III: Small: Provisioning for Autonomous Data Analysis and Scenario Exploration

III：小型：提供自主数据分析和场景探索

• 批准号: 1217798
• 负责人: Val Tannen
• 金额: $ 50万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217798&HistoricalAwards=false
• 关键词: III; Small; Provisioning; Autonomous; Data

In business intelligence and data-driven science, users often wish to consider various "what-if scenarios": hypothetical updates and query refinements. Unfortunately current techniques do not support this type of exploration when query answers have high latency, when data sources charge fees, or in mobile, disconnected settings. This project considers how, given a particular set of query aspects and updates, one can precompute a special data representation that can be stored on a client machine and can be used to directly answer queries under a variety of updates and what-if scenarios, without direct access to the data sources. The project achieves this goal by developing "provisioned representations" that capture a form of parameterized data instances, from which complex queries (even with multiple levels of aggregation) can be answered. The work develops: (1) the provisioned representation (PR) formalism, (2) means of encoding and storing PRs, (3) a query system and query processing techniques for PRs, (4) a "wizard" for generating PRs from parameterized scenarios, and (5) interactive tools for exploring changes to data and queries over PRs. The work will improve decision support systems and help enable "information foresight" - the ability to provide, given a question, answers that include additional data relevant to a user's interests. The project supports Ph.D. students, and also develops a new course on networked information management for the University of Pennsylvania's innovative Market and Social Systems Engineering undergraduate degree program on networks and markets. Data and code will be disseminated through public collaborative portals (GitHub, Google Code) and the project Web site (https://dbappserv.cis.upenn.edu/home/?q=node/173).

在商业智能和数据驱动科学中，用户通常希望考虑各种“假设场景”：假设更新和查询细化。 不幸的是，当查询答案具有高延迟、当数据源收费或在移动、断开连接的设置中时，当前技术不支持这种类型的探索。 该项目考虑如何在给定一组特定的查询方面和更新的情况下，预先计算可以存储在客户端计算机上的特殊数据表示形式，并可用于在各种更新和假设场景下直接回答查询，而无需直接访问数据源。 该项目通过开发“预配置表示”来实现这一目标，该表示捕获某种形式的参数化数据实例，从中可以回答复杂的查询（即使具有多个聚合级别）。 这项工作开发了：(1) 预配置表示 (PR) 形式，(2) 编码和存储 PR 的方法，(3) PR 的查询系统和查询处理技术，(4) 用于从参数化场景生成 PR 的“向导”，以及 (5) 用于探索数据更改和 PR 查询的交互式工具。 这项工作将改进决策支持系统，并帮助实现“信息预见”——针对给定的问题提供包含与用户兴趣相关的附加数据的答案的能力。 该项目支持博士。学生，还为宾夕法尼亚大学创新的市场和社会系统工程网络和市场本科学位课程开发了网络信息管理新课程。 数据和代码将通过公共协作门户（GitHub、Google Code）和项目网站（https://dbappserv.cis.upenn.edu/home/?q=node/173）传播。