II: Data Cooperatives: Rapid and Incremental Data Sharing with Applications to Bioinformatics

将开发用于创建和维护数据合作社--其目的是共享分布式数据的联合会--的通用工具和技术，以克服在共享生命科学，特别是生物信息学方面的信息方面遇到的困难。大规模数据共享的愿景一直是生物信息学领域的一个长期目标，其中大部分是通过数据集成努力进行的。然而，传统的数据集成方法不具有必要的灵活性和适应性，无法使现有和未来的大量数据对典型的生物学家可访问和使用，同时保持其快速扩展到新概念、领域和查询类型，从而促进新的研究发展。主要原因是：(1)不同的生物学家处理不同类型的数据，并处于不同的抽象级别；(2)生物信息学世界中的模式通常很大且复杂；(3)查询和映射可能会因为异步更新而在没有警告的情况下“中断”；(4)操作集中式数据集成设施在逻辑上、经济上和政治上都存在困难。作为对这些困难的回应，数据合作社强调：为了可伸缩性和灵活性的去中心化，诸如模式、映射和查询等资源的增量开发，用于找到与主题相关的资源的快速发现机制，以及对成员间歇参与和映射的大致一致性的容忍。更具体地说，该提议的技术目标包括：(1)协作开发生物主题的黄页；(2)模式模板，捕获与特定兴趣有关的数据结构的部分，并用作创建查询表格的可视模板；(3)映射的增量规范；(4)通过使用统计工具测量映射的可靠性程度来推理映射中的不确定性，并将其用于查询回答；(5)大规模数据合作社中具有缓存和复制的查询的多路径回答，其中单个成员的参与并不总是得到保证。数据合作社通过在各种科学和工业领域的应用将产生更广泛的影响，但在生物信息学领域，它们可能会产生直接和重大的影响。因此，一个特定的数据合作社作为生物试验台来评估所提出的技术。这一试验台是基于一小部分数据库，这些数据库已经在合作和交换与恶性疟原虫有关的数据。还将通过拟议的教育举措产生更广泛的影响，特别是通过“计算管弦乐队”生物信息学课程，使学生通过项目工作接触数据集成问题，以及为大费城生物信息学联盟(GPBA)举办讲习班。还将通过GPBA实习计划鼓励少数人参与。

Generic tools and technologies for creating and maintaining data cooperatives- confederations whose purpose is distributed data sharing-will be developed to overcome the difficultiess encountered in the sharing of information in life sciences, specifically in bioinformatics.The vision of large-scale data sharing has been a long-time goal of the bioinformatics field, much of it proceeding through data integration efforts. However, conventional approaches to data integration do not have the necessary flexibility and adaptability to make the existing and future plethora of data accessible and usable to typical biologists, while keeping it rapidly extensible to new concepts, domains, and types of queries, and thus fostering new research developments. The main reasons are that (1) different biologists work with different types of data and at differing levels of abstraction; (2) schemas in the bioinformatics world are typically large and complex; (3) queries and mappings may "break" without warning because of asynchronous updates; (4) it is logistically, economically and politically difficult to operate centralized data integration facilities. In response to these difficulties data cooperatives emphasize: decentralization for both scalability and flexibility, incremental development of resources such as schemas, mappings, and queries, rapid discovery mechanisms for finding the resources relevant to a topic, and tolerance for intermittent participation of members and for approximate consistency of mappings.More specifically, the technical goals of the proposal include: (1)collaboratively developed yellow pages of biological topics; (2) schema templates, capturing the part of the structure of data pertaining to a specific interest and functioning also as visual templates from which a query form created; (3) incremental specification of mappings; (4) reasoning about uncertainty in mappings by measuring with statistical tools their degree of reliability and using it in query answering; (5) multi-path answering for queries with caching and replication in a large-scale data cooperative where the participation of individual members may not always be assured.Data cooperatives will have broader impact through applications in a variety of scientific and industrial fields, but it is in the field of bioinformatics that they are likely to have an immediate and significant impact. Therefore, a specific data cooperative as a biological testbed for evaluating the proposed technologies. This testbed is based on a small set of databases which are already collaborating and exchanging data related to Plasmodium falciparum. Broader impact will be also be achieved through the proposed educational initiatives, specifically through a "compu-tational orchestra" bioinformatics course which will expose students to data integration issues through project work, and a workshop for the Greater Philadelphia Bioinformatics Alliance (GPBA). Minority involvement will also be encouraged through a GPBA internship program.