一种基于目标的过程实例演化方法

过程具有长生命周期、分布式结构、异构等特性.为适应不可预料的环境变化,过程还需要不断地演化.提出一种过程实例的演化方法以控制对过程实例的改变.它可以检查并保证演化前后的过程实例具备"基于目标的行为一致性",从而避免用户对过程的盲目修改.目标作为行为一致的标准,需要分析演化后的过程实例来保证它可以完成目标.通过用Büchi自动机描述过程实例和其目标的语义,一个过程实例是否能够完成其目标是可判定的.     

A Unifying View on Instance Selection

In this paper, we consider instance selection as an important focusing task in the data preparation phase of knowledge discovery and data mining. Focusing generally covers all issues related to data reduction. First of all, we define a broader perspective on focusing tasks, choose instance selection as one particular focusing task, and outline the specification of concrete evaluation criteria to measure success of instance selection approaches. Thereafter, we present a unifying framework that covers existing approaches towards solutions for instance selection as instantiations. We describe specific examples of instantiations of this framework and discuss their strengths and weaknesses. Then, we outline an enhanced framework for instance selection, generic sampling, and summarize example evaluation results for several different instantiations of its implementation. Finally, we conclude with open issues and research challenges for instance selection as well as focusing in general.     

一种基于实例状态的工作流系统监控方法

工作流监控是处理工作流运行期各种例外、保证工作流实例正确高效执行的有效方法.基于状态的工作流监控方法不仅可以实时处理实例执行过程中出现的各种例外、消除实例运行的阻塞,并能对任务执行进行必要的干预.但这类监控也经常造成工作流模型及实例的不一致,如不加以调整,会导致后续任务无法执行.在对ADEPT模型及过程实例进行形式化定义基础上,给出用于验证过程模型正确性和过程实例一致性的判定规则;提出一个基于状态的实时监控方法,该方法由一组状态转换原语和过程实例转换算法组成.采用该监控方法并结合上述判定规则,在完成各种监控任务的同时,保证了模型的正确性和过程实例的一致性.最后讨论了原语操作的完备性和一致性.     

面向企业的数据集成建模方法研究

针对目前企业普遍存在的信息孤岛林立和数据断层的局面，本文提出了一种进行企业数据集成的模型——企业统一数据模型，应用该模型可以较好地解决企业混乱的数据环境，建立一个统一高效的企业数据平台。同时本文结合在炼化企业建立统一数据模型的经验，详细论述了企业统一数据模型的建模方法。     

一种基于扩展任务结构的工作流实例迁移方法

工作流实例迁移是工作流演化研究中典型而重要的问题.在基于扩展任务结构的工作流定义的基础上,提出了适用于一般动态变化处理的实例迁移规则、条件和算法.与同类研究工作比较,该方法在适用范围、通用性、正确性和可实现能力等方面具有一定的先进性.     

更快速的高阶细胞自动机超并行数据压缩方法

构造出高阶置换映射,进而得出更有效的高阶细胞自动机超并行数据压缩方法,在不增加细胞自动机总体结构复杂性的情况下,比文献「１」中并行压缩方法的处理速度可以成倍地提高。证明了用遗传进化算法得到的高阶细胞自动机元胞级无失真数据压缩规则的正确性和可行性,讨论了有关的时间复杂性及高阶数据压缩方法的有效性。     

“数据结构”中基本教学案例的研究

本文阐明了"数据结构"教学过程中应用案例的重要性,并对示例案例进行详细的分析、设计;描述了如何进行案例教学的全过程;最后将案例研究应用于实际教学中,结合实验教学展示了通过数据结构案例进行教学的一个实例。     

A New Approach to the Construction of Surfaces from Contour Data

This paper presents a new approach to the construction of a surface from a stack of contour slices. Unlike most existing methods, this new approach handles ambiguous conditions consistently without employing an algorithm to establish a correspondence between vertices on one contour and those on the next. It is easy to implement and fast to compute, requiring only basic geometric properties, namely closedness and simplicity, to be available with contour data. The advantages of this new approach have also been demonstrated with solutions to a few classical problems from the literature and some practical problems in medical imaging. It can also be applied to geographical surveying and keyframe animations.     

HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.     

Data Mining and Machine Oriented Modeling: A Granular Computing Approach

From the processing point of view, data mining is machine derivation of interesting properties (to human) from the stored data. Hence, the notion of machine oriented data modeling is explored: An attribute value, in a relational model, is a meaningful label (a property) of a set of entities (granule). A model using these granules themselves as attribute values (their bit patterns or lists of members) is called a machine oriented data model. The model provides a good database compaction and data mining environment. For moderate size databases, finding association rules, decision rules, and etc., can be reduced to easy computation of set theoretical operations of granules. In the second part, these notions are extended to real world objects, where the universe is granulated (clustered) into granules by binary relations. Data modeling and mining with such additional semantics are formulated and investigated. In such models, data mining is essentially a machine calculus of granules-granular computing.     

Adaptive Intrusion Detection: A Data Mining Approach

In this paper we describe a data mining framework for constructingintrusion detection models. The first key idea is to mine system auditdata for consistent and useful patterns of program and user behavior.The other is to use the set of relevant system features presented inthe patterns to compute inductively learned classifiers that canrecognize anomalies and known intrusions. In order for the classifiersto be effective intrusion detection models, we need to have sufficientaudit data for training and also select a set of predictive systemfeatures. We propose to use the association rules and frequentepisodes computed from audit data as the basis for guiding the auditdata gathering and feature selection processes. We modify these twobasic algorithms to use axis attribute(s) and referenceattribute(s) as forms of item constraints to compute only therelevant patterns. In addition, we use an iterative level-wiseapproximate mining procedure to uncover the low frequency butimportant patterns. We use meta-learning as a mechanism to makeintrusion detection models more effective and adaptive. We report ourextensive experiments in using our framework on real-world audit data.     

