服务工作流中基于用户需求的调度模型及算法研究

在前期研究的基础上,从工作流的实用性与用户的个性化需求出发,提出了一种基于用户需求的工作流调度模型,该模型由业务逻辑模型、用户个性化定制模型、服务组织模型、工作流调度模型、工作流执行模型等五部分组成;同时研究了一种按用户需求的工作流调度机制,该机制能实现高层用户逻辑向底层执行映射的动态服务绑定;最后分析了调度机制失效时的最小代价恢复策略.与同类成果相比,调度模型具有灵活地支持用户个性化需求,算法复杂度低,并能以最小代价快速失效恢复等优点.     

可扩展工作流模型的信访业务协同处理系统

根据可定制业务流程和信息安全访问控制的需求,提出了一种可扩展动态工作流的协同处理模型。用户可以按照任务处理环节的需求以工作流子模型为核心定制任务中的活动类型和业务处理逻辑,定制各种附加子模型的业务知识与规则,并将基于角色和任务的访问控制运用于模型中,实现安全访问控制的工作流建模。利用该模型,研发了信访业务协同处理系统,介绍了系统的动态工作流管理技术、基于角色和任务的工作流访问控制与协调处理方法、以及基于任务授权控制的监控机制。三年多的应用情况证明,该系统在系统维护、协同处理、以及监控异常方面,具有应用优势。     

基于工作流的遥感产品生产可视化定制平台研究

以实现具有可视化的遥感产品生产定制功能为目标,提出了一种基于工作流的遥感产品生产可视化定制平台。给出了该平台的体系结构和系统组成,详细描述了平台工作流模型的建立、算法组件的构成、平台与算法的接口处理和可视化技术实现的具体方法。在.NET环境中实现了该平台。实际应用表明,该平台能够有效提高遥感产品的生产效率。     

基于工作流技术的信访业务协同处理系统

本文提出了一个基于工作流技术的协同处理模型。用户可以定制工作流,并将基于角色和任务的访问控制运用于模型中,实现安全访问控制的工作流建模。利用该模型,研发了信访业务协同处理系统,介绍了系统的动态工作流管理技术、基于角色和任务的工作流访问控制与协调处理方法。     

基于角色和任务的工作流访问控制模型及其应用*

提出了一种基于角色和任务的工作流访问控制模型RTBWAC,描述了模型中用户、角色、许可、活动等要素间的指派关系和该模型的静态、动态约束规则,最后给出了该模型在实际工作流系统中的应用.     

基于RFID数据集的供应链数据挖掘

为了从无线射频识别(RFID)设备收集的大量数据中得到优化商品供应链的信息,提出了利用RFID设备所收集的数据信息构建数据集的方法,运用数据清理及数据变换的方法生成用户有利的数据集。在对用户有利的数据集基础上,构建了工作流模型,提出了将Apriori算法应用到图中,进而得到进行数据挖掘发现的优化工作流网,最后写出了具体的实现算法,并验证了该算法的可行性。     

基于质量的数据挖掘服务选择

在面向服务的数据挖掘系统中各种数据挖掘的算法封装成 Web服务.用户选择合适的数据挖掘服务执行自己的数据挖掘任务,而大多数最终用户并不具备这样的专业知识.从方便用户的角度出发,系统需提供一套服务选择机制,来帮助用户选择高质量的数据挖掘服务.综合通用Web服务的评价标准、数据挖掘领域的专用评价因子及用户评价反馈等多种因素及服务的动态性,给出了一个较全面的数据挖掘服务评价本体,讨论了服务质量的评价方法,给出了基于服务质量评价的动态数据挖掘服务选择方法,用户可根据数据挖掘服务评价本体的语义模型,输入质量约束条件,也可以调整评价因子权值,系统在满足用户约束条件的服务集中,通过计算出服务的综合质量值,挑选最适合的算法执行.     

