Isidd: An interactive system for inductive database design

When designing a deductive database, the designer has to decide for each predicate (or relation) whether it should be defined extensionally or intensionally and what the definition should look like. An intelligent interactive system is presented to assist the designer in this task. It starts from an example state ofa database in which all predicates are defined extensionally. It can then compact the database by transforming extensionally defined predicates into intensionally defined ones. These predicates can be chosen by the user or by the system itself. Further compaction is possible by inventing new predicates; this invention is controlled by user-specified templates. The systemalso proposes semantic integrity constraints to the user. These do not lead to extra compaction but can be used to make the database more robust. The intelligent system employs techniques from the area of inductive logic programming.     

基于数据挖掘技术的自适应入侵检测系统模型

为了提高数据库系统的安全,将改进的数据预处理算法和改进的Apriori算法应用于数据库入侵检测系统,提出一个基于数据挖掘技术的自适应的数据库入侵检测系统模型.模型中,针对滥用检测规则生成的局限性,提出将改进算法的中间结果运用到滥用检测规则的生成中,不断完善滥用检测规则库,结合滥用检测和异常检测的特点,先进行滥用检测,再进行异常检测,降低漏检率和误警率.检测结果表明,不断更新规则库,能够提高系统的自适应性.     

Analysis of a virtual memory model for maintaining database views

An analytical model is given for predicting the performance of a new support strategy for database views. This strategy, called the virtual method, is compared with traditional methods for supporting views. The analytical model's predictions of improved performance by the virtual method are then validated by comparing these results with those achieved in an experimental implementation     

一个基于数据挖掘的环境质量预测系统

环境问题愈来愈受到人们的重视，现在积累了大量空气污染数据为空气质量日报预报提供了坚实的基础。大气环境预测系统Atosphere Environment Forecast System(AEFS)是采用数据挖掘技术开发的一个环境质量预报系统。该系统主要运用了粗集理论和在线分析处理技术，取得了较好的效果。     

An intelligent PE-malware detection system based on association mining

The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic/metamorphic and new, previously unseen malicious executables. Data mining methods such as Naive Bayes and Decision Tree have been studied on small collections of executables. In this paper, resting on the analysis of Windows APIs called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of KingSoft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system outperform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques. Our system has already been incorporated into the scanning tool of KingSoft’s Anti-Virus software. A short version of the paper is appeared in [33]. The work is partially supported by NSF IIS-0546280 and an IBM Faculty Research Award. The authors would also like to thank the members in the anti-virus laboratory at KingSoft Corporation for their helpful discussions and suggestions.     

An expert database design system based on analysis of forms

A form model and an expert database system that analyzes instances of the form model to derive a conceptual schema are proposed. The form model describes the properties of form fields such as their origin, hierarchical structure, and cardinality. The expert database design system creates a conceptual schema by incrementally integrating related collections of forms. The rules of the expert systems are divided into six phases form selection; entity identification; attribute attachment; relationship identification; cardinality identification; and integrity constraints. The rules of the first phase use knowledge about the form flow to determine the order in which forms are analyzed. The rules in other phases are used in conjunction with a designer dialog to identify the entities, relationships, and attributes of a schema that represents the collection of forms     

基于XML的在线数据库驱动的虚拟实验室设计与实现

介绍了在线数据库驱动的虚拟实验室模型、场号建模技术及其详细设计和实现,重点论述了如何在VRML和ASP中引入XML（可扩展性描述语言）作为数据的载体,让相关场景实现可以由XML这个平面数据库作为数据源来重构场景,实现了虚拟现实场景数据在不同平台间的易交换性。利用VRML实现的在线虚拟集成电路实验室能使远程学生把网络课堂上学习的理论知识和实验相结合,能给远程学生以逼真的学习体验,使之不受资金、位置、仪器和师资等限制。     

基于关系数据库的关联规则的形式化开采

从研究关联规则概念入手,探讨了项目、项目集合和交易数据库等概念在关系数据库中的具体含义,提出了一种全面的、易于理解和使用的关联规则形式化描述方法,并给出了开采关系型数据库中关联规则的一般过程,这为关系型数据库中关联规则开采系统的设计奠定了基础,对系统设计者有一定的指导意义。     

An agent-based hybrid framework for database mining

While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.     

A novel automatic virtual metrology system architecture for TFT-LCD industry based on main memory database

The semiconductor and thin-film-transistor–liquid-crystal-display (TFT-LCD) industries widely value Automatic Virtual Metrology System (AVMS). AVMS needs to handle a large volume of VM-related data, which may cause poor internal database performance. In general, AVMS adopts efficient but expensive commercial database management systems (DBMSs) to yield good AVMS performance. This usually makes the AVMS construction cost very high. Therefore, the industries require a novel AVMS architecture with lower cost and greater efficiency in database. This paper proposes a novel AVMS architecture based on Main Memory Database (MMDB) technology. Specifically, the MMDB is used to improve the performance bottlenecks of the current Disk Resident Database (DRDB). Also, we design automatic data-backup and automatic data-query sources integration mechanisms to effectively relieve rapidly increased data volume in the original AVMS architecture. In addition, the novel AVMS architecture adopts a free commercial MMDB to significantly reduce total system cost. Integrated testing results show that the proposed AVMS architecture and developed technologies can enable the AVMS to have better data-storage efficiency, superior data-query performance, and lower database cost. The proposed AVMS architecture and research results in this paper can be a useful reference for TFT-LCD manufacturing companies in constructing their own AVM systems. The proposed AVMS architecture can also be applied in the semiconductor and solar-cell industries.     

