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15.5 - Online Mining for Association Rules and Collective Anomalies in Data Streams

Project - Summary


The objective of this project is to develop an online mining framework for data streams. To accomplish this, we (a) developed scalable algorithms for processing large volumes of data for the mining of association rules over time frames, which address issues with data processing latency that results in data depreciating in value, (b) developed distributed batch processing algorithms for building a model using high-volume historical data available, © developed online distributed streams processing algorithms for continuously comparing fast incoming data with the model, and evaluating them to detect collective anomalies.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Jian Chen PI Not available Not available UL Lafayette
Jennifer Lavergne Co-PI Not available Not available UL Lafayette
Ryan Benton Co-PI Not available Not available University of South Alabama
Shaaban Abbady Graduate Student Not available Not available UL Lafayette
Cheng-Yuan Ke Graduate Student Not available Not available UL Lafayette

Project - Impact and Uses/Benefits

Online mining of associations and collective anomaly detection is practically so important because it enables decision makers to take actions to prevent losses or take advantage and make profits on the right time before the value of information deprecates. This project provides designs and implementations of online mining framework that is designed specifically for big data. It is designed to be general and can be applied in various application domains with minimal customizations.

This system can be beneficial in several applications such as in intrusion detection, epidemiology, click analysis, law enforcement, anti-terrorism, marketing (emerging markets, cross-selling, and advertisement optimization), etc.

Project - Deep Dive

Project - Documents

FilenameFilesizeLast modified
15.5_year_4_presentation.pptx2.5 MiB2019/08/22 11:50
15.5_year_4_ip_letter_combined.pdf371.0 KiB2019/08/22 11:50
15.5_year_4_quad_chart.pptx1.4 MiB2019/08/22 11:50
15.5_year_4_executive_summary.pdf155.1 KiB2019/08/22 11:50
15.5_year_4_final_report.pdf1.4 MiB2019/08/22 10:33
projects/year4/15.5.txt · Last modified: 2019/08/22 10:35 by sally.johnson