“As an undergraduate, I initially wanted to become a high school math teacher. But, I took a programming course and found myself working on problems late into the night without even realizing the time that had gone by,” says Professor Karen Works, assistant professor in the Department of Computer and Information Science.
After having earned a master’s degree in computer science and working in the software industry for 10 years, Works was ready to move into an academic environment and continue her keen interest in research. Thus, she happily accepted a temporary teaching position in the Computer and Information Science Department at Ŕ¶Ý®ĘÓƵ in 2005. When the temporary position ended, Works continued her education, attaining a Ph.D., and rejoined the department in 2011.
“I believe that teaching students computer science involves teaching them how to approach new challenging problems, propose possible solutions, implement their solutions, and finally evaluate their solutions,” she says.
Her research is in the area of big data, sensors, and monitoring technology. One consequence of this technology is the continuous generation of massive volumes of streaming data. To support this, stream processing systems have emerged that must produce results while meeting near real-time response obligations.
However, computation-intensive processing on high velocity streams of data is challenging as stream arrival rates are often unpredictable and can fluctuate. This causes systems to be unable to process all
incoming data within a required response time. In addition, some results may be much more significant than others and the delay or neglect of producing certain significant information could result in
catastrophic consequences.
To address this critical problem of targeted prioritized processing in overloaded environments remains largely unaddressed. Works’ research explores a rich diversity of workloads, queries, and data sets, including real data streams. Put concisely, her research involves how to manage massive data, while being able to access relevant data in real or response-relevant time.
Works quotes Thomas Edison when talking about teaching students computer science. “I have not failed. I’ve just found 10,000 ways that won’t work.”
Since not all solutions proposed solve the problem they are trying address, Works says that students gain valuable insights from analyzing why a solution does not work.
“Computer science is not a spectator sport. Solving problems takes a lot of work. Memorizing lines
of code or the constructs of programming languages will not teach a student how to solve problems,” says Works. “Students need to have many opportunities to practice those skills, and need support from teachers. I hope to build good problem solving skills in my students.”
Professor Works holds a B.A. from State University of New York at Geneseo, an M.S. from Union College and a Ph.D. from Worcester Polytechnic Institute. She and Elke Rundensteiner won the 2014 Best Paper Award at the Academy of Science Big Data/Social Information/PASSAT/BioMedCom Conference for their paper entitled “Utilizing Dynamic Precedence Criteria to Ensure the Production of Critical Results from Big Data Streams.”