基于时尚知识管理的服装概念设计方法研究
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摘要
在我国服装行业创新驱动、转型发展的背景下,增强服装品牌的自主研发能力,提高创新概念设计水平是我国迫切需要解决的问题。时尚在很长一段时间内被认为由时装设计师引导,本文认为时尚是在一定的社会背景下客观形成的,而不以人的主观意志为转变,时尚的形成可通过对众多因素客观、精准地综合分析。在当今时尚快速变化的背景下,服装设计工作已不再完全依赖设计师的个人创意,人量的现代化信息科技工具被运用于研发流程中。本文依托国家211重点学科建设子项目“服装艺术创意与流行趋势优化”,提出基于知识管理的理念,运用数据仓库工具模拟原本依赖服装设计师主观判断的时尚知识优化过程,通过决策支持系统形成科学、客观、快速、准确的服装概念设计方法。本文的主要内容为：
一、根据时尚知识管理的需求,提出基于知识管理的时尚数据仓库(Fashion Data Warehouse,简称FDW)的概念,并分析了采用数据仓库技术的必要性和可行性。根据时尚数据仓库体系的特点,构建了时尚数据字典、时尚数据来源、时尚数据整理、时尚数据挖掘和前端决策支持的总体结构。提出时尚数据字典(Fashion Data Dictionary,简称FDD)的概念,包括时尚色彩数据字典、时尚面料数据字典、时尚辅料数据字典、时尚图案数据字典、时尚工艺数据字典、时尚款式数据字典和时尚风格数据字典等,将来源于不同渠道的各种时尚数据进行格式统一。每种数据字典将数据类型、数据层级、数据内容及其标准表示方法进行了整理归纳。本文把时尚数据的抽取来源归纳为流行服装、社会背景和艺术作品,其中流行服装数据来源包括服装秀场、服装市场、服装品牌宣传、目标消费群、服装网店产品、服装和面料展会等,社会背景数据来源包括政治、经济、环境、科技、体育、生活方式等,艺术作品数据来源包括影视剧、美术、设计、音乐、舞台艺术、文学等。本文定义了时尚数据整理规范,包括时尚数据抽取内容、命名规则、转换规范和加载标准,使从各种渠道抽取的时尚数据以标准化的数据格式载入数据仓库
二、论述针对时尚服装知识的多维数据挖掘方法。为了解决对服装设计师获取时尚知识的渠道、数量、质量、理解程度等难以控制的问题,本文从时尚分析的需求和数据挖掘原理出发,提出时尚数据挖掘的概念,建立了OLAP多维数据挖掘模型。论述了时尚服装数据挖掘维表设计,包括时间维、来源维、品牌维、色彩维、面料维、辅料维、图案维、工艺维、款式维、风格维的层次和成员,制定了OLAP星型挖掘模型。根据时尚数据多维化的特性,定义了时尚服装数据方体,以及色彩、面料、辅料、图案、工艺、款式和风格等各维度数据挖掘的方体模型,包括时尚分析目标对象、数据挖掘层次、数据方体设置、挖掘结果示例例及其有效性分析等。时尚数据通过OLAP多维数据挖掘方法能够得到客观的时尚知识,包括时尚色彩、时尚面料、时尚辅料、时尚图案、时尚工艺、时尚款式和时尚风格等。本文提出时尚数据占比统计、排序统计和关键快照的前端报表呈现方式,提供定量、直观的时尚分析结论,作为服装产品概念设计的参考。以时尚色彩为例,展现了OLAP的前端报表的实际效果。同时论述了来源为社会背景和艺术作品数据的OLAP时尚风格挖掘方法。
三、社会背景对时尚风格的形成有着重要影响是服装行业的共识,但两者间的关系研究始终停留在社会学的定性研究。本文研究重点在于将属于隐性知识的社会背景转化为属于显性知识的时尚风格。把服装时尚风格归纳为具备相反特征的“两极型”时尚风格倾向：复古与未来、奢华与简约、保守与个性、中性与性别差异。同时将时尚风格分为现实时尚风格倾向和理想时尚风格倾向两大类。研究了欧美1900年至2009年社会背景中政治、经济、环境、科技事件对时尚风格的影响规律,归纳了社会背景和时尚风格倾向之间的对应关系。本文把社会背景归类成“两极型”和“单极型”指数表示方法,将每类事件按照其不同的特性归纳出极端,并提出社会背景综合指数的概念和获取方法。通过抽取社会背景综合指数的平均值、特异值和指数集中区域,得到时尚风格知识,并通过实例论证了从社会背景隐性知识向时尚风格显性知识的转换的可行性。这种研究方法的优点在于能够通过突破时间限制来细分时尚风格,以及通过突破区域限制分析本土时尚风格。
四、论述了时尚服装概念设计决策支持方法的概念和架构。服装概念设计具有明显的创造性、多解性、层次性、近似性、经验性和综合性特点,是设计过程中最关键和最具创造性的阶段。本文提出采用基于时尚数据仓库的服装概念设计决策支持方法,避免概念设计中的盲目性和主观性,达到客观、准确、高效的目的。本文将服装概念设计定义为用于指导服装新产品开发的时尚创意概念设计,包括风格主题概念,及其对应的色彩、面料、辅料、图案、工艺和款式等概念设计。论述了通过数据仓库进行时尚服装概念主题和服装元素概念设计决策支持的方法。对人机交互进行服装概念设计的架构、流程和界面设计等进行了系统规划,系统决策支持的结果可以储存、导出、共享和更新,以确保服装概念设计知识的积累和重新应用,为设计团队的实时协同工作提供保障。本文进行了基于决策支持的时尚服装概念设计模拟实验,就关键词选择与概念设计对象确立、时尚风格主题概念推荐、主题概念汇总界面、色彩概念设计等方法进行了论证。
本文的创新点主要表现在以下四个方面：
一、提出时尚数据仓库与时尚数据字典的概念和构架。将时尚数据按照数据字典的规范标准进行转换,其中包括服装的色彩、面料、辅料、图案、工艺、款式、风格等各项数据字典,将来源于不同渠道的各种数据进行格式标准化,以满足后续数据挖掘和决策支持功能。
二、提出采用联机分析处理(OLAP)进行时尚服装知识多维数据挖掘的方法。设计了针对时尚服装构成元素的多维数据挖掘立体模型,规定了数据挖掘的各个维度,以及统计报表的多种表现形式。
三、提出利用综合指数将社会背景隐性知识对应转换为时尚风格显性知识的方法,针对时尚风格受社会背景的影响的观点进行了从定性研究向定量研究突破性的尝试。
四、提出一种基于时尚数据仓库的服装概念设计决策支持方法,基于时尚数据仓库,通过数据挖掘提供服装时尚风格主题以及相对应的色彩、面料、图案、工艺、款式等概念设计方案。
本文研究成果将应用于服装企业和行业公共服务平台,协助服装企业提升概念设计的科学性、客观性、准确性和高效性,为我国服装行业的转型升级和提升自主研发水平做出贡献。
