III: Medium: Enabling Technologies for 21st Century Entity Matching Applications

实体匹配（EM）决定不同的数据实例（例如“UW-麦迪逊”和“威斯康星大学麦迪逊”）是否引用相同的真实世界实体。这个问题对于许多大数据和数据科学应用至关重要。该项目将为EM开发解决方案和工具，这将大大推进最先进的技术水平。与当前的解决方案相比，拟议的解决方案将考虑整个EM管道，将由外行用户（如领域科学家和记者）使用，将扩展到大量数据，并将利用众包来最大限度地提高匹配精度。该项目开发的技术将在三个领域进行评估：湖沼学（湖泊和其他淡水水体的研究）的科学数据管理，WalmartLabs的电子商务产品匹配，以及约翰逊控制公司的楼宇互联网开发。因此，该项目将促进EM工具的广泛部署，从而为社会带来更有效的信息管理和访问。通过发布开源软件，它将有助于教育下一代工人和研究人员。这项研究将帮助湖泊学领域的科学家，并可能通过与约翰逊控制和沃尔玛实验室的合作，影响数十万建筑物和数百万用户。最后，将在研究界广泛传播计划中的教科书和该项目的开放源码系统产品，以大大加强研究和教育的数据管理基础设施，该项目将介绍概念和技术创新。从概念上讲，该项目不只是关注匹配步骤（并研究如何准确和可扩展地匹配，正如目前的大部分工作所做的那样），而是倡导为整个原始数据匹配EM管道开发解决方案。此外，它提倡使用匹配步骤来指导EM管道中剩余步骤的执行。从技术上讲，该项目将为EM管道中的非匹配步骤开发新的解决方案，并以匹配驱动的方式进行。并介绍了新的重要问题，如EM调试。最后，它开发了新的解决方案，以扩大整个EM管道并利用众包。如上所述，该项目采取了EM研究的下一个逻辑步骤，可以显着推进最先进的技术水平。此外，这项研究的许多问题与其他数据管理方案具有共性。因此，这项研究也有可能为这些领域做出贡献。有关更多信息，请访问该项目的主页https://sites.google.com/site/anhaidgroup/projects/nsf-em-project-2016。

Entity matching (EM) decides if different data instances (such as "UW-Madison" and "Univ of Wisc Madison") refer to the same real-world entity. This problem is critical for numerous Big Data and data science applications. This project will develop solutions and tools for EM which will significantly advance the state of the art. Compared to the current solutions, the proposed solutions will consider the entire EM pipeline, will be usable by lay users (such as domain scientists and journalists), will scale to large amounts of data, and will exploit crowdsourcing to maximize matching accuracy. Technologies developed in this project will be evaluated in three domains: managing scientific data for limnology (the study of lakes and other bodies of freshwater), product matching for e-commerce at WalmartLabs, and developing the Internet of Buildings at Johnson Control Inc. As such, the project will facilitate the widespread deployment of EM tools, thus resulting in more effective information management and access for society. Through its release of open-source software it will help educate next-generation workers and researchers. The research will help domain scientists in limnology, and can potentially impact hundreds of thousands of buildings and millions of users via collaboration with Johnson Controls and WalmartLabs. Finally, a planned textbook and open-source system artifacts from the project will be disseminated broadly in the research community, to significantly enhance the data management infrastructure for research and education.The project will introduce both conceptual and technical novelties. Conceptually, instead of focusing on just the matching step (and studying how to match accurately and scalably, as much of current work has done), the project advocates developing solutions for the entire raw-data-to-matches EM pipeline. Further, it advocates using the matching step to guide the execution of the remaining steps in the EM pipeline. Technically, the project will develop novel solutions for non-matching steps in the EM pipeline, and do so in a matching-driven fashion. It also introduces new important problems, such as EM debugging. Finally, it develops novel solutions to scale up the entire EM pipeline and to exploit crowdsourcing. As described, the project takes the next logical step in EM research, and can significantly advance the state of the art. Further, many problems underlying this research have commonalities with other data management scenarios. Hence, the research has the potential to contribute to those areas as well. For more information, see the project's homepage at https://sites.google.com/site/anhaidgroup/projects/nsf-em-project-2016.