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Feng Yu
Youngstown State University
Youngstown, Ohio

Feng “George” Yu is currently an assistant professor in the Department of Computer Science and Information Systems at Youngstown State University, Youngstown, OH, where he founded a research lab, Data Lab, devoted to the research of data-oriented sciences. Yu’s major research interests include databases, big data, and cloud computing.

His primary research focuses are query processing and optimization for database engines. The central topic of this field is called the join ordering problem, which is the search for the optimal plan for a given join query. He uses statistical sampling methods to develop data structure summaries, which can provide accurate query size estimations to improve the query plan generation of database query optimizers. His methods feature higher computational efficiency and accuracy. In addition, his work can also be applied in another related field, called approximate query processing, for fast query result estimation with controlled accuracy.

Yu’s second research focus lies in NoSQL for big data. Write optimization of column-store databases, which compose a major category of NoSQL, is a well-known challenge in this field. Instead of in-memory scenarios, he focuses on out-of-core scenarios and developed a new data structure, time-stamped binary association tables, and several data cleaning methods to hasten the writing operations on column-store databases.

His research interest in cloud computing lies in a new cloud architecture called heterogeneous cloud. Different from a hybrid cloud, which combines public and private clouds, a heterogeneous cloud features the combination of both cloud computing services and big data services on the same cluster for better resource utilization and cost efficiency. More importantly, big data services are deployed directly on the bare-metal servers in the same cloud cluster with a special infrastructure design.

Besides his work at Youngstown State University, Yu also serves as a campus champion of NSF XSEDE (Extreme Science and Engineering Discovery Environment). He is active in promoting supercomputing resources in XSEDE to regional researchers to assist their research. He is also enthusiastic in cultivating the next-generation specialists of big data and data-centric computing. Collaborating with XSEDE and Pittsburgh Supercomputing Center, Yu has been continuously organizing regional students, staff, and faculty to attend national workshops on high-performance computing since 2014.

Yu received his PhD (2013) in Computer Science at Southern Illinois University, Carbondale, IL. He received his MS (2008) in Pure Mathematics at Shandong University and BS (2005) in Information and Computation Science at Northeastern University, both in P.R. China. His research work led to a number of publications in leading international conferences including SIGMOD, EDBT, DEXA, and IEEE Big Data Congress.

For more information of Yu’s lab and research work, please visit his homepage.


Querying graphs
Bonifati A., Fletcher G., Voigt H., Yakovets N., Morgan&Claypool Publishers, San Rafael, CA, 2018. 184 pp.  Type: Book (978-1-681734-30-9)

Graph data management (GDM) has become an increasingly important discipline, both in academia and in industry. One reason is that graph data has been ubiquitously created, collected, and released for analysis and learning. Every day, m...


Spatial network big databases: queries and storage methods
Yang K., Shekhar S., Springer International Publishing, New York, NY, 2017. 101 pp.  Type: Book (978-3-319566-56-6)

Spatial network big data (SNBD) can be widely found in every corner of modern society. Examples include temporally detailed road maps that provide car speeds every minute for every road segment and GPS trace data from our cell phones. ...


Data mining for social robotics: toward autonomously social robots
Mohammad Y., Nishida T., Springer International Publishing, New York, NY, 2016. 328 pp.  Type: Book (978-3-319252-30-8)

This comprehensive work focuses on human-robot interaction (HRI) using data mining and time series analysis. There are two major objects in this vast field: autonomy and sociality. The author provides a concrete introduction to the the...


Big data science & analytics: a hands-on approach
Bahga A., Madisetti V., VPT, Johns Creek, GA, 2016. 542 pp.  Type: Book (978-0-996025-54-6)

This book provides rich knowledge on big data analytics from theory to practical applications. Readers can get a sense of this from its subtitle. The book is organized in three parts....


Next generation databases: NoSQLand big data
Harrison G., Apress, New York, NY, 2015. 235 pp.  Type: Book (978-1-484213-30-8)

In this big data era, numerous new techniques are emerging every day. It’s difficult to find a good book focusing on big data from a database aspect. Even inside the database community, it’s not easy to find such a ...


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