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Iris: A Goal-Oriented Big Data Business Analytics Framework

机译：虹膜：面向目标的大数据业务分析框架

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摘要

Big data analytics is the hottest new practice in Business Analytics today. However, recent industrial surveys find that big data analytics may fail to meet business expectations because of lack of business context and lack of expertise to connect the dots, inaccurate scope and batch-oriented Hadoop system. In this dissertation, we present IRIS -- a goal-oriented big data analytics framework for better business decisions, which consists of a conceptual model that connects a business side and a big data side, providing context information around the data, an evidence-based evaluation method which enables to focus the most effective solutions, a process on how to use IRIS framework and an assistant tool using Spark, which is a real-time big data analytics platform. In this framework, problems against business goals of the current process and solutions for the future process are explicitly hypothesized in the conceptual model and validated on real big data using big analytics queries. As an empirical study, a shipment decision process is used to show how IRIS can support better business decisions in terms of comprehensive understanding both on business and data analytics, high priority and fast decisions.;Additionally, at the core of Big Data lies data, which is essential for supporting business analytics in gaining insights about business practices towards making better business decisions. The quality of business analytics inevitably depends on the kinds of individual data and relationships between the data, which should all be defined in a data model. A poor data model can lead to omissions or commissions of important business considerations, likely resulting in bad business decisions. However, there is little work on systematically and rationally developing a big data model for better supporting business analytics, especially in the presence of a variety of sources and types of data that are increasingly becoming available and useful. In this dissertation, we propose three notions of big data model quality -- relevance, comprehensiveness and relative priorities with a goal-oriented approach to building such qualities in a big data model. In this goal-oriented approach, alternatives in big data models are explored and selected for validating potential problems and solutions, while also achieving business goals. An empirical study has been conducted on the shipping decision process of a world-wide retail chain, to gain an initial understanding of the applicability of this approach.;Finally, many software systems are being developed to help with business processes, which typically involve a number of (human) tasks in achieving organizational goals. However, aligning a software system well with its intended business process has been challenging, since the tasks in a business process usually lack formal definitions and can be performed via multiple different allocations of resources. In this dissertation, we propose a goal-oriented transformational approach to deriving use cases, as requirements on the software system, from a business process which is modeled in BPMN (Business Process Model and Notation). In this approach, a business process is modeled not only in terms of the functionally-oriented BPMN but also non-functional business goals, and the target software requirements are also modeled in terms of functionally-oriented use cases together with non-functional requirements. Those tasks to be performed by a software system are transformed into use cases, in consideration of multiple alternative interpretations of business tasks, different allocations of software functionality and the granularity of the target requirements guided via similarity and granularity. Additionally, an intermediate model is utilized in the 2-step transformation process to deal with the ontological gap and the many-to-many relationships between the source and the target. This process is facilitated by context-aware transformation rules and a supporting tool. A study of a quote flow business process shows that our goal-oriented transformational approach helps produce more cohesive, correct and comprehensive use cases.
机译：大数据分析是当今业务分析中最热门的新实践。但是，最近的行业调查发现，大数据分析可能由于缺乏业务环境和缺乏连接点的专业知识，范围不准确以及面向批处理的Hadoop系统而无法满足业务期望。在本文中，我们提出了IRIS-一种用于更好业务决策的面向目标的大数据分析框架，该框架由连接业务侧和大数据侧的概念模型组成，提供围绕数据的上下文信息，基于证据一种评估方法，可用于关注最有效的解决方案，有关如何使用IRIS框架的过程以及使用Spark（实时大数据分析平台）的辅助工具的过程。在此框架中，在概念模型中明确假设了与当前流程的业务目标和未来流程的解决方案有关的问题，并使用大分析查询对实际大数据进行了验证。作为一项实证研究，货运决策流程用于展示IRIS如何在全面理解业务和数据分析，高优先级和快速决策方面支持更好的业务决策。此外，大数据的核心在于数据，这对于支持业务分析以获取有关业务实践的见解以做出更好的业务决策至关重要。业务分析的质量不可避免地取决于单个数据的类型以及数据之间的关系，所有这些都应在数据模型中定义。不良的数据模型可能导致重要业务考虑因素的遗漏或委托，可能导致错误的业务决策。但是，很少有系统地，合理地开发大数据模型以更好地支持业务分析的工作，尤其是在存在越来越多可用和有用的各种数据源和数据类型的情况下。在本文中，我们提出了大数据模型质量的三个概念-相关性，全面性和相对优先级，并采用了面向目标的方法来构建大数据模型的质量。在这种面向目标的方法中，探索并选择了大数据模型中的备选方案，以验证潜在的问题和解决方案，同时也实现了业务目标。对全球零售链的运输决策过程进行了实证研究，以初步了解这种方法的适用性。最后，正在开发许多软件系统来帮助进行业务流程，通常涉及到实现组织目标的（人类）任务数量。但是，使软件系统与其预期的业务流程保持一致非常困难，因为业务流程中的任务通常缺少正式定义，并且可以通过多种不同的资源分配来执行。在本文中，我们提出了一种面向目标的转换方法，用于从用BPMN（业务流程模型和表示法）建模的业务流程中获取用例，作为对软件系统的要求。在这种方法中，不仅根据面向功能的BPMN对业务流程进行建模，而且还针对非功能性业务目标进行建模，并且还针对面向功能的用例以及非功能性需求对目标软件需求进行建模。考虑到业务任务的多种替代解释，软件功能的不同分配以及通过相似性和粒度指导的目标需求的粒度，将由软件系统执行的那些任务转换为用例。此外，在两步转换过程中使用了一个中间模型来处理本体差距和源与目标之间的多对多关系。上下文相关的转换规则和支持工具有助于此过程。对报价流程业务流程的研究表明，我们面向目标的转换方法有助于产生更紧密，正确和全面的用例。

著录项

  • 作者

    Park, Eunjung.;

  • 作者单位

    The University of Texas at Dallas.;

  • 授予单位 The University of Texas at Dallas.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 165 p.
  • 总页数 165
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 康复医学;
  • 关键词

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