In the context of innovation-driven reformation and development of fashion industry in China, it becomes the most essential issue to enhance the ability of independent R&D and creative design level for Chinese local fashion brands. In quite a long period, fashion is considered to be determined by fashion designers. However, fashion is hereby considered to be formed according to certain social background, instead of being determined by certain people's subjective minds. So fashion could be generated by precise analysis from objective factors. Now in the context of fast fashion, fashion design is not merely rely on the designers' creativity, but all kinds of modern information technology are applied in the process of fashion design. The dissertation is formed by the National211Sub-project "fashion art creativity and trend optimization", based on the idea of knowledge management, using the tool of data warehouse to imitate the process of fashion designers' subjective fashion optimization and judgment, thus to provide scientific, objective, fast and accurate decision support for fashion conceptual design. The main content of the paper is as following:
1. According to the demand of fashion knowledge management, the paper proposes the concept of Fashion Data Warehouse (FDW) based on knowledge management, and analyzes the necessity and feasibility of using data warehouse technology. According to the characteristics of the fashion data warehouse system, an overall structure composed of fashion data dictionary, fashion data sources, fashion data management, fashion data mining and the front-end decision support is formed. The proposed concept of Fashion Data Dictionary (FDD), including Fashion Color Data Dictionary, Fashion Material Data Dictionary, Fashion Accessory Data Dictionary, Fashion Pattern Data Dictionary, Fashion Technique Data Dictionary, Fashion Style Data Dictionary, and Fashion Look Data Dictionary is formed, in order that all kinds of fashion data from different sources are unified in format. Each data dictionary regulates its data type, level, content and standard presentation. Sources of fashion data extraction are fashion clothing, social background, and art works. Fashion clothing data sources include fashion shows, fashion market, fashion brand advertisement, target consumer, fashion e-shop, fashion and fabric exhibition, etc. Social background data sources include politics, economy, environment, science and technology, sports, lifestyle, etc. Art works data sources include TV drama, art, design, music, performance art, literature, etc. The fashion data management is defined including fashion data extraction, naming method, conversion rules, and loading standard, so that the fashion data extracted from a variety of sources could be loaded in the fashion warehouse with standardized data format.
