III:Small: Outlier Discovery Paradigm

III：小：异常值发现范式

基本信息

批准号：
1910880
负责人：
Elke Rundensteiner
金额：
$ 49.96万
依托单位：
Worcester Polytechnic Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910880&HistoricalAwards=false
关键词：
III Small Outlier Discovery Paradigm

项目摘要

Staggering volumes of data sets collected by modern applications from financial transaction systems, smart health sensors, and Internet of Things devices contain critical insights from rare phenomena to anomalies indicative of financial fraud, health alerts to system failure, respectively. To decipher the valuables from the counterfeit, analysts need to interactively sift through and explore the data deluge. By discovering anomalies, analysts may detect financial fraud, identify behavior irregularities, or prevent catastrophic sensor failures, thus touching the lives of citizens in countless ways. While a treasure trove of stand-alone algorithms for detecting particular types of outliers exists, they tend to be variations on a theme. This research project is game-changing in that it will offer the first end-to-end outlier services that bring this wealth of algorithms to bear in an integrated infrastructure to support effective anomaly discovery. The broader impact of this project also includes: the integration of the PI's project activities with the training of a STEM workforce; impacting the PI's WPI REU data science summer site; and impacting the new interdisciplinary degree programs from PhD, MS to BS in Data Science spearheaded and led by the PI. The PI has a long history of working with diverse student populations at all levels and is determined to similarly foster diversity of the participants involved in this project.This research will go well beyond developing yet another outlier detection algorithm by instead demonstrating the feasibility of outlier discovery as a service. It will break fundamentally new ground in supporting outlier discovery from identification, refinement to explanation. The proposed end-to-end anomaly discovery paradigm will support all stages of anomaly discovery by seamlessly integrating outlier-related services within one integrated platform. The result is a database-system inspired solution that models services as first class citizens for the discovery of outliers. It integrates outlier detection processes with data sub-spacing, explanations of outliers with respect to their context in the original data set, user feedback on the relevance of outlier candidates in the domain, and metric-learning to refine the effectiveness of the outlier detection process. Evaluation using outlier benchmark data sets and real-world data sets and workloads explored in partnerships with collaborators from industry will be conducted to establish the utility of the innovation. The resulting system will enable the analyst to steer the discovery process with human ingenuity, empowered by near real-time interactive responsiveness of the platform during exploration. Our solution aims to be the first to achieve the power of sense-making afforded by outlier explanation services and human feedback integrated into the discovery process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由金融交易系统，智能健康传感器和物联网设备收集的现代应用程序收集的大量数据集包含从稀有现象到异常的关键见解，表明财务欺诈，健康警报到系统故障。为了使贵重物品从伪造者中窃取，分析师需要进行交互性筛选并探索数据洪水。通过发现异常，分析师可以发现财务欺诈，确定行为违规行为或防止灾难性传感器失败，从而以无数方式触及公民的生活。尽管存在用于检测特定类型异常值的独立算法的宝库，但它们往往是主题的变化。该研究项目正在改变游戏规则，它将提供首个端到端的离群服务，从而使这些算法拥有丰富的算法在集成的基础架构中，以支持有效的异常发现。该项目的更广泛影响还包括：通过培训STEM劳动力的PI项目活动的整合；影响PI的WPI REU数据科学夏季网站；并影响了由PI带头和领导的数据科学领域的新的跨学科学位课程。 PI在各个层面上与各种学生人群合作有悠久的历史，并决心促进参与该项目的参与者的多样性。这项研究将不仅仅是开发另一种异常检测算法，而是通过证明异常发现的可行性作为服务。它将从根本上破裂，从而支持离群发现从识别，改进到解释。拟议的端到端异常发现范式将通过将与异常相关的服务无缝集成在一个集成平台中，以支持异常发现的所有阶段。结果是一个受数据库系统启发的解决方案，该解决方案将其建模为一流公民，以发现异常值。它将异常检测过程与数据子间距集成在一起，在原始数据集中对其上下文的异常值的解释，用户对域中异常候选者相关性的反馈以及指标学习以完善异常检测过程的有效性。将使用与行业合作者合作探索的基准数据集以及现实世界中的数据集以及现实世界数据集的评估和工作负载，以建立创新的实用性。最终的系统将使分析师能够以人类创造力来引导发现过程，并在探索过程中平台的近乎实时互动响应能力赋予了能力。我们的解决方案旨在成为第一个旨在实现“异常解释服务”和人类反馈所提供的感知力的力量。该奖项反映了NSF的法定任务，并认为值得通过基金会的智力优点和更广泛的影响来通过评估来支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Continuously Adaptive Similarity Search

DOI：
10.1145/3318464.3380601
发表时间：
2020-05
期刊：
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
影响因子：
0
作者：
Huayi Zhang;Lei Cao;Yizhou Yan;S. Madden;Elke A. Rundensteiner
通讯作者：
Huayi Zhang;Lei Cao;Yizhou Yan;S. Madden;Elke A. Rundensteiner

ELITE: Robust Deep Anomaly Detection with Meta Gradient

ELITE：使用元梯度进行稳健的深度异常检测

DOI：
10.1145/3447548.3467320
发表时间：
2021
期刊：
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
影响因子：
0
作者：
Zhang, Huayi;Cao, Lei;VanNostrand, Peter;Madden, Samuel;Rundensteiner, Elke A.
通讯作者：
Rundensteiner, Elke A.

AutoOD: Automatic Outlier Detection