Welcome to the home page of the Data Management Research Group at Brown University's Department of Computer Science. Our research group is focused on a wide-range of problem domains for database management systems, including analytical (OLAP), transactional (OLTP), and scientific workloads.

Latest News

The Brown Data Management Research Group has the following paper in HotCDP 2012 from the C-MR Project:

• Managing Parallelism for Stream Processing in the Cloud
     Nathan Backman, Rodrigo Fonseca, Ugur Cetintemel

  We present a framework that parallelizes and schedules workflows of stream operators, in real-time, to meet latency objectives. It supports data- and task-parallel processing of all workflow operators, by all computing nodes, while maintaining the ordering properties of sorted data streams. We show that a latency-oriented operator scheduling policy coupled with the diversification of computing node responsibilities encourages parallelism models that achieve end-to-end latency-minimization goals. We demonstrate the effectiveness of our framework with preliminary experimental results using a variety of real-world applications on heterogeneous clusters.

The Brown Data Management Research Group has the following paper in SIGMOD 2012 from the H-Store Project:

• Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems
    Andrew Pavlo, Carlo Curino, Stanley Zdonik

  We present a novel approach to automatically partitioning databases for enterprise-class OLTP systems that significantly extends the state of the art by: (1) minimizing the number distributed transactions, while concurrently mitigating the effects of temporal skew in both the data distribution and accesses, (2) extending the design space to include replicated secondary indexes, (4) organically handling stored procedure routing, and (3) scaling of schema complexity, data size, and number of partitions. This effort builds on two key technical contributions: an analytical cost model that can be used to quickly estimate the relative coordination cost and skew for a given workload and a candidate database design, and an informed exploration of the huge solution space based on large neighborhood search. To evaluate our methods, we integrated our database design tool with a high-performance parallel, main memory DBMS and compared our methods against both popular heuristics and a state-of-the-art research prototype.

The Brown Data Management Research Group has the following paper in EDBT 2012:

• Optimizing Index Deployment Order for Evolving OLAP
    Hideaki Kimura, Carleton Coffrin, Alexander Rasin, Stanley Zdonik

  Many database applications need hundreds or thousands of indexes to speed up query execution, making performance tuning a difficult task for database administrators. The well-known problem of index selection is to automatically design an optimal set of indexes on behalf of DBAs. This paper brings a new perspective to the problem with a scalable and extensible solution. We study the problem of optimizing the order of index creation to achieve prompt query runtime improvements and also reduce the index deployment time. We found that traditional approaches, such as A* search and MIP, are not suitable for this problem. Instead, we demonstrate that Constraint Programming is an efficient and flexible solution for this problem and its future extension. Our experimental results show that our pruning techniques can reduce the size of the search space by many orders of magnitude, and we verify that our local search algorithm based on CP is a highly scalable and stable method for quickly finding the best known solutions.