堤身的数据采集及断面设计的几点建议

针对堤防设计的基础资料采集和设计优化，提出了一些合理性建议。     

数据压缩的综合字典模型

文中提出一种无损的数据压缩的字典方法，被称为综合字典方法，该方法在较大程度上消除数据文件中多种形式的冗余，达到较好的压缩效果。     

Database Encoding and A New Algorithm for Association Rules Mining

This paper proposes a method to encode database. By this way, a record is denoted by only one binary number, so the size of the database is reduced sharply. If some known modified algorithms are used on the database encoded, the efficiency will be improved significantly. At the meantime, a new algorithm based on the proposed encoding method is introduced too. By using some properties of numbers, the itemsets of the database can be converted into numerical fields. Different from the Apriori, the new algorithm discovers the association rules from the largest frequent itemset at first, and then all subitemsets, which are also frequent, will be gotten without any calculation, and all the other small none-frequent itemsets that must be generated in the Apriori will be omitted, and the scan times of the database are also reduced. Test results show the new algorithm based on the encoding database has a lower complexity of time and space.     

Analysis and visualisation with host-based software – The features of STNAnaVist™

This article is based on experiences with data and text mining to gain information for strategic business decisions, using host-based analysis and visualisation (A/V), primarily in the field of patents. The potential advantages of host-based A/V are pointed out and the features of the first such A/V software, STN^®AnaVist™, are described in detail. Areas covered include the user interfaces, initial set of documents for A/V, data mining, text mining, reporting, and suggestions for further development.     

Delphi开发环境下的实时数据采集程序设计

本文介绍了利用Delphi开发平台开发数据采集程序的两种方法，并结合某测深仪给出了一个编程的例子.     

空调表冷器性能微机测试系统设计与软件开发

介绍了一套用于空调表冷器性能测试的试验装置及配套软件，重点叙述了系统的总体构成、程序的设计思想以及数据采样和处理等功能模块。微机测试技术以及配套软件的使用大大减少了测试人员的工作量，并在一定程度上提高了测试的精度。     

Mining evolving data streams for frequent patterns

A data stream is a potentially uninterrupted flow of data. Mining this flow makes it necessary to cope with uncertainty, as only a part of the stream can be stored. In this paper, we evaluate a statistical technique which biases the estimation of the support of patterns, so as to maximize either the precision or the recall, as chosen by the user, and limit the degradation of the other criterion. Theoretical results show that the technique is not far from the optimum, from the statistical standpoint. Experiments performed tend to demonstrate its potential, as it remains robust even under significant distribution drifts.     

On the cost of searching signature trees

A precise analysis of the retrieval of signature trees is presented. A signature tree is a data structure constructed over a signature file to speed up searching all those signatures, which match a given query signature. The methods used include a detailed study of probabilistic analysis in conjunction with suitable contour integration of complex variabled functions.     

GIS数据库中的数据挖掘

首先探讨了DM在GIS中应用的必要性和可能性,然后讨论了GIS数据库中的DM可以发现的知识类型、可以使用的方法,可以采用的DM系统原型,最后简单介绍了DM对GIS的贡献。     

Quality of manual data collection in Java software: an empirical investigation

Data collection, both automatic and manual, lies at the heart of all empirical studies. The quality of data collected from software informs decisions on maintenance, testing and wider issues such as the need for system re-engineering. While of the two types stated, automatic data collection is preferable, there are numerous occasions when manual data collection is unavoidable. Yet, very little evidence exists to assess the error-proneness of the latter. Herein, we investigate the extent to which manual data collection for Java software compared with its automatic counterpart for the same data. We investigate three hypotheses relating to the difference between automated and manual data collection. Five Java systems were used to support our investigation. Results showed that, as expected, manual data collection was error-prone, but nowhere near the extent we had initially envisaged. Key indicators of mistakes in manual data collection were found to be poor developer coding style, poor adherence to sound OO coding principles, and the existence of relatively large classes in some systems. Some interesting results were found relating to the collection of public class features and the types of error made during manual data collection. The study thus offers an insight into some of the typical problems associated with collecting data manually; more significantly, it highlights the problems that poorly written systems have on the quality of visually extracted data.