GridDaen数据网格中Cache机制的设计与实现

数据网格是一个典型的分布式系统,访问其中的广域分布的海量数据需要很大的时间开销,介绍GridDaen数据网格系统实现高速数据统一访问的Cache技术。GridDaen采用二级Cache机制,使用两个数据缓冲表来快速定位缓冲数据和控制缓冲数据访问,给出了各级Cache的数据替换算法,并提供了灵活的配置方法,可以将Cache与客户机,服务器独立分布,实现了Cache的扩展性。     

Deriving Trends in Historical and Real-Time Continuously Sampled Medical Data

Monitors in Intensive Care Units generate large volumes of continuous data which can overwhelm a database and result in information overload for the medical staff. Instead of reasoning with individual data samples of one or more variables, it is better to work with the trend of the data i.e., whether the data is increasing, decreasing or steady. We have developed a system which abstracts continuous data into trends; it consists of three consecutive processes: filtering which smooths the data; temporal interpolation which creates simple intervals between consecutive data points; and temporal inference which iteratively merges intervals which share similar characteristics into larger intervals. Storing trends can result in a reduction in database volume. Our system has been applied both to historical and real-time data.     

数据网格中的一个多级Cache模型

数据网格是一个典型的分布式系统,访问其中的广域分布的海量数据需要很大的时间开销.本文介绍一个数据网格系统中实现高速数据统一访问的Cache模型,该模型采用二级Cache机制,使用二个数据缓冲表来快速定位缓冲数据和控制缓冲数据访问,给出了各级Cache的数据替换算法,并提供了灵活的配置方法,可以将Cache与客户机、服务器独立分布,实现了Cache的可扩展性.     

High-dimensional data visualisation: The textile plot

The textile plot is a parallel coordinate plot in which the ordering, locations and scales of the axes are simultaneously chosen so that the connecting lines, each of which represents a case, are aligned as horizontally as possible. Plots of this type can accommodate numerical data as well as ordered or unordered categorical data, or a mixture of these different data types. Knots and parallel wefts are features of the textile plot which greatly aid the interpretation of the data. Several practical examples are presented which illustrate the potential usefulness of the textile plot as an aid to the interpretation of multivariate data.     

Characteristic Rule Discovery in Aurum-3

One strategy for increasing the efficiency of rule discovery in data mining is to target a restricted class of rules, such as exact or almost exact rules, rules with a limited number of conditions, or rules in which each condition, on its own, eliminates a competing outcome class. An algorithm is presented for the discovery of rules in which each condition is a distinctive feature of the outcome class on its right-hand side in the subset of the data set defined by the conditions, if any, which precede it. Such a rule is said to be characteristic for the outcome class. A feature is defined as distinctive for an outcome class if it maximises a well-known measure of rule interest or is unique to the outcome class in the data set. In the special case of data mining which arises when each outcome class is represented by a single instance in the data set, a feature of an object is shown to be distinctive if and only if no other feature is shared by fewer objects in the data set.     

基于TPM的终端数据可信迁移研究

提出一种终端数据可信迁移方案以解决数据无防护地流入/流出终端所带来的安全问题。根据“全程BLP规则”对待流入/流出的数据进行安全检查,只允许符合安全策略的数据迁移,由TPM负责将其加密/解密。介绍实现框架并分析其安全性。该方案可以保证迁移数据的机密性和可控性。     

Software review

Summary The QL retrieval system is a significant piece of software. While it is not the kind of system one would use to manage a small personal collection of technical reports, it does provide the necessary capabilities to handle information retrieval requirements involving large data bases and/or multiple data bases and/or multiple users. It has evolved into a reliable, conceptually sound piece of software, especially suitable for applications in which data base modification consists principally of periodic updating (with little deletion or modification of existing data), and in which fast retrieval is desired.     

