A User-centric Architecture for Ad-hoc Analytical Processing


Arnab Nandi (Principal Investigator)

Spyros Blanas (Co-Principal Investigator)

Niranjan Kamat (Graduate Student)

Feilong Liu (Graduate Student)

Supported by

NSF Award III: Small: A User-centric Architecture for Ad-hoc Analytical Processing
September 1, 2014 — August 31, 2017 (Estimated)
This material is based upon work supported by the National Science Foundation under grant No. (