2. A multidimensional data mining method for fashion knowledge is discussed. In order to solve the problem of controlling fashion designers' fashion knowledge, including information sources, quantity, quality, understanding, and analysis level of fashion knowledge, based on fashion analysis demand and OLAP cube data mining principle, the paper discusses the design of fashion data mining dimension table, including the time-dimension, source-dimension, brand-dimension, color-dimension, fabric-dimension, accessory-dimension, pattern-dimension technique-dimension, style-dimension, look-dimension levels and members, and develops an OLAP star-model. According to fashion data multidimensional characteristics, fashion data cube and the cube model of various dimensions of data mining are defined, including fashion color, material, accessory, pattern, technique, style, look, and accordingly the target of fashion analysis, data mining hierarchy, data cube set, data mining result example and validity. Through OLAP multidimensional data mining, the objective knowledge of fashion, including fashion colors, fashion materials, fashion accessories, fashion patterns, fashion techniques, fashion styles and fashion looks could be acquired. As results for data mining, the fashion data proportion statistics, order statistics and key snapshot of front-end reports for presentation could provided with quantitative and visualized conclusions, so as to provide objective references for fashion conceptual design. The paper takes fashion color as example to show the actual effect of OLAP data mining result. Meanwhile, the paper discusses the OLAP data mining methods of fashion data from the social background and art work sources.
3. Social background has an important impact on the formation of fashion style is the consensus of the fashion industry, but the study of the relationship between the two has always been to stay in the sociology of qualitative research. The focus of the paper is the methodology of turning the implicit knowledge of social background into the explicit knowledge of fashion style. The fashion style tendencies are concluded into opposite directions:retro-futuristic, luxury-simple, conservative-individual, neutral-gender differences. And the styles are divided into realistic style tendency and ideal style tendency. With the historical research of western fashion and the social background of politics, economy, environment, science and technology events from1900to2009, the regulations between fashion style tendency and social background are concluded. The social background is indexed as "bipolar type" and "monopole type", and the concept of the social background Composite Index is put forward. By extracting the average figure, peculiar figure and the concentrated area of Composite Index, the fashion style tendency could be drawn. The paper demonstrated the feasibility of the conversion from the implicit knowledge of social background to explicit knowledge of fashion style through example research. The advantage of this approach is breaking through time limit and regional restrictions, so that the fashion styles can be drawn from various time period and districts.
4. The concept and structure of fashion conceptual design decision support is proposed in the paper. Fashion conceptual design is with creativity, multiple solution, hierarchy, similarity, empiric and comprehensive features, and is the most essential and most creative stage in the design process. The paper presents a conceptual design decision support method based on the fashion data warehouse, in order to avoid blindness and subjectivity in the conceptual design, to achieve objective, accurate and efficient conceptual design results. The definition of fashion conceptual design is the fashion creative concepts for guiding the new fashion product development, including the concept of design theme, and the corresponding color, material, accessory, pattern, technique and style design. The method of fashion conceptual design decision support for fashion themes and elements based on data warehouse is discussed. The paper also plans for the human-computer interaction of fashion conceptual design, including the structure, process and interface design. Decision support results from system can be stored, exported, shared and updated, so that the accumulation and reuse fashion conceptual design knowledge could provide possibility for the real-time collaborative design teamwork. The paper conducted experiments on fashion conceptual design based on decision support, to demonstrate the feasibility of the process of keyword selection, conceptual design object establishment, fashion style theme concept recommendation, theme concept summary interface, and color conceptual design.
The innovation of the paper is as the following four areas:
1. The concepts of Fashion Data Warehouse and Fashion Data Dictionary are proposed. Fashion data could be converted with the standard of the data dictionary, including fashion color, material, accessory, pattern, technique, style and look. Various fashion data from different sources could be unified in format, thus to meet requirements for subsequent data mining and decision support functions.
2. The online analytical processing (OLAP) method of fashion knowledge multidimensional data mining is proposed. The multidimensional model is designed according to the various fashion data. Also the designs of data mining statistical reports are discussed.
3. The method of turning the implicit knowledge of social background into the explicit knowledge of fashion style with composite index is proposed. The paper tries to make a shift from qualitative research to quantitative research in fashion style.