工作流系统中基于任务状态的转授权模型

为了解决工作流系统中多个用户协作完成某个任务时的权限分配问题,提出了工作流系统中用户-用户间基于任务状态的转授权模型,用任务处于某个状态的时间段作为转授权有效期的限制,将任务的部分权限作为转授权对象,在任务的各个状态下为不同的用户分配不同的权限.给出了模型的形式化定义,并提出了模型中转授权约束规则、冲突消解办法和转授权的撤销,最后举例说明该模型在实际工作流系统中的应用.模型能在不增加系统管理员负担的前提下,较好地解决工作流系统中多个用户协作完成某个任务时的授权问题.     

服务质量感知的网格工作流调度

在网格工作流中引入服务质量,可以使网格中的资源更好地围绕用户的要求进行组织和分配,服务质量为工作流执行过程中选择成员服务提供了依据.工作流服务质量的估算和服务质量感知的工作流调度是实现服务质量感知的网格工作流的两个关键问题.基于一种网格工作流模型讨论了网格工作流的服务质量参数体系,提出了工作流服务质量的估算算法和网格工作流调度数学模型,并提出了基于遗传算法的调度方法.仿真实验表明,该调度算法具有较好的收敛性.     

一种基于模式的服务工作流个性化定制方法

针对业务复杂多变,用户目标逐步明确的特点,提出一种面向用户需求的动态服 务工作流构建和实例化方法。首先构造服务流程模式,将业务知识和工作流相结合,作为服务 工作流的知识描述,实现对业务领域知识和与之适应的服务流程的抽象和规范;基于服务流程 模式,利用多层次匹配构造多粒度抽象服务组织模型,实现服务工作流的业务逻辑定制;然后, 利用QoS 分析进行具体服务选择,实现服务实例定制,最后通过一个应急处理示例来说明此 方法的应用。     

A process-mining-based scenarios generation method for SOA application development

Business process models which are usually constructed by business designers from experience and analysis are the main guidelines for services composition in the service-oriented architecture (SOA) applications development. However, due to the complexity of business models, it is a challenging task for business process designers to optimize the process models dynamically in accordance with changes in business environments. In this paper, a process-mining-based method is proposed to support business process designers to monitor efficiency or capture the changes of a business process. Firstly, we define a scenario model to depict business elements and their relationships which are critical to business process design. Based on the proposed scenario model, process mining algorithms, including control flow mining, roles mining and data flow mining are carried out in a certain sequence synthetically to extract business scenarios from event logs recorded by SOA application systems. Finally, we implement a prototype using a logistic scenario to illustrate the feasibility of our method in SOA applications development.     

Process mining using BPMN: relating event logs and process models

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified.     

模糊控制查询表的数据挖掘研究

一个性能良好的模糊控制系统的关键是它的模糊控制查询表。文章在详尽分析典型的模糊控制系统的基础上,给出了一种采用布尔关联规则挖掘技术,从人工操作记录数据库中直接挖掘模糊控制查询表的原理和方法。     

Constraint-based, multidimensional data mining

Although many data-mining methodologies and systems have been developed in recent years, the authors contend that by and large, present mining models lack human involvement, particularly in the form of guidance and user control. They believe that data mining is most effective when the computer does what it does best-like searching large databases or counting-and users do what they do best, like specifying the current mining session's focus. This division of labor is best achieved through constraint-based mining, in which the user provides restraints that guide a search. Mining can also be improved by employing a multidimensional, hierarchical view of the data. Current data warehouse systems have provided a fertile ground for systematic development of this multidimensional mining. Together, constraint-based and multidimensional techniques can provide a more ad hoc, query-driven process that effectively exploits the semantics of data than those supported by current standalone data-mining systems     

Encoding of primary structures of biological macromolecules within a data mining perspective

An encoding method has a direct effect on the quality and the representation of the discovered knowledge in data mining systems. Biological macromolecules are encoded by strings of characters, called primary structures. Knowing that data mining systems usually use relational tables to encode data, we have then to reencode these strings and transform them into relational tables. In this paper, we do a comparative study of the existing static encoding methods, that are based on the Biologist know-how, and our new dynamic encoding one, that is based on the construction of Discriminant and Minimal Substrings (DMS). Different classification methods are used to do this study. The experimental results show that our dynamic encoding method is more efficient than the static ones, to encode biological macromolecules within a data mining perspective.     

