NSF Org:	IIS Division of Information & Intelligent Systems
Recipient:	WASHINGTON STATE UNIVERSITY
Initial Amendment Date:	August 26, 2016
Latest Amendment Date:	August 26, 2016
Award Number:	1633629
Award Instrument:	Standard Grant
Program Manager:	Sylvia Spengler sspengle@nsf.gov  (703)292-7347 IIS  Division of Information & Intelligent Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2016
End Date:	August 31, 2019 (Estimated)
Total Intended Award Amount:	$321,678.00
Total Awarded Amount to Date:	$321,678.00
Funds Obligated to Date:	FY 2016 = $321,678.00
History of Investigator:	Yinghui Wu (Principal Investigator) yinghuiwu.ed@gmail.com
Recipient Sponsored Research Office:	Washington State University 240 FRENCH ADMINISTRATION BLDG PULLMAN WA  US  99164-0001 (509)335-9661
Sponsor Congressional District:	05
Primary Place of Performance:	Washington State University 355 Spokane St. Pullman WA  US  99164-2752
Primary Place of Performance Congressional District:	05
Unique Entity Identifier (UEI):	XRJSGX384TD6
Parent UEI:
NSF Program(s):	Info Integration & Informatics, Big Data Science &Engineering
Primary Program Source:	01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7433, 8083
Program Element Code(s):	736400, 808300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Association analysis is a fundamental problem in Big Data analytics. Emerging applications require computationally efficient association models and scalable association mining techniques to find regularities of graph data. Conventional association analysis for transactional data is hard or infeasible to be adapted to effectively support the next generation of graph data analytics, especially under limited computing resources. In this project, the PIs develop models, algorithms and tools to support association analysis over large-scale graph data under resource constraints. The project formulates new variants of the conventional association model that are enhanced by advanced capability of graph queries. Both exact and approximate querying and mining paradigms are explored to support effective association analysis over multi-source, large-scale, and fast-changing graph data. The PIs instantiate the generic framework to two practical association analysis scenarios, notably, a) multi-graph association analysis, and b) association detection over graph streams. The project develops a package of distributed and stream association mining techniques supported by the proposed generic model and algorithms.

The enhanced model and algorithms enable scalable association analysis in a wide range of massive data applications. The principles learned from this project can be applied to big data analytics and system design in general. The study of new association analysis framework has immediate applications in emerging areas, including data quality, affinity marketing, and network security. Application collaborators of the project include Pacific Northwest National Laboratory, LogicMonitor, and Facebook. Broader impacts of the project also include research training and education of students including women and minorities, and design of new curricula and education tools that target both CS and non-CS students.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The outcome of this project includes the following. (1) Developed a general graph association model and its variants and implementations for various practical information networks. (2) Devised and developed a package of feasible and computationally efficient association detection algorithms, and their specifications and optimization techniques for dynamic and streaming networks. (3) We have also developed a new spatiotemporal graph association model, and have developed three prototype association analysis tools based on our graph association models and algorithms to three practical applications: smart grid resiliency, knowledge exploration, and smart irrigation. 

The project outcomes has identified feasible association detection and analysis techniques in richy-attributed, dynamic information networks, and has provided a complexity hierarchy of association analysis for different rule models. The funded group has produced in total more than 25 publications, including 5 SIGMOD, 2 VLDB, 5 ICDE, 1 KDD), and 6 journal papers. These results has been demonstrated at NSF workshop on several NSF workshops including NSF workshop on Real Time Data Analytics for the Power Grid Resiliency. The outcome of the project has received multiple awards including ACM research highlight award, SIGMOD best paper award, and VLDB best demo award. The PI has received VLDB distinguished reviewer award. 

The project has supported 2 PhD students. Both will graduate in Spring 2020. The project has led to two new data science courses, both at undergraduate level and are welcomed by students from CS, biology, finance, Engineering, and social science. The project has also promoted multidisciplinary collaboration including partners such as Siemens, Amazon, Microsoft, and applications in smart grid, smart agriculture and social recommendation. 

 

Last Modified: 01/05/2020
Modified by: Yinghui Wu

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