Advances in hardware, software and communication networks have enabled applications to generate data at unprecedented volume and velocity. An important type of this data are event streams generated from financial transactions, health sensors, web logs, social media, mobile devices, and vehicles. The world is thus poised for a sea-change in time-critical applications from financial fraud detection to health care analytics empowered by inferring insights from event streams in real time. Event processing systems continuously evaluate massive workloads of Kleene queries to detect and aggregate event trends of interest. Examples of these trends include check kites in financial fraud detection, irregular heartbeat in health care analytics, and vehicle trajectories in traffic control. These trends can be of any length. Worst yet, their number may grow exponentially in the number of events. State-of-the-art systems do not offer practical solutions for trend analytics and thus suffer from long delays and high memory costs. In this dissertation, we propose the following event trend detection and aggregation techniques. First, we solve the trade-off between CPU processing time and memory usage while computing event trends over high-rate event streams. Namely, our event trend detection approach guarantees minimal CPU processing time given limited memory. Second, we compute online event trend aggregation at multiple granularity levels from fine (per matched event), to medium (per event type), to coarse (per pattern). Thus, we minimize the number of aggregates – reducing both time and space complexity compared to the state-of-the-art approaches. Third, we share intermediate aggregates among multiple event sequence queries while avoiding the expensive construction of matched event sequences. In several comprehensive experimental studies, we demonstrate the superiority of the proposed strategies over the state-of-the-art techniques with respect to latency, throughput, and memory costs.

Creator

• Poppe, Olga

Contributors

• Dougherty, Daniel J.
• Maier, David
• Eltabakh, Mohamed Y.
• Rundensteiner, Elke A.

Degree

• PhD

Unit

• Computer Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-010518-180452

Keyword

• Complex Event Processing
• algorithms
• Query Optimization
• Data streaming

Advisor

• Rundensteiner, Elke A.

Committee

• Maier, David
• Dougherty, Daniel J.
• Eltabakh, Mohamed Y.

Defense date

• 2017-11-27

Year

• 2018

Date created

• 2018-01-05

Resource type

• Dissertation

Rights statement

• In Copyright

Last modified

• 2023-09-19