4. A fashion conceptual design decision support method based on fashion data warehouse is proposed. Through data mining, the recommended fashion style theme and corresponding color, fabric, pattern, technique, style concepts are suggested for conceptual design.
The results of the paper will be applied in the fashion enterprises and public service platform, in order to help the companies to enhance the scientific objectivity, accuracy and efficiency in fashion conceptual design, thus to enhance transformation, upgrading, and raise level of independent R&D for fashion industry in China.
一、根据时尚知识管理的需求,提出基于知识管理的时尚数据仓库(Fashion Data Warehouse,简称FDW)的概念,并分析了采用数据仓库技术的必要性和可行性。根据时尚数据仓库体系的特点,构建了时尚数据字典、时尚数据来源、时尚数据整理、时尚数据挖掘和前端决策支持的总体结构。提出时尚数据字典(Fashion Data Dictionary,简称FDD)的概念,包括时尚色彩数据字典、时尚面料数据字典、时尚辅料数据字典、时尚图案数据字典、时尚工艺数据字典、时尚款式数据字典和时尚风格数据字典等,将来源于不同渠道的各种时尚数据进行格式统一。每种数据字典将数据类型、数据层级、数据内容及其标准表示方法进行了整理归纳。本文把时尚数据的抽取来源归纳为流行服装、社会背景和艺术作品,其中流行服装数据来源包括服装秀场、服装市场、服装品牌宣传、目标消费群、服装网店产品、服装和面料展会等,社会背景数据来源包括政治、经济、环境、科技、体育、生活方式等,艺术作品数据来源包括影视剧、美术、设计、音乐、舞台艺术、文学等。本文定义了时尚数据整理规范,包括时尚数据抽取内容、命名规则、转换规范和加载标准,使从各种渠道抽取的时尚数据以标准化的数据格式载入数据仓库
二、论述针对时尚服装知识的多维数据挖掘方法。为了解决对服装设计师获取时尚知识的渠道、数量、质量、理解程度等难以控制的问题,本文从时尚分析的需求和数据挖掘原理出发,提出时尚数据挖掘的概念,建立了OLAP多维数据挖掘模型。论述了时尚服装数据挖掘维表设计,包括时间维、来源维、品牌维、色彩维、面料维、辅料维、图案维、工艺维、款式维、风格维的层次和成员,制定了OLAP星型挖掘模型。根据时尚数据多维化的特性,定义了时尚服装数据方体,以及色彩、面料、辅料、图案、工艺、款式和风格等各维度数据挖掘的方体模型,包括时尚分析目标对象、数据挖掘层次、数据方体设置、挖掘结果示例例及其有效性分析等。时尚数据通过OLAP多维数据挖掘方法能够得到客观的时尚知识,包括时尚色彩、时尚面料、时尚辅料、时尚图案、时尚工艺、时尚款式和时尚风格等。本文提出时尚数据占比统计、排序统计和关键快照的前端报表呈现方式,提供定量、直观的时尚分析结论,作为服装产品概念设计的参考。以时尚色彩为例,展现了OLAP的前端报表的实际效果。同时论述了来源为社会背景和艺术作品数据的OLAP时尚风格挖掘方法。
三、社会背景对时尚风格的形成有着重要影响是服装行业的共识,但两者间的关系研究始终停留在社会学的定性研究。本文研究重点在于将属于隐性知识的社会背景转化为属于显性知识的时尚风格。把服装时尚风格归纳为具备相反特征的“两极型”时尚风格倾向：复古与未来、奢华与简约、保守与个性、中性与性别差异。同时将时尚风格分为现实时尚风格倾向和理想时尚风格倾向两大类。研究了欧美1900年至2009年社会背景中政治、经济、环境、科技事件对时尚风格的影响规律,归纳了社会背景和时尚风格倾向之间的对应关系。本文把社会背景归类成“两极型”和“单极型”指数表示方法,将每类事件按照其不同的特性归纳出极端,并提出社会背景综合指数的概念和获取方法。通过抽取社会背景综合指数的平均值、特异值和指数集中区域,得到时尚风格知识,并通过实例论证了从社会背景隐性知识向时尚风格显性知识的转换的可行性。这种研究方法的优点在于能够通过突破时间限制来细分时尚风格,以及通过突破区域限制分析本土时尚风格。
四、论述了时尚服装概念设计决策支持方法的概念和架构。服装概念设计具有明显的创造性、多解性、层次性、近似性、经验性和综合性特点,是设计过程中最关键和最具创造性的阶段。本文提出采用基于时尚数据仓库的服装概念设计决策支持方法,避免概念设计中的盲目性和主观性,达到客观、准确、高效的目的。本文将服装概念设计定义为用于指导服装新产品开发的时尚创意概念设计,包括风格主题概念,及其对应的色彩、面料、辅料、图案、工艺和款式等概念设计。论述了通过数据仓库进行时尚服装概念主题和服装元素概念设计决策支持的方法。对人机交互进行服装概念设计的架构、流程和界面设计等进行了系统规划,系统决策支持的结果可以储存、导出、共享和更新,以确保服装概念设计知识的积累和重新应用,为设计团队的实时协同工作提供保障。本文进行了基于决策支持的时尚服装概念设计模拟实验,就关键词选择与概念设计对象确立、时尚风格主题概念推荐、主题概念汇总界面、色彩概念设计等方法进行了论证。
本文的创新点主要表现在以下四个方面：
一、提出时尚数据仓库与时尚数据字典的概念和构架。将时尚数据按照数据字典的规范标准进行转换,其中包括服装的色彩、面料、辅料、图案、工艺、款式、风格等各项数据字典,将来源于不同渠道的各种数据进行格式标准化,以满足后续数据挖掘和决策支持功能。
二、提出采用联机分析处理(OLAP)进行时尚服装知识多维数据挖掘的方法。设计了针对时尚服装构成元素的多维数据挖掘立体模型,规定了数据挖掘的各个维度,以及统计报表的多种表现形式。
三、提出利用综合指数将社会背景隐性知识对应转换为时尚风格显性知识的方法,针对时尚风格受社会背景的影响的观点进行了从定性研究向定量研究突破性的尝试。
四、提出一种基于时尚数据仓库的服装概念设计决策支持方法,基于时尚数据仓库,通过数据挖掘提供服装时尚风格主题以及相对应的色彩、面料、图案、工艺、款式等概念设计方案。
本文研究成果将应用于服装企业和行业公共服务平台,协助服装企业提升概念设计的科学性、客观性、准确性和高效性,为我国服装行业的转型升级和提升自主研发水平做出贡献。
