Call for Papers

The last decade has witnessed an explosive growth in database engines optimized for main memory based execution. With the current size and cost of DRAM, many analytic and transactional data sets now fit completely in memory, resulting in a vastly different set of design tradeoffs for databases compared to their earlier disk-based counterparts. There is no disk IO to overlap computation with in these systems, and they are therefore highly sensitive to CPU performance, memory bandwidth, cache sizes, network latency, etc.

The 1st annual Accelerating In-Memory Databases (AIMD) workshop aims to bring together researchers and practitioners in the area of building novel acceleration technologies for inmemory databases, for analytics, OLTP, hybrid workloads and other emerging use cases including IoT, machine learning, geo-spatial and graph applications.

The workshop invites researchers and practitioners to submit papers covering research challenges, novel ideas and methodologies that can advance the state-of-the-art in accelerating main memory data management systems.

Topics of Interest

Relevant topics include, but are not limited to, the following: