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

科学变得越来越数据密集。 随着实验数据开始在机构内部或跨机构积累，随着时间的推移，它确实是一个宝贵的信息财富。 然而，很少有人做查询数据存储库中的一个集成的方式，主要是因为不同的重复实验的结果往往表现出很大程度的不一致性和方差。 该数据库在数据类型、查询类型和分布精度等方面都提出了独特的挑战，该项目的总体目标是解决这样一个数据库中的一些主要查询处理挑战，具体技术包括：（1）将top-k查询应答与top-k元组得分分布计算相耦合，以及该框架在许多上下文中的新颖用法;（2）对影响用户的概率分布的准确性进行度量？（3）针对不确定数据的连接查询提出了新的语义和高效的查询处理方法;设计了一套用于在线监测查询的近似子串匹配和窗口子序列匹配算法，实现了有效准确的共享、查询、对各种科学实验数据进行大规模的监测，将使科学界受益匪浅。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。 教育计划包括教授计算机科学专业的学生管理科学数据和科学专业的学生相关的数据库技术，吸引中学生和大学生进入计算机科学，并激励代表性不足的群体的学生。

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.