In the context of innovation-driven reformation and development of fashion industry in China, it becomes the most essential issue to enhance the ability of independent R&D and creative design level for Chinese local fashion brands. In quite a long period, fashion is considered to be determined by fashion designers. However, fashion is hereby considered to be formed according to certain social background, instead of being determined by certain people's subjective minds. So fashion could be generated by precise analysis from objective factors. Now in the context of fast fashion, fashion design is not merely rely on the designers' creativity, but all kinds of modern information technology are applied in the process of fashion design. The dissertation is formed by the National211Sub-project "fashion art creativity and trend optimization", based on the idea of knowledge management, using the tool of data warehouse to imitate the process of fashion designers' subjective fashion optimization and judgment, thus to provide scientific, objective, fast and accurate decision support for fashion conceptual design. The main content of the paper is as following:
1. According to the demand of fashion knowledge management, the paper proposes the concept of Fashion Data Warehouse (FDW) based on knowledge management, and analyzes the necessity and feasibility of using data warehouse technology. According to the characteristics of the fashion data warehouse system, an overall structure composed of fashion data dictionary, fashion data sources, fashion data management, fashion data mining and the front-end decision support is formed. The proposed concept of Fashion Data Dictionary (FDD), including Fashion Color Data Dictionary, Fashion Material Data Dictionary, Fashion Accessory Data Dictionary, Fashion Pattern Data Dictionary, Fashion Technique Data Dictionary, Fashion Style Data Dictionary, and Fashion Look Data Dictionary is formed, in order that all kinds of fashion data from different sources are unified in format. Each data dictionary regulates its data type, level, content and standard presentation. Sources of fashion data extraction are fashion clothing, social background, and art works. Fashion clothing data sources include fashion shows, fashion market, fashion brand advertisement, target consumer, fashion e-shop, fashion and fabric exhibition, etc. Social background data sources include politics, economy, environment, science and technology, sports, lifestyle, etc. Art works data sources include TV drama, art, design, music, performance art, literature, etc. The fashion data management is defined including fashion data extraction, naming method, conversion rules, and loading standard, so that the fashion data extracted from a variety of sources could be loaded in the fashion warehouse with standardized data format.
