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

在商业智能和数据驱动的科学中，用户通常希望考虑各种“假设场景”：假设更新和查询优化。 不幸的是，当前技术在查询答案具有高等待时间时、在数据源收费时或者在移动的断开连接的设置中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，预先计算一个特殊的数据表示，该数据表示可以存储在客户端机器上，并可以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，这些表示捕获一种形式的参数化数据实例，可以从这些实例中回答复杂的查询（即使具有多级聚合）。 工作发展：（1）规定的表示（PR）形式体系，（2）编码和存储PR的手段，（3）用于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PR上的数据和查询的变化的交互式工具。 这项工作将改善决策支持系统，并有助于实现“信息预见”-提供一个问题的答案的能力，其中包括与用户兴趣相关的额外数据。 该项目支持博士学位。学生，并制定了一个新的网络信息管理课程，为宾夕法尼亚大学的创新市场和社会系统工程本科学位课程的网络和市场。 数据和代码将通过公共协作门户（GitHub、Google Code）和项目网站（https：//www.example.com）传播dbappserv.cis.upenn.edu/home/? q=节点/173）。

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).