A real time process management system using RFID data mining

Recently, there have been numerous efforts to fuse the latest Radio Frequency Identification (RFID) technology with the Enterprise Information System (EIS). However, in most cases these attempts are centered mainly on the simultaneous multiple reading capability of RFID technology, and thus neglect the management of massive data generated from the RFID reader. As a result, it is difficult to obtain flow information for RFID data mining related to real time process control. In this paper, we propose an advanced process management method, called ‘Procedure Tree’ (PT), for RFID data mining. Using the suggested PT, we are able to manage massive RFID data effectively, and perform real time process management efficiently. Then we evaluate the efficiency of the proposed method, after applying it to a real time process control system connected to the RFID-based EIS. For the verification of the suggested system, we collect an enormous amount of data in the Enterprise Resource Planning (ERP) database, analyze characteristics of the collected data, and then compute the elapsed time on each stage in process control. The suggested system was able to perform what the traditional RFID-based process control systems failed to do, such as predicting and tracking of real time process and inventory control.     

业务流程的时延预测队列挖掘方法

过程挖掘是针对流程信息系统所记录下的日志进行分析,将业务流程真实过程还原的技术。目前已有的方法多是基于控制流与数据流的观点,针对任务运行状态的,无时延的业务过程进行挖掘。但在挖掘存在多任务的有时延的业务进程方面,目前的方法存在一定局限性。提出基于队列挖掘优化过程模型的方法,首先利用现有的基于过程挖掘的方法,挖掘业务流程的初始模型。再运用队列挖掘的观点对特定的顾客进行时延预测,挖掘出顾客的行为信息,以此对初始流程模型进行优化。最后通过实例验证了所提出的优化挖掘方法的有效性,优化后的流程模型不仅对事件日志有很好的重放效果,并且能够反应出多类别的,且存在时延的业务流程中任务的行为信息。     

增量更新Crawler进行Web收集方法研究

本文针对目前Web信息挖掘中存在的各种问题，对网络爬虫系统进行研究，提出了一种基于HTTP协议原理、旨在减少网络爬虫系统运行时网络流量的Web页面收集方法--增量更新Crawler方法。该方法通过Web预取技术对现有的Web链接数据库进行演化更新，可以在减少网络流量的同时获得接近现有网络爬虫系统的效果。     

A hybrid cost estimation framework based on feature-oriented data mining approach

This paper presents an informatics framework to apply feature-based engineering concept for cost estimation supported with data mining algorithms. The purpose of this research work is to provide a practical procedure for more accurate cost estimation by using the commonly available manufacturing process data associated with ERP systems. The proposed method combines linear regression and data-mining techniques, leverages the unique strengths of the both, and creates a mechanism to discover cost features. The final estimation function takes the user’s confidence level over each member technique into consideration such that the application of the method can phase in gradually in reality by building up the data mining capability. A case study demonstrates the proposed framework and compares the results from empirical cost prediction and data mining. The case study results indicate that the combined method is flexible and promising for determining the costs of the example welding features. With the result comparison between the empirical prediction and five different data mining algorithms, the ANN algorithm shows to be the most accurate for welding operations.     

Data mining model-based control charts for multivariate and autocorrelated processes

Process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional statistical process control (SPC) tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of multivariate and autocorrelated data found in modern systems. As the limitations of SPC methodology become increasingly obvious in the face of ever more complex processes, data mining algorithms, because of their proven capabilities to effectively analyze and manage large amounts of data, have the potential to resolve the challenging problems that are stretching SPC to its limits. In the present study we attempted to integrate state-of-the-art data mining algorithms with SPC techniques to achieve efficient monitoring in multivariate and autocorrelated processes. The data mining algorithms include artificial neural networks, support vector regression, and multivariate adaptive regression splines. The residuals of data mining models were utilized to construct multivariate cumulative sum control charts to monitor the process mean. Simulation results from various scenarios indicated that data mining model-based control charts performs better than traditional time-series model-based control charts.