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Apparel pattern making is an age‐old occupation which is full of heuristics and unwritten rules. These rules have yet to be comprehensively studied for possible incorporation into apparel CAD systems. At present, most of these systems have drafting subsystems that address only a portion of this process. Explores pattern making in detail. Seeks to identify the motivations behind all its procedures and operations. A knowledge of such motivations provides a better understanding of the whole process. This knowledge can subsequently be used to design better computer‐aided pattern‐making systems.

With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In this paper, we propose a novel WUL (Web Usage Lattice) based mining approach for mining association access pattern rules for personalized web recommendations. The proposed approach aims to mine a reduced set of effective association pattern rules for enhancing the online performance of web recommendations. We have incorporated the proposed approach into a personalized web recommender system known as AWARS. The performance of the proposed approach is evaluated based on the efficiency and the quality. In the efficiency evaluation, we measure the number of generated rules and the runtime for online recommendations. In the quality evaluation, we measure the quality of the recommendation service based on precision, satisfactory and applicability. This paper will discuss the proposed WUL‐based mining approach, and give the performance of the proposed approach in comparison with the Apriori‐based algorithms.

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

The way organizations cope with uncertainty in strategic decision making is prominently discussed. Concepts such as heuristics and simple rules are gaining increasing attention in strategic management research. However, despite their importance, little is known how heuristics and simple rules operate. Our qualitative study reveals that, first, strategic decisions consist of three basic elements: single rules, rule patterns, and emotional handling. Second, we find that firms develop generalizable rule patterns which follow a sequential order of inter-linked rules. Based on the findings we introduce the concept of organizational heuristics as inter-linked rule patterns drawing on organizational experience.

Discusses MCell (version 4.2), a popular cellular automata explorer for Windows™. Gives a brief overview of the program, demonstrates its capabilities and shows possible applications, presents the library of collected rules and patterns, and explains how user‐defined extensions can be programmed.

The purpose of this paper is to focus on how to automatically generate the individualized patterns for women’s suits based on the 3D body point-cloud images.

With the software Imageware, the point-cloud data of the female body were measured according to the female body feature to obtain the heights, widths, depths and girths at various landmarks. Then the relationship between the height of each landmark and the body height was analyzed to build the height calculation rules by software SPSS, and the prediction models of body girths were established from the body widths and depths using regression analysis for pattern generation.

The pattern generation rules were built with the relationships between a human body and the garment patterns using the graphic flattening method. Based on the above rules, the final patterns were drafted automatically by using these dimensions to fit the subjects. The try-on experiment also showed that the individualized suits could fit the subjects’ body well at some feature landmarks.

In order to realize tailor-made and meet the consumers’ demands for individualized clothes, the development of garment CAD system has become inevitable in the garment industry. This paper could provide the foundation for automatic pattern generation, and technical support for tailor-made.

In terms of service hospitality, recent discussions of value-in-use from the perspective of service-dominant logic have focused on the customer’s determination of value and control of the value creation process. The purpose of this paper is to extend these discussions by exploring the value creation process in the Western-style restaurant in Taiwan, which is developed value-in-eat creation for restaurants. In Taiwan, Western-style restaurants are as popular as Chinese restaurants because of globalization and cultural integration. However, to local restaurateurs and managers, managing a Western-style restaurant in terms of localization and hospitality on value-in-eat creation presents both academic and practical issues. Thus, this paper aims to investigate Western-style restaurant hospitality management alternatives on the value-in-eat creation process in Taiwan using a data mining approach.

Based on a market survey, a total of 1,187 questionnaires was incorporated into a database. The questionnaire design is divided into 7 parts with 35 items. All questions are designed as nominal and ordinal (not the Likert scale) scales. Data mining approach, including cluster analysis and association rules, cluster analysis is investigated possible customer profiles and association rules is implemented to explore customer preference patterns and rules on the value-in-eat creation process.

Data mining results show two patterns including Pattern 1: meal patterns and customer preferences for restaurant hospitality management and Pattern 2: customer relationship management (CRM) for restaurant hospitality management that customer profiles and preferences on meal patterns, service patterns and CRM are engaged to suggest effective Western restaurant hospitality management alternatives, such as proper bundles for restaurant types, meals, exotic atmosphere and services of hospitalities in terms of a value-in-eat creation process.

To the best of the authors’ knowledge, this study is the first study to investigate consumers’ behaviors in Western-style restaurants using the measurement of nominal and ordinal scale for questionnaire development and further to implement a data mining approach on selected data samples. In addition, this study illustrates the patterns/rules of Taiwan customer preferences that best explain the knowledge of how to manage Western-style restaurants from the perspective of customer hospitality using data mining.

