In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantag...In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.展开更多
To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. Th...To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.展开更多
[目的]利用数据挖掘技术,了解耳穴压豆治疗小儿遗尿的常见证型及选穴配伍规律,为耳穴压豆干预小儿遗尿提供参考依据。[方法]利用计算机检索建库至2025年1月8日有关耳穴压豆治疗小儿遗尿的文献,检索数据库包括Web of Science、PubMed、Co...[目的]利用数据挖掘技术,了解耳穴压豆治疗小儿遗尿的常见证型及选穴配伍规律,为耳穴压豆干预小儿遗尿提供参考依据。[方法]利用计算机检索建库至2025年1月8日有关耳穴压豆治疗小儿遗尿的文献,检索数据库包括Web of Science、PubMed、Cochrane library、中国知网、中国生物医学文献数据库、维普数据库、万方数据库,对文献中的耳穴数据进行描述性分析及关联规则分析。[结果]最终纳入文献90篇,包括中文88篇、英文2篇。小儿遗尿有4种主要证型:肾气不固型、肺脾气虚型、肝经湿热型、脾肾两虚型。耳穴压豆治疗小儿遗尿共涉及耳穴31个,其中常用耳穴为膀胱、肾、皮质下、缘中、神门。常用的配伍耳穴为膀胱-尿道-缘中-皮质下-肾、肾-神门-缘中-膀胱等。[结论]数据挖掘可有效分析小儿遗尿的常见证型、耳穴压豆治疗小儿遗尿的常用耳穴及其配伍规律,揭示了耳穴压豆辨证施护的特点与选穴的规律性,为临床上科学选穴提供依据,有望推动耳穴压豆技术在小儿遗尿症护理中的精准应用。展开更多
文摘In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.
基金supported by the Key Project of National Defense Basic Research Program of China (No.B1120132031)supported by the Cultivation and Development Program for Technology Innovation Base of Beijing Municipal Science and Technology Commission (No.Z151100001615034)
文摘To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.
文摘[目的]利用数据挖掘技术,了解耳穴压豆治疗小儿遗尿的常见证型及选穴配伍规律,为耳穴压豆干预小儿遗尿提供参考依据。[方法]利用计算机检索建库至2025年1月8日有关耳穴压豆治疗小儿遗尿的文献,检索数据库包括Web of Science、PubMed、Cochrane library、中国知网、中国生物医学文献数据库、维普数据库、万方数据库,对文献中的耳穴数据进行描述性分析及关联规则分析。[结果]最终纳入文献90篇,包括中文88篇、英文2篇。小儿遗尿有4种主要证型:肾气不固型、肺脾气虚型、肝经湿热型、脾肾两虚型。耳穴压豆治疗小儿遗尿共涉及耳穴31个,其中常用耳穴为膀胱、肾、皮质下、缘中、神门。常用的配伍耳穴为膀胱-尿道-缘中-皮质下-肾、肾-神门-缘中-膀胱等。[结论]数据挖掘可有效分析小儿遗尿的常见证型、耳穴压豆治疗小儿遗尿的常用耳穴及其配伍规律,揭示了耳穴压豆辨证施护的特点与选穴的规律性,为临床上科学选穴提供依据,有望推动耳穴压豆技术在小儿遗尿症护理中的精准应用。
