Mohamed Sarwat
Mohamed Sarwat, a computer science and engineering assistant professor, in the Ira A. Fulton Schools of Engineering, was awarded the Best Research Paper Award at the 16th IEEE International Conference on Mobile Data Management 2015.
The paper “RECATHON: A Middleware for Context-Aware Recommendation in Database Systems,” presents a multidimensional recommender system built entirely inside a database system. This paper is part of the RecDB project, which proposes a unified approach for declarative recommender systems inside the database engine. Recommender systems are becoming extremely common in recent years with programs such as Pandora using data from the Music Genome Project to seed a “station” to play music with similar properties to the requested song or artist. Netflix uses a similar method to recommend movies or television shows you may like based on your previous viewing habits.
RecDB provides an intuitive interface for application developers to build custom-made recommenders. This allows application developers to implement myriad recommendation applications quickly through a wide variety of built-in recommendation algorithms. To achieve that, RecDB extends SQL with new statements to create and/or drop recommenders. The system efficiently maintains each created recommender that is queried to generate personalized recommendations to end-users. RecDB proposes a novel-querying paradigm that allows database users to express recommendation as part of the issued SQL queries. The system then optimizes the recommendation-aware SQL query through a set of newly introduced recommendation-aware relational operators to realize a variety of popular data recommendation algorithms inside the database query processor.
Sarwat directs the Data Systems Lab at ASU (DataSys@ASU). The main mission of DataSys@ASU is to impact society by boosting education and conducting novel scientific research in the data management area. Members of DataSys@ASU design and develop data management systems that enable emerging applications such as predictive analytics, location-based services, social networking), new data types (examples; geospatial data, graph data) and new workload. The lab also develops data management systems that support new computing paradigms (examples; Cloud computing and Cluster computing) and new hardware system architectures such as flash memory storage devices.
