数据挖掘技术在学生评估中的应用

近几年随着科技和现代教育的发展,数据库和网络技术得到飞速的发展和广泛的应用,智能教学系统也就大量地出现,这样就有大量的数据伴随着出现,而怎样处理这些数据使人们从中获得有用的信息变成一个热门的话题。数据挖掘技术就是将大量数据以一定的方式进行处理,得到对人们有用的信息,来帮助人们学习和预测。将数据挖掘技术引入到教学过程中学生的个人学习素质评估当中,创新性地通过决策树算法和聚类算法相结合的方法对学生数据分析,挖掘潜在的信息资源,找出影响个人综合素质的关键因素及各因素之间的关系,总结优秀学生的个人综合素质的培养方法,弥补不足,提高学生的个人综合素质。     

Business intelligence approach to supporting strategy-making of ISP service management

The recent deregulation of telecommunication industry by the Taiwanese government has brought about the acute competition for Internet Service Providers (ISP). Taiwan’s ISP industry is characterized by the heavy pressure for raising revenue after hefty capital investments of last decade and the lack of knowledge to develop competitive strategies. To attract subscribers, all ISP dealers are making an all-out effort to improve their service management. This study proposes a Business Intelligence process for ISP dealers in Taiwan to assist management in developing effective service management strategies. We explore the customers’ usage characteristics and preference knowledge through applying the attribute-oriented induction (AOI) method on IP traffic data of users. Using the self-organizing map (SOM) method, we are able to divide customers into clusters with different usage behavior patterns. We then apply RFM modeling to calibrate customers’ value of each cluster, which will enable the management to develop direct and effective marketing strategies. For network resource management, this research mines the facility utilization over various administrative districts of the region, which could assist management in planning for effective network facilities investment. With actual data from one major ISP, we develop a BI decision support system with visual presentation, which is well received by its management staff.     

Integrating scientific modeling and supporting dynamic hazard management with a GeoAgent-based representation of human–environment interactions: A drought example in Central Pennsylvania, USA

Recent natural disasters indicate that modern technologies for environmental monitoring, modeling, and forecasting are not well integrated with cross-level social responses in many hazard-management systems. This research addresses this problem through a Java-based multi-agent prototype system, GeoAgent-based Knowledge System (GeoAgentKS). This system allows: (1) computer representation of institutional regulations and behavioral rules used by multiple social institutions and individuals in cross-level human–environment interactions, (2) integration of this representation with scientific modeling of dynamic hazard development, and (3) application of automated reasoning that suggests to users the appropriate actions for supporting cooperative social responses. This paper demonstrates the software architecture of GeoAgentKS and presents such an integrated approach by modeling the drought management processes in Central Pennsylvania, USA. The results show that it is possible to use GeoAgentKS to represent multilevel human–environment interactions and to use those interactions as input to decision making in hazard management.     

A DSS for integrated distribution of empty and full containers

A DSS integrating empty and full containers transshipment operations is presented, addressing the typically unbalanced export/import containers trading problem. The problem is modeled as a network, where nodes represent customers, leasing companies, harbors and warehouses, while arcs represent transportation routes. The underlying mathematical model operates in stages, first prioritizing and adjusting full containers demands considering available empty containers supplies, and then statically optimizing costs. Transportation routes are registered and dynamically controlled, cyclically, for a given time horizon. The DSS is flexible, allowing several parameters to be configured. Experimental examples using randomly generated parameters were conducted to evaluate the effectiveness of the system.     

Approximate steady-state analysis of large Markov models based on the structure of their decision diagram encoding

We propose a new approximate numerical algorithm for the steady-state solution of general structured ergodic Markov models. The approximation uses a state-space encoding based on multiway decision diagrams and a transition rate encoding based on a new class of edge-valued decision diagrams. The new method retains the favorable properties of a previously proposed Kronecker-based approximation, while eliminating the need for a Kronecker-consistent model decomposition. Removing this restriction allows for a greater utilization of event locality, which facilitates the generation of both the state-space and the transition rate matrix, thus extends the applicability of this algorithm to larger and more complex models.     

Quasi-rocking real-time pushdown automata

A pushdown automaton (PDA) is quasi-rocking if it preserves the stack height for no more than a bounded number of consecutive moves. Every PDA can be transformed into an equivalent one that is quasi-rocking and real-time and every finite-turn (one-turn) PDA can be transformed into an equivalent one that is quasi-rocking or real-time. The quasi-rocking [quasi-rocking in the increasing mode, and quasi-rocking in the decreasing mode] real-time restriction in finite-turn (one-turn) PDAs coincides with the double Greibach [reverse Greibach, and Greibach] form in nonterminal-bounded (linear) context-free grammars. This provides complete grammatical characterizations of quasi-rocking and/or real-time (finite-turn and one-turn) PDAs and, together with known relations and other relations proved in the present paper, yields an extended hierarchy of PDA languages. Basic decision properties for PDAs can be stated in stronger forms by using the quasi-rocking and real-time restrictions and their undecidability/decidability status rests on the way PDAs quasi-rock.     

数据挖掘在PACS系统中的应用

PACS系统在医院得到越来越广泛的应用，大量的图像和文本资料存贮在PACS数据库中。这些海量数据仅仅是一些“数据”的集合，而不是“信息”或者“知识”的集舍。本文将数据挖掘技术与PACS系统结合，通过处理PACS系统中的各种病案资料，从“数据”中获取有用知识的集合，为医生的辅助诊断提供了智能的决策支持。     

数据挖掘在移动通信中的应用

简单介绍了数据挖掘技术,数据挖掘所需要的条件、数据仓库的建立,以及移动通信中经常用的两种算法：决策树算法和K最近邻算法,并较为详尽地阐述了它们的工作原理。     

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

Engineering-Based Decisions in Construction

This paper uses four case studies to emphasize the fact that field engineering decisions cannot be made without a full understanding of all the technical problems involved. Two of the studies are taken from conventional design-bid-build projects and two are taken from design-build projects in which the ability to strongly influence design in the interest of construction efficiency places a new and increased emphasis on construction and field engineering skills. The cases also show that field engineering decisions frequently address new and unexpected realities, changed conditions, and transient loads, and thus they require high levels of understanding, technical analysis, and creative thought. We emphasize the need for universities and construction engineering programs to develop graduates who are capable of integrating design and construction considerations throughout the project delivery process. We also urge programs to continue to focus strongly on field engineering as the foundation for success in an increasingly complex and technically driven construction industry.