Exploratory Research On Engineering The Service Sector: Towards A Decision Informatics Paradigm

这项研究将寻求机会，将面向工程和科学的工具和方法推广到新兴类型的服务企业。“与此目的相一致，该研究在考虑了美国服务业的范围、规模和独特特征后，从工程角度确定了新兴服务的特征，这反过来又预示着一种决策信息学范式，用于加强服务的生产和提供。在这一新的批判性范式框架下，本研究的重点是多个非同质数据源的实时融合和分析。 首先，它是问题驱动的，而不是解决方案驱动的。 因此，它是工程相关的新兴服务的特点，明确确定了需要决策信息，这代表了一个范式的转变，向实时，信息驱动的决策。 其次，它将大大增加对多个数据源的实时分析和融合的知识体系。 虽然有大量的文献数据挖掘和模式分析，进行实时数据分析的工具是缺乏的。 例如，从实时决策的角度来看，关键是要知道最近收到的数据点是数据点的历史分布的一部分，还是一个离群值，即实际上是一个新现象的开始-因此，自适应实时决策不仅受到数据输入的影响，而且还受到输入顺序的影响。 此外，虽然存在关于来自多个同质（大多数是定量的）源的数据的融合的大量文献，但是用于进行来自多个非同质（即，（数量和质量）来源不足。 此外，多个非同质数据源的同步实时融合和分析代表了一个逃避调查的领域;然而，新兴服务，特别是电子服务，需要这样的工具来提高其生产和交付。 还应指出，研究报告力求产生最广泛的影响，特别是在研究与教育相结合方面。 首先，它构成了博士论文的基础。其次，开发的实时数据融合和分析工具将应用于现有的服务问题，采用来自多个非同质来源的真实的数据。 第三，将在一些地点介绍和公布结果。 第四，研究结果将被适当地组织起来，并被纳入主要研究者正在教授的两门现有课程中：一门名为“决策评估方法”的研究生课程和一门名为“以决策为重点的系统工程”的本科生课程。“第五，根据研究的探索性质，将确定额外的研究需求，以进一步支持决策信息学方法，类似于生物信息学的持续发展。

This study will pursue opportunities for extending engineering- and science-oriented tools and approaches to newly emerging types of service enterprises." Consistent with this purpose, the study, after considering the scope, size and unique features of the U.S. service sector, identifies characteristics of emerging services from an engineering perspective, which, in turn, augur for a decision informatics paradigm for enhancing the production and delivery of services. Within the framework of this new and critical paradigm, the study is focused on the real-time fusion and analysis of multiple non-homogeneous data sources.The intellectual merit of the study is underscored from two critical perspectives. First, it is problem-driven, not solution-driven. Thus, it is the engineering-related characteristics of emerging services that have clearly identified a need for decision informatics, which represents a paradigm shift towards real-time, information-driven decision making. Second, it will add significantly to the body of knowledge on the real-time analysis and fusion of multiple data sources. While there is considerable literature on data mining and pattern analysis, tools for undertaking real-time data analysis are lacking. For example, from a real-time decision perspective, it is critical to know whether the most recently received data point is part of the historical distribution of data points or an outlier that is, in effect, the beginning of a new phenomenon -- thus, adaptive, real-time decision making is not only affected by data input but also by the sequence of that input. Additionally, while there is considerable literature on the fusion of data from multiple, homogenous -- mostly quantitative -- sources, tools for undertaking real-time fusion of data from multiple, non-homogeneous (i.e., quantitative and qualitative) sources are lacking. Moreover, the coincident real-time fusion and analysis of multiple, non-homogeneous data sources represents an area that has eluded investigation; yet, emerging services -- especially e(lectronic)-services -- require such tools to enhance their production and delivery. It should also be noted that the study seeks to have the broadest impact, especially in regard to the integration of research and education. First, it constitutes the basis for a doctoral dissertation effort. Second, the developed, real-time data fusion and analysis tools will be applied to an existing service problem, employing real data from multiple, non-homogeneous sources. Third, the results will be presented and published in a number of venues. Fourth, the results will be appropriately structured and integrated into two existing courses being taught by the Principal Investigator; a graduate course, entitled "Evaluation Methods for Decision Making," and an undergraduate course entitled "Decision-Focused Systems Engineering." Fifth, consistent with the exploratory nature of the study, additional research needs will be identified to further underpin the decision informatics approach, similar to the continuing development of bioinformatics.