CAREER: MUSE: An Integrated Approach to Managing Uncertain Scientific Experimental Data

科学正变得越来越数据密集型。随着实验数据开始在机构内部或跨机构积累，并随着时间的推移，它确实是一笔宝贵的信息财富。然而，很少有人以集成的方式查询数据存储库，主要是因为实验的不同重复之间的结果往往显示出很大程度的不一致和差异。该数据库在数据类型、查询类型和分布精度等方面提出了独特的挑战。该项目的总体目标是解决此类数据库中的一些主要查询处理挑战。实现这一目标的具体技术包括：(1)将top-k查询回答与top-k元组得分分布的计算相结合，并在多个上下文中新颖地使用该框架；(2)测量影响用户S对查询结果感知的概率分布的精度，并为决策设计新的谓词；(3)针对不确定数据上的连接查询，提出新颖的语义和高效的查询处理；(4)设计了一套用于在线监测查询的近似子串匹配和加窗子序列匹配算法，能够高效、准确地大规模共享、查询和监控各种科学实验数据，将极大地造福于科学界。为科学家提供的额外数据分析能力甚至可以改变他们进行研究的方式。该教育计划包括向计算机专业的学生和理科专业的学生教授科学数据管理和相关数据库技术，吸引中学生和大学生进入计算机科学，以及激励代表不足的群体的学生。

Science is becoming increasingly data intensive. As the experimental data starts to accumulate within or across institutions and over time, it is indeed a valuable wealth of information. However, little has been done to query the data repository in an integrated manner, mainly because the results among different replicates of an experiment often show a large degree of inconsistency and variance. This database poses unique challenges in data types, query types, and accuracy of distributions, etc. The broad goal of the project is to solve some major query processing challenges in such a database.Specific techniques used to achieve this goal include: (1) Coupling top-k query answering with computation of the score distribution of top-k tuples, and the novel usage of this framework in a number of contexts; (2) Measuring the accuracy of probability distributions which affects user?s perception of query results, and devising new predicates for decision making; (3) Proposing novel semantics and efficient query processing for join queries on uncertain data; and (4) Designing a suite of algorithms for approximate substring matching and windowed subsequence matching for online monitoring queries.The ability to effectively and accurately share, query, and monitor diverse scientific experimental data on a large scale will greatly benefit the science community. The extra data analysis capability provided to scientists can even change the way they conduct their research. The education plan includes teaching both computer science students on managing scientific data and science major students on relevant database techniques, attracting middle school and college students into computer science, and inspiring students of underrepresented groups.