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.     

新型动态汽车衡称重系统的设计与实现

针对目前现有动态汽车衡称重误差大等问题，提出了一种新型动态汽车衡称重系统的实现方法，该称重系统在基于Intel x86硬件平台和linux操作系统的基础上设计实现的。同时分析了产生称重误差的根本原因。采用了数字信号处理手段来提高动态汽车衡的称重精度。现场测试数据分析表明，该新型动态汽车衡称重系统技术指标优于国家动态衡标准。     

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.     

Data decomposition using independent component analysis with prior constraints

In many data analysis problems, it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make of a system are then taken to be related to these sources through some unknown function. Furthermore, the (unknown) number of underlying latent sources may be less than the number of observations. Recent developments in independent component analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, such data decomposition may be achieved in a mathematically elegant manner. In this paper, we extend the general ICA paradigm to include a very flexible source model, prior constraints and conditioning on sets of intermediate variables so that ICA forms one part of a hierarchical system. We show that such an approach allows for efficient discovery of hidden representation in data and for unsupervised data partitioning.     

大冶铁矿无底柱分段崩落法矿量管理与数据自动输入合成系统

本文简要介绍了作者应用数据库和OCR技术研制一个可处理大量开采信息的数据管理与自动输入的合成系统的概况，包括系统的设计、组成、功能和使用价值，并就有关技术问题进行了必要的讨论     

北京谱仪Ⅱ（BESⅡ）顶点探测器数据获取系统

北京谱仪（ＢＥＳⅡ）顶点探测器数据获取系统是北京谱仪数据获取系统的一部分，该子系统电子学采用快总线标准，本文描述了该系统的硬件结构和软件系统，软件包括快总线系统的微码软件和上层控制软件，该系统的死时间为１．５ｎｓ，对ＢＥＳⅡ系统总死时间的贡献小于０．５ｍｓ。     

稀土萃取信息系统

应稀土化学发展的需要,我们组建了稀土萃取信息系统。其中包括:数据的来源和数据类型;数据的组织;检索功能;数据间的关联;最优化设计以及萃取剂结构与其物理性质和结构与萃取性能的相关分析研究。