A Data Flow Approach to Procedural Modeling

Procedural models can simulate an object's behavior as well as its appearance. When combined with data flow methods, they provide a useful approach to image composition and animation.     

基于建构主义的程序设计教学实例设计

在程序设计教学中,实例设计的优劣影响整个程序设计的教学效果。本文以VB程序设计教学为例,提出一种基于建构主义学习理论、融合情境创设教学理念的实例设计方法。使用生活中真实熟悉的软件实例,从整体上由浅入深渐进式把握程序设计的教学。教研组多次教学实践证明,使用该方法设计实例进行教学,易于激发学生的学习兴趣和探究欲望,利于学生的主动意义建构。     

Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases

Spatio-temporal databases deal with geometries changing over time. In general, geometries cannot only change in discrete steps, but continuously, and we are talking about moving objects. If only the position in space of an object is relevant, then moving point is a basic abstraction; if also the extent is of interest, then the moving region abstraction captures moving as well as growing or shrinking regions. We propose a new line of research where moving points and moving regions are viewed as 3-D (2-D space+time) or higher-dimensional entities whose structure and behavior is captured by modeling them as abstract data types. Such types can be integrated as base (attribute) data types into relational, object-oriented, or other DBMS data models; they can be implemented as data blades, cartridges, etc. for extensible DBMSs. We expect these spatio-temporal data types to play a similarly fundamental role for spatio-temporal databases as spatial data types have played for spatial databases. The paper explains the approach and discusses several fundamental issues and questions related to it that need to be clarified before delving into specific designs of spatio- temporal algebras.     

面向压缩生物基因数据的高效的查询方法

随着下一代,第三代等测序技术的快速发展,DNA 等生物序列数据快速增长.如何高效的处理这些大数据是目前的一个挑战.研究发现这些生物序列数据尽管很大,但是不同数据之间具有高相似性.因此可以通过保存这些基因串同一个基准序列之间的差异来减少存储的代价.最新的研究发现可以在这些压缩的数据上直接进行查询,而不需要解压缩.本文研究的目标是进一步提高索引和查询的可伸缩性,从而满足日益增长的大数据需要.本文首先在现有方法基础之上,对基准序列进行了压缩存储.基于该压缩数据,本文提出了一系列优化查询方法来高效的支持任意长度序列的精确和近似查询.在此基础上,本文进一步对原有方法进行改进,利用并行计算来提高对大数据的查询效率.最后,实验研究展示了本文提出的方法的高效性.     

An Adaptive Approach to Schema Classification for Data Warehouse Modeling

Data warehouse （DW） modeling is a complicated task, involving both knowledge of business processes and familiarity with operational information systems structure and behavior. Existing DW modeling techniques suffer from the following major drawbacks -- data-driven approach requires high levels of expertise and neglects the requirements of end users, while demand-driven approach lacks enterprise-wide vision and is regardless of existing models of underlying operational systems. In order to make up for those shortcomings, a method of classification of schema elements for DW modeling is proposed in this paper. We first put forward the vector space models for subjects and schema elements, then present an adaptive approach with self-tuning theory to construct context vectors of subjects, and finally classify the source schema elements into different subjects of the DW automatically. Benefited from the result of the schema elements classification, designers can model and construct a DW more easily.     

Enterprise Data Quality: A Pragmatic Approach

Enterprise data—the data that is created, used and shared by a corporation in conducting business—is a critical business resource that must be analyzed, architected and managed with data quality as a guiding principle. This paper presents results, practical insights, and lessons learned from a large scale study conducted in the telecommunications industry that synthesizes data quality issues into an architectural and management approach. We describe the real life case study and show how requirements for data quality were collected, how the data quality metrics were defined, what guidelines were established for intersystem data flows, what COTS (commercial off-the-shelf) technologies were used, and what results were obtained through a prototype effort. As a result of experience gained and lessons learned, we propose a comprehensive data quality approach that combines data quality and data architectures into a single framework with a series of steps, procedures, checklists, and tools. Our approach takes into account the technology, process, and people issues and extends the extant literature on data quality.     

如何进行数据压缩

信息如何被高效存储和传递的问题一直是计算机研究的一个重要课题,而解决这一问题的最常用的就是数据压缩技术。首先讲述了数据压缩的原理、分类,然后用哈夫曼方法编写了一个用于无损压缩的算法并对这个算法进行了详细的描述。这个算法不仅适用于文档类文件的压缩,还可以对图像类文件进行压缩。最后对这个算法进行分析得出结论。     

人民银行基层分支机构机房建设初探

文章是计算机中心机房建设的经验教训总结。探索了银行业基层机构计算机房建设的总体规划、工程建设质量控制,以及后期测试、验收等,为银行业IT从业者提供了有益的探索和经验。     

体数据压缩技术综述

数据压缩是体数据可视化研究中的一个重要问题.随着体数据的维数、分辨率、变量个数不断增加,数据量呈指数增长,体数据的压缩研究显得更为重要.文中从有损压缩和无损压缩2个方面对已有的体数据压缩方法进行了总结,对无损压缩的各类方法的优缺点进行了比较,对有损压缩的一般流程和其中的重要步骤做了详细的介绍,同时着重介绍了时变体数据压缩方法.最后提出了该领域需进一步探索的方向.