developed with Relational Database technology. When business scales become larger and more complex, a large number of various rule cases must be operated in system, and processing these rule cases requires additional time, overhead and storage space, and the speed of execution slows down. To solve these problems, we propose a simplification algorithm that converts a large amount of rule cases to simplification rule cases with same effects. The proposed algorithm is applied to hypothetical business rule data and a large number of simplification experiments and tests are conducted. The final results proved that the number of rows can be reduced to some extent. The proposed algorithm can be used to simplify business rule data for improving performance of the Rule-Based System implemented with the Relational Database.

A Study on the Hybrid Data Mining Mechanism Based on Association Rules and Fuzzy Neural Networks (연관규칙과 퍼지 인공신경망에 기반한 하이브리드 데이터마이닝 메커니즘에 관한 연구)

  • Kim Jin Sung
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.884-888
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    • 2003
  • In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

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Prediction of User's Preference by using Fuzzy Rule & RDB Inference: A Cosmetic Brand Selection

  • Kim, Jin-Sung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.4
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    • pp.353-359
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    • 2005
  • In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).