Systems and Methods for Crowdsourcing Domain Expertise

软件专业人员越来越多地开发应用程序，以支持复杂领域（如生物信息学、财务分析、医疗保健等）领域专家的工作。然而，这些软件专业人员主要接受计算机科学、工程或信息技术方面的培训，如果没有领域专家的帮助，他们很难理解一个不熟悉的复杂领域的细微差别。由于财务专家、医生、药剂师和其他领域专家已经在受限制的时间表内工作，他们不能一对一地回答领域特定的问题，这经常导致软件项目的延迟或失败。**建议的工作将通过发明解决方案来减少对领域专家的一对一依赖，从而提高在复杂领域工作的软件专业人员的生产力。本研究将通过设计和评估利用领域专家知识的新工具和方法，探索众包领域专业知识的理念。众包（Crowdsourcing）是指将一个任务分解成若干个微任务，由一个在线群体工作者或志愿者社区完成的过程（例如，维基百科、雅虎问答和亚马逊的Mechanical Turk都是众包系统）。与现有的服务在很大程度上为大众分解任务不同，拟议的研究将为领域专家创造微任务环境，这些专家拥有深厚的主题知识、专家见解，在许多情况下，还拥有博士学位或数十年的经验。此外，本研究还将发明方法，将这些众包服务嵌入到软件专业人员使用的工具中，以促进特定领域知识的上下文检索。**借鉴人机交互（HCI）和软件工程领域，以及我在开发众包系统方面的经验，拟议的研究将有三个关键目标：**1)开发一个框架，将复杂的、特定领域的问题分解为微任务：我们将建立一个框架，用于在微任务环境中如何将任务分解并分配给领域专家小组。我们将调查哪些类型的专家任务适合众包，并根据经验确定在众包服务中参与领域专家所需的激励措施。**2)开发支持特定领域众包的软件服务和基础设施：我们将设计和实施利用领域专业知识的众包服务。我们将特别关注如何为任务分配开发有效的系统和算法，以及如何将这些众包服务嵌入到软件专业人员使用的工具中。**3)通过实证研究验证众包技术和服务：尽管在整个研究中将使用迭代评估方法，但核心焦点将放在与软件专业人员验证拟议的系统设计上。通过对不同领域的实证调查，我们将能够确定长期使用众包服务如何影响软件专业人员的生产力和领域专家的工作流程。**本研究的工具和发现将提高在复杂领域工作的加拿大软件专业人员的生产力，更广泛地说，将推进众包领域专业知识的科学。除了支持软件专业人员之外，这项研究将为在研究和工业环境中转变其他类型的多学科合作奠定关键基础，并使加拿大成为众包领域专业知识研究的领导者。

Software professionals are increasingly developing applications to support the work of domain experts in complex domains, such as bioinformatics, financial analysis, healthcare, among others. However, these software professionals are primarily trained in computer science, engineering, or information technology and it can be challenging for them to understand the nuances of an unfamiliar complex domain without the help of a domain expert. Since financial experts, physicians, pharmacists, and other domain experts already work within constrained schedules, they are not able to make one-on-one commitments for answering domain-specific questions, often leading to delayed or failed software projects.**The proposed work will improve the productivity of software professionals working in complex domains by inventing solutions that reduce the one-on-one dependency on domain experts. This research will explore the idea of crowdsourcing domain expertise by designing and evaluating new tools and methods for harnessing knowledge from domain experts. Crowdsourcing is the process of accomplishing a task by de-composing into several micro-tasks to be completed by a community of online crowd workers or volunteers (e.g., Wikipedia, Yahoo Answers, and Amazon's Mechanical Turk are crowdsourcing systems). Unlike existing services that largely decompose tasks for the masses, the proposed research will invent micro-task environments for domain experts who have deep subject-matter knowledge, expert insights, and in many cases, doctoral degrees or decades of experience. Furthermore, this research will also invent approaches for embedding these crowdsourcing services within the tools used by software professionals to facilitate the contextual retrieval of domain-specific knowledge.**Drawing upon the fields of human-computer interaction (HCI), and software engineering, and my experience in developing crowdsourcing systems, the proposed research will have three key objectives:**1) Develop a framework for decomposing complex, domain-specific problems into micro-tasks: We will establish a framework for how we can decompose and distribute tasks to groups of domain experts in a micro-task environment. We will investigate what types of expert tasks are amenable to crowdsourcing and also empirically determine the incentives necessary for engaging domain experts in a crowdsourcing service.**2) Develop software services and infrastructure to support domain-specific crowdsourcing: We will design and implement crowdsourcing services that harness domain expertise. We will specifically look at how to develop effective systems and algorithms for task distribution and how to embed these crowdsourcing services within the tools used by software professionals. **3) Validate the crowdsourcing techniques and services through empirical studies: Although an iterative evaluation approach will be used throughout this research, a core focus will be on validating the proposed system designs with software professionals. Through empirical investigations in different domains, we will be able to determine how the long-term usage of crowdsourcing services affects the productivity of software professionals and workflows of domain experts. **The tools and findings from this research will improve the productivity of Canadian software professionals working in complex domains and, more broadly, advance the science of crowdsourcing domain expertise. Beyond supporting software professionals, this research will lay the critical groundwork for transforming other types of multidisciplinary collaborations in research and industry settings and make Canada a leader in research on crowdsourcing domain expertise.