2. A multidimensional data mining method for fashion knowledge is discussed. In order to solve the problem of controlling fashion designers' fashion knowledge, including information sources, quantity, quality, understanding, and analysis level of fashion knowledge, based on fashion analysis demand and OLAP cube data mining principle, the paper discusses the design of fashion data mining dimension table, including the time-dimension, source-dimension, brand-dimension, color-dimension, fabric-dimension, accessory-dimension, pattern-dimension technique-dimension, style-dimension, look-dimension levels and members, and develops an OLAP star-model. According to fashion data multidimensional characteristics, fashion data cube and the cube model of various dimensions of data mining are defined, including fashion color, material, accessory, pattern, technique, style, look, and accordingly the target of fashion analysis, data mining hierarchy, data cube set, data mining result example and validity. Through OLAP multidimensional data mining, the objective knowledge of fashion, including fashion colors, fashion materials, fashion accessories, fashion patterns, fashion techniques, fashion styles and fashion looks could be acquired. As results for data mining, the fashion data proportion statistics, order statistics and key snapshot of front-end reports for presentation could provided with quantitative and visualized conclusions, so as to provide objective references for fashion conceptual design. The paper takes fashion color as example to show the actual effect of OLAP data mining result. Meanwhile, the paper discusses the OLAP data mining methods of fashion data from the social background and art work sources.
3. Social background has an important impact on the formation of fashion style is the consensus of the fashion industry, but the study of the relationship between the two has always been to stay in the sociology of qualitative research. The focus of the paper is the methodology of turning the implicit knowledge of social background into the explicit knowledge of fashion style. The fashion style tendencies are concluded into opposite directions:retro-futuristic, luxury-simple, conservative-individual, neutral-gender differences. And the styles are divided into realistic style tendency and ideal style tendency. With the historical research of western fashion and the social background of politics, economy, environment, science and technology events from1900to2009, the regulations between fashion style tendency and social background are concluded. The social background is indexed as "bipolar type" and "monopole type", and the concept of the social background Composite Index is put forward. By extracting the average figure, peculiar figure and the concentrated area of Composite Index, the fashion style tendency could be drawn. The paper demonstrated the feasibility of the conversion from the implicit knowledge of social background to explicit knowledge of fashion style through example research. The advantage of this approach is breaking through time limit and regional restrictions, so that the fashion styles can be drawn from various time period and districts.
4. The concept and structure of fashion conceptual design decision support is proposed in the paper. Fashion conceptual design is with creativity, multiple solution, hierarchy, similarity, empiric and comprehensive features, and is the most essential and most creative stage in the design process. The paper presents a conceptual design decision support method based on the fashion data warehouse, in order to avoid blindness and subjectivity in the conceptual design, to achieve objective, accurate and efficient conceptual design results. The definition of fashion conceptual design is the fashion creative concepts for guiding the new fashion product development, including the concept of design theme, and the corresponding color, material, accessory, pattern, technique and style design. The method of fashion conceptual design decision support for fashion themes and elements based on data warehouse is discussed. The paper also plans for the human-computer interaction of fashion conceptual design, including the structure, process and interface design. Decision support results from system can be stored, exported, shared and updated, so that the accumulation and reuse fashion conceptual design knowledge could provide possibility for the real-time collaborative design teamwork. The paper conducted experiments on fashion conceptual design based on decision support, to demonstrate the feasibility of the process of keyword selection, conceptual design object establishment, fashion style theme concept recommendation, theme concept summary interface, and color conceptual design.
The innovation of the paper is as the following four areas:
1. The concepts of Fashion Data Warehouse and Fashion Data Dictionary are proposed. Fashion data could be converted with the standard of the data dictionary, including fashion color, material, accessory, pattern, technique, style and look. Various fashion data from different sources could be unified in format, thus to meet requirements for subsequent data mining and decision support functions.
2. The online analytical processing (OLAP) method of fashion knowledge multidimensional data mining is proposed. The multidimensional model is designed according to the various fashion data. Also the designs of data mining statistical reports are discussed.
3. The method of turning the implicit knowledge of social background into the explicit knowledge of fashion style with composite index is proposed. The paper tries to make a shift from qualitative research to quantitative research in fashion style.
4. A fashion conceptual design decision support method based on fashion data warehouse is proposed. Through data mining, the recommended fashion style theme and corresponding color, fabric, pattern, technique, style concepts are suggested for conceptual design.
The results of the paper will be applied in the fashion enterprises and public service platform, in order to help the companies to enhance the scientific objectivity, accuracy and efficiency in fashion conceptual design, thus to enhance transformation, upgrading, and raise level of independent R&D for fashion industry in China.
引文
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