基于数据挖掘的Snort系统改进模型

针对Snort系统对新的入侵行为无能为力的缺点,设计了一种基于数据挖掘理论的Snort网络入侵检测系统的改进模型。该模型在Snort入侵检测系统的基础上增加了正常行为模式挖掘模块、异常检测引擎模块和新规则生成模块,使得系统具有从新的入侵行为中学习新规则和从正常数据中学习正常行为模式的双重能力。实验结果表明,新模型不仅能够有效地检测到新的入侵行为,降低了Snort系统的漏报率,而且提高了系统的检测效率。     

综采工作面虚拟现实监控系统设计

针对目前综采工作面自动化生产过程中视频监控图像效果差的问题,设计了综采工作面虚拟现实监控系统,详细介绍了系统功能设计、结构组成及系统实现的关键技术。该系统采用虚拟现实技术构建出了高仿真度的虚拟矿井作业场景,实现了综采工作面生产设备实时状态信息的采集、传输、显示、预警以及反向控制等功能,满足了综采工作面自动化生产监控的需求。     

基于FPGA的平面螺旋电感式微位移传感器系统

基于平面螺旋电感和现场可编程门阵列（ FPGA）技术,系统设计了一种以频率为输出的电感式微位移传感器,实现了电感式传感器的数字化输出与检测。系统主要由位移/电感转换模块、电感检测电路、差频模块、FPGA四部分组成,并在FPGA系统中搭建NIOSⅡ软核,对采集所得数据进行处理。实验证明：系统具有灵敏度高,温度稳定性好等特点,其测量范围为0～15 mm,在0～6 mm 范围内,最大误差为0.03 mm,分辨率达到10μm。     

基于中医方剂数据库的Top-Rank-k频繁模式挖掘算法

为降低中医（TCM）方剂频繁模式挖掘过程中对经验参数的依赖,提高挖掘结果的准确性,针对中医方剂的数据特点,提出一种基于带权无向图的Top-Rank-k频繁模式挖掘算法。该算法可以直接挖掘出频繁k-itemset（k≥3）而无需产生1-itemset和2-itemset,并随之快速回溯到核心药物组合的频繁项集所对应的方剂信息;此外,采用一种动态位向量（DBV）的压缩机制对无向图中边的权重进行压缩存储,以有效地提高算法的空间存储效率。分别对中医方剂数据集、真实数据集（Chess、Pumsb和Retail）和合成数据集（T10I4D100K和Test2K50KD1）进行测试和比较,结果表明该算法与iNTK和BTK相比具有更高的时间和空间效率,而且也可以应用于其他类型的数据集。     

An approach to mining the multi-relational imbalanced database

The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multi-relational database. Nevertheless, many data nowadays such as financial transactions and medical anamneses are stored in a multi-relational database rather than a single data sheet. On the other hand, the widely used multi-relational classification approaches, such as TILDE, FOIL and CrossMine, are insensitive to handle the imbalanced databases. In this paper, we propose a multi-relational g-mean decision tree algorithm to solve the imbalanced problem in a multi-relational database. As shown in our experiments, our approach can more accurately mine a multi-relational imbalanced database.     

基于XML的虚拟数据库管理系统

针对分布式数据同步时因为有异构的数据源而造成数据格式不统一,应用层进行同步的时候要进行相当复杂的转换并且维护起来也要耗费相当大的成本,提出了利用XML来统一异构数据源的格式,构建基于XML中间件的虚拟数据库.介绍此系统下的异构数据集成的思想和实现框架,同时详细介绍了该框架提供的服务功能及实现的关键技术.     

基于数据库的专家系统建设

专家系统是人工智能领域中最为活跃的分支 ,也是决策支持的重要工具 ,但是其构建通常依赖于为数较少的专用工具 ,如Prolog、Lisp等 ,本文作者在吸收前一时期国内外众多研究成果的基础上 ,结合自己的工作经验和设想 ,提出了一个使用数据库构建专家系统的方案 ,以飨读者。     

EDUA: An efficient algorithm for dynamic database mining

Maintaining frequent itemsets (patterns) is one of the most important issues faced by the data mining community. While many algorithms for pattern discovery have been developed, relatively little work has been reported on mining dynamic databases, a major area of application in this field. In this paper, a new algorithm, namely the Efficient Dynamic Database Updating Algorithm (EDUA), is designed for mining dynamic databases. It works well when data deletion is carried out in any subset of a database that is partitioned according to the arrival time of the data. A pruning technique is proposed for improving the efficiency of the EDUA algorithm. Extensive experiments are conducted to evaluate the proposed approach and it is demonstrated that the EDUA is efficient.     

Allegro基于MySQL数据库的数据录入系统设计

为了实现通过Allegro软件对PCB板卡数据自动采集、录入、保存等功能,研究基于SKILL语言的Allegro软件二次开发和IPC通信原理,提出并设计一种Allegro软件基于MySQL数据库的数据录入系统。根据实际数据存取需求及用户权限,设计前端数据采集模块和网页显示模块,在前端数据采集模块中,通过研究IPC通信,制定通信协议,并结合SKILL语言、Python语言编写通信接口,实现Allegro软件与MySQL数据库通信;网页显示模块使用Java语言用于对数据进行展示和处理。最后对数据录入系统进行功能测试,测试证明能够达到预期效果。系统中Allegro软件与MySQL数据库通信协议设计以及通信接口设计是系统的关键设计,系统数据采集模块的实现对后续Allegro软件与其他语言通信、网页端功能扩展具有很大的参考价值。