My primary area of research interest is distributed systems and data management.


Recent Projects


  1. BulletInteractive Data Exploration: This project is the continuation of our  Interaction History Management project. It studies techniques for assisting users in their big data exploration tasks by capturing user profiles and and leveraging them to automatically navigate users in the data space.  This work is supported an NSF award. More details can be found in our CIDR 2013 and SIGMOD 2014  papers.


  2. BulletXCloud: The project studies declarative mechanisms that allow application developers to express custom performance criteria for data processing tasks and exploits the properties of these mechanisms to design extensible resource, workload and Service-Level-Agreement (SLA) management services for cloud databases. This work is supported by an NSF Career Award.


  3. BulletJoin Optimization for Array DBs: In this project we focus on the optimization of parallel joins on skewed data sets in the context of array-based databases. More details can be found in our IEEE Data Engineering Bulletin and our SIGMOD 2015 paper.


  4. BulletEngineering Query Optimizers:  In this project we explore the design, development and evaluation of a development environment that facilitates the engineering of system-specific optimizer designs by supporting the rapid prototyping, evaluation and refinement of query optimizer components. This work is supported by an NSF award. More details can be found in our DBTest10 paper and ICDE 2014 demo description.

  5. BulletConcurrent Query Performance Prediction: We study the problems of performance prediction and workload characterization that arise within the context of concurrent DBMS workloads. More details can be found in our SIGMOD 2011 and EDBT 2014 paper.


Past Projects


  1. Bullet XPORT:  XPORT is a general-purpose infrastructure that provides the core functionalities of large-scale stream processing and dissemination applications. It can be extended to support diverse processing logic, stream types, and performance targets and, given these specifications, it automatically creates and optimizes a data stream acquisition, processing and  overlay network. Its optimization is driven by metric-independent operations, which refine the structure of the overlay network as well as efficiently distribute processing across the network.



  1. Bullet SemCast: SemCast investigates efficient content-based data filtering and dissemination over conventional multicast channels. SemCast splits input data streams into multiple pieces and spreads the pieces across multiple multicast channels for delivery. This approach eliminates the need for content-based filtering and routing at interior nodes of the overlay.



  1. Bullet Borealis: Borealis is a distributed stream processing engine developed by Brandeis University, Brown University, and MIT. It deploys a network of cooperating Borealis stream engines, distributes query processing across multiple machines, and maintains integrity and correct operation as the network is dynamically mutated.