A computer method of constructing geological histories from field surveys and maps

A computer method has been designed to analyze binary relations between geological events. It interprets the relations in a form which allows construction of a multinodal network of relationships (of which tree-form networks are a special situation).The method is a form of data management which does not require the names to be ranked or the relations between names to be specified in advance, as do other geological systems. The data within the file may be added to as new information is collected, and the relationships determined from the data on file at any time. The method therefore is suited particularly to studies in which data accumulate progressively. Data structures which are more complex than a multinodal network are detected as “contradictions” or “contradictory rings”.The method has been used successfully in constructing explanations for geological maps from the field evidence recorded on the face of the map. It provides a means of assembling historical data from many maps as well as from supplementary information, such as radiometric or paleontological dates, and synthesizing a history for a region. The method therefore may be useful in tectonic analysis.     

A Very High-Level Interactive Graphical Trace for the Pascal Heap

A very high-level trace for data structures is one which displays a data structure in the shape in which the user conceptualizes it, be it a tree, an array, or a graph. GRAPHTRACE is a system that facilitates the very high-level graphic display of interrelationships among dynamically allocated Pascal records. It offers the user a wide range of options to enable him to "see" the data structures on a graphics screen in a format as close as possible to that in which he visualizes it, thereby providing a useful display capability when the user's conceptual model is a directed graph or tree.     

外-残缺数据的生成-辨识

P-集合具有动态特性,它是研究动态信息系统的一个新的数学工具。利用P-集合结构中的外P-集合与F-元素补充集合,给出外-残缺数据与外-残缺数据圆的概念。外-残缺数据是由(x)F与(x)+构成的一个数据对((x)F,(x)+);外-残缺数据圆是由数据圆OF与O+构成的一个数据圆对(OF,O+)。利用外-残缺数据与外-残缺数据圆讨论外-残缺数据的生成与辨识,给出相关定理与外-残缺数据辨识准则,及外-残缺数据辫识的应用。     

信息管理系统操作动态处理方法的研究与实现

创建一个公共类,实现信息系统用户对重要业务数据操作动态的处理,用户修改或删除业务数据时自动保存操作动态,并把修改或删除的数据保存到数据库中,以便恢复或查询历史动态,解决误操作或人为因素导致信息丢失的问题。     

Cluster Validity for the Fuzzy c-Means Clustering Algorithrm

The uniform data function is a function which assigns to the output of the fuzzy c-means (Fc-M) or fuzzy isodata algorithm a number which measures the quality or validity of the clustering produced by the algorithm. For the preselected number of cluster c, the Fc-M algorithm produces c vectors in the space in which the data lie, called cluster centers, which represent points about which the data are concentrated. It also produces for each data point c-membership values, numbers between zero and one which measure the similarity of the data points to each of the cluster centers. It is these membership values which indicate how the point is classified. They also indicate how well the point has been classified, in that values close to one indicate that the point is close to a particular center, but uniformly low memberships indicate that the point has not been classified clearly. The uniform data functional (UDF) combines the memberships in such a way as to indicate how well the data have been classified and is computed as follows. For each data point compute the ratio of its smallest membership to its largest and then compute the probability that one could obtain a smaller ratio (indicating better classification) from a clustering of a standard data set in which there is no cluster structure. These probabilities are then averaged over the data set to obtain the values of the UDF.     

一种XML数据并行更新方法*

提出了一种支持在分布式系统中并行地修改XML数据的方法策略,实现了不同的系统客户端可以同时修改同一个XML数据的不同部分或相同部分.支持修改操作的分散式管理,即客户端在不与服务器进行交互的情况下进行正确的修改.支持动态客户端和动态权限控制信息,适用于在更新过程中参与更新的客户端和更新权限都发生变化的情况.     

A Method for Representing Data Items of Unlimited Length in a Computer Memory

In this paper, a recursive data structure is defined which permits a data item of-unlimited length to be represented in a computer memory. The encoded data are a linear string of symbols which contains a specification of the length of the data item and the data item itself. In a single forward scan of the encoded data string, the length of the data item can be determined and the data item located and/or extracted.     