在酒店服务领域, 近期的从服务主流逻辑为视角关于使用价值的讨论主要集中在消费者对价值的定义以及掌控价值创造的过程。本研究的主要目的是拓展这些相关的讨论从而发掘关于在台湾经营的西餐厅的顾客价值创造过程, 进而开发餐厅的饮食价值创造。由于全球化进程, 台湾的西餐厅和中餐厅同样受欢迎。然而, 对于本地的餐厅所有人和经营管理者来讲, 管理西餐厅关于价值创造过程中的地方化和服务管理还存在学术和实践问题。因此, 本文运用了数据挖掘的方法对西餐关于价值创造的另类途径进行了探索。

基于市场调研, 本研究导入了1187份问卷作为数据库。问卷由7部分35项条目组成。所有问题以称名量表和顺序量表（非李克特量表）测量。数据挖掘包括了聚类分析和关联分析。聚类分析用来分析消费者概况, 关联分析来探究顾客倾向以及饮食价值创造过程。

数据挖掘结果显示了两种模式, 1：食物以及顾客对餐厅的接待管理的偏爱以及模式, 2：客户关系管理包括顾客概况和对饮食模式的偏爱, 服务模式以及顾客关系维护的另类建议, 诸如适度的捆绑销售包括餐厅种类, 菜系, 异域风情的就餐环境以及服务来体现饮食价值创造的过程。

本研究是首次探索了用称名量表和顺序量来研发的消费者问卷并且运用了数据挖掘的方法研究了西餐厅的消费者行为。 此外, 本研究阐明了台湾消费者的偏爱模式从而更好的解释了如何用数据挖掘的方法来研究西餐厅的服务管理。

Changing scattered and dynamic business rules in business workflow systems has become a growing problem that hinders the use and configuration of workflow-based applications. There is a gap in the existing research studies which currently focus on solutions that are application specific, without accounting for the universal logical dependencies between the business rules and, as a result, do not support adaptation of the business rules in real time. The paper aims to discuss this issue.

To tackle the above problems, this paper adopts a bottom-up approach, which puts forward a component model of the business process workflows and then adds business rules which have clear logical semantics. This allows incremental development of the workflows and semantic indexing of the rules which govern them during the initial acquisition.

The paper introduces an event-driven model for development of business workflows which is purely logic-based and can be easily implemented using an object-oriented technology, together with a model of the business rules dependencies which supports incremental semantic indexing. It also proposes a two-level inference mechanism as a vehicle for controlling the business process execution and the process of adaptation of the business rules at real time based on propagating the dependencies.

The framework is strictly logical and completely domain-independent. It allows to account both synchronous and asynchronous triggering events as well as both qualitative and quantitative description of the conditions of the rules. Although our primary interest is to apply the framework to the business processes typical in the construction industry we believe our approach has much wider potential due to its strictly logical formalization and domain independence. In fact it can be used to control any business processes where the execution is governed by rules.

The framework could be applied to both large business process modelling tasks and small but very dynamic business processes like the typical digital business processes found in online banking or e-Commerce. For example, it can be used for adjusting security policies by adding the capability to adapt automatically the access rights to account for additional resources and new channels of operation which can be very interesting ion both B2C and B2B applications.

The potential scope of the impact of the research reported here is linked to the wide applicability of rule-based systems in business. Our approach makes it possible not only to control the execution of the processes, but also to identify problems in the control policies themselves from the point of view of their logical properties – consistency, redundancies and potential gaps in the logics. In addition to this, our approach not only increases the efficiency, but also provides flexibility for adaptation of the policies in real time and increases the security of the overall control which improves the overall quality of the automation.

The major achievement reported in this paper is the construction of a universal, strictly logic-based event-driven framework for business process modelling and control, which allows purely logical analysis and adaptation of the business rules governing the business workflows through accounting their dependencies. An added value is the support for object-oriented implementation and the incremental indexing which has been possible thanks to the bottom-up approach adopted in the construction of the framework.

The purpose of this paper is to analyze how pattern recognition can contribute to the behavioral options of a goal‐oriented system.

A functional approach is used to develop the necessary cybernetic structures of a pattern recognition unit that can store observations as new standards for pattern matching by itself and can later apply them to recognize patterns in incoming sensor data.

Combining such a structure for pattern recognition with a feedback system shows that the resulting system can only deal with known patterns. To deal with novel patterns this structure has to be added to an adaptive system that can develop system‐specific behavior. Such a system has to able to initiate a trial and error process to test new behavior towards new patterns and to evaluate its effect on the highest, existential goal‐values of the system.

A system with a pattern recognition unit that can set new standards for pattern matching by itself is identified as the point of departure where not‐programmable and unpredictable individual behavior starts. Dealing with newly‐recognized pattern requires individual behavioral solutions and a system‐specific evaluation of the achieved results in relation to the highest goal‐values of the system. Here internal “emotional” criteria to select behavior emerge as a cybernetic necessity.

The paper is the third in a series of three on a cybernetic theory distinguishing system capable of pre‐programmed adaptation, system‐specific adaptation and learning. It determines the cybernetic starting point of individual psychology.

Natural collective phenomena, for example, the movement of crowds of pedestrians and the impressive nest formations of social insects, provide us with an existence proof that sophisticated constructions may be built by swarms of relatively simple artificial agents. The constructions often appear to have required impressive control and coordination – yet each agent in the collective does not appear to be provided with an internal world model or blue‐print for the complete construction. These macroscopic structures emerge as the consequence of interaction of agents, carrying out simple rules based upon the local state of the world, which includes the interaction between agents and the growing structure. In an attempt to understand the underpinning principles of structure formation in collectives of minimal mobile agents the paper focuses on an investigation of automata‐like agents in a two‐dimensional lattice. All agents start their evolution at the same site on the lattice. Every agent moves at random until it finds a neighbourhood it likes more than other neighbourhoods. The agents form a stationary structure of their immobile bodies. The paper focuses upon the parameterisation of the rule space and the mapping between parameter space and the resulting global structure formed by the agents.