Data Integration and Real-Time Analytics for Diverse Applications at the University of Hawaii
This project, led by Prof. Lipyeow Lim and collaborating with Profs. Luz Quiroga, Dennis Streveler, and Nancy Reed, focuses on integrating databases related to homelessness services in Hawaii. It also explores energy-efficient query processing on smartphones and implements event processing for emergency notifications. Additionally, real-time data analytics for wind power management addresses energy supply challenges. The research encompasses massive scientific data warehouses emphasizing performance benchmarks and parallel database systems to handle extensive datasets from astronomical observations.
Data Integration and Real-Time Analytics for Diverse Applications at the University of Hawaii
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Presentation Transcript
ICS 499Projects Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa Lipyeow Lim -- University of Hawaii at Manoa
Data Integration & Data Warehousing • In collaboration with Profs. Luz Quiroga, Dennis Streveler, Nancy Reed. • State Dept of Human Services • Integrate databases on homeless shelter and homeless people in Hawaii. • Develop scripts, possibly web-based frontends. Lipyeow Lim -- University of Hawaii at Manoa
Energy Efficient Query Processing in Smart Phones Alerts can notify emergency services or caregiver Wearable sensors transmit vitals to cell phone via wireless (eg. bluetooth) Phone runs a complex event processing (CEP) engine with rules for alerts SPO2 ECG HR Temp. Acc. ... IFAvg(Window(HR)) > 100 ANDAvg(Window(Acc)) < 2 THEN SMS(doctor) Lipyeow Lim -- University of Hawaii at Manoa
Real-time Data Analytics for Wind Power Management • Key problem in renewable energy is the variability in supply • Demand is predictable • Accurate and continuous forecasting can help utility company balance the load • Weather forecasting algorithms in streaming mode. • Parallel programming. Lipyeow Lim -- University of Hawaii at Manoa
Scientific Data Warehouses • Massive amount of data (petabyte range) • No updates, append only • Interactive queries + long running analytical queries • Commodity clusters and/or virtualized “cloud” environment • Data-intensive vs Compute-intensive infra-structures • Setup and run benchmarks on several parallel DBMS systems • Further development and improvement on a parallel DBMS system Pan-STARRS 1 Telescope 109-pixel camera 30-second exposures > 2 TB per night 5 * 109 objects 5 * 1011 detections Lipyeow Lim -- University of Hawaii at Manoa