Random multispace quantization as an analytic mechanism for BioHashing of biometric and random identity inputs

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the random multispace quantization (RMQ) of biometric and external random inputs     

一种数据块相关标记码的测试数据压缩方法

提出了一种类似于字典索引的编码压缩方法,该方案将与参考数据块相容、反相容的测试数据块用“0”、“1”标记来压缩数据,并用定长的数据来标识与参考数据块相关的数据块个数。通过分析可知方案的解压电路结构简单,所需的硬件开销很小,对ISCAS’89基准电路的实验结果表明,该编码方法能有效地压缩测试数据。     

RMINE: A Rough Set Based Data Mining Prototype for the Reasoning of Incomplete Data in Condition-based Fault Diagnosis

Condition-based fault diagnosis aims at identifying the root cause of a system malfunction from vast amount of condition-based monitoring information and knowledge. The needs of extracting knowledge from vast amount of information have spurred the interest in data mining, which can be categorized into two stages data preparation and knowledge extraction. It has been established that most of the current data mining approaches to fault diagnosis focus on the latter stage. In reality, condition-based monitoring data may, most of the time, contain incomplete, or missing data, which have an adverse effect on the knowledge or diagnostic rules extracted. Several approaches to deal with missing data can be found in literature. Unfortunately, all of which have serious drawbacks. In this paper, a novel approach to the treatment of incomplete data is proposed for the data preparation stage, while a rough set based approach has been developed to pre-process the data for the extraction of diagnostic rules. The two-stage data mining technique is implemented into a prototype system, RMINE, which also possesses a self-learning ability to cope with the changing condition-based data. A real industrial case study of a pump system is used to demonstrate the fault diagnosis process using RMINE. The result has shown the potential of RMINE as a general data mining prototype to condition-based fault diagnosis.     

Incorporating domain knowledge into attribute‐oriented data mining

It is frequently the case that data mining is carried out in an environment which contains noisy and missing data. This is particularly likely to be true when the data were originally collected for different purposes, as is commonly the case in data warehousing. In this paper we discuss the use of domain knowledge, e.g., integrity constraints or a concept hierarchy, to re‐engineer the database and allocate sets to which missing or unacceptable outlying data may belong. Attribute‐oriented knowledge discovery has proved to be a powerful approach for mining multi‐level data in large databases. Such methods are set‐oriented in that attribute values are considered to belong to subsets of the domain. These subsets may be provided directly by the database or derived from a knowledge base using inductive logic programming to re‐engineer the database. In this paper we develop an algorithm which allows us to aggregate imprecise data and use it for multi‐level rule induction and knowledge discovery. ©2000 John Wiley & Sons, Inc.     

一种新的基于相似度过滤的大数据保序匹配与检索算法

伴随大数据时代的到来,数据快速保序匹配与检索成为众多大数据应用急需解决的关键问题,通过抽象与归约等措施,数据对象可抽象为具有若干属性的点集或序列,从而将数据匹配问题转化为字符或数字序列匹配问题。提出一种基于相似度过滤的数据保序匹配与检索算法,算法分三步:(1)数据转换,基于幅值变化趋势将原始序列转换为二进制,对序列中任何一个字符,通过判断包括其前后邻居在内的三个点的关系定义二进制序列,准确反映相邻三点之间的凸增长(降低)或凹增长(降低)关系;(2)数据归约,为方便候选序列与模式序列之间的相似度计算,运用基于幅度变化比例的数据归约方法,将候选序列与模式序列均归约到固定区间;(3)相似度计算,为区分不同趋势的凸增长(降低)或凹增长(降低)幅度,通过计算候选序列与模式序列对应点之间的差值绝对值之和作为相似度判断依据,提出基于相似度过滤的快速匹配方法,寻找与模式序列变化趋势一致的子序列集合,并按照相似度大小排序。理论分析与实验结果表明:(1)该算法具有亚线性时间复杂度;(2)该算法能有效解决Chhabra等人算法对数据震荡幅度失控的问题,同时解决数据序列与模式序列分段规律但整体不相似的问题;(3)解决了Chhabra等人算法中对匹配序列排序造成的匹配结果疏漏问题。该方法不仅能更准确、更多地匹配出变化趋势一致的子字符串,同时将多个候选子串根据与模式之间的相似度进行排序,为进一步的数据精确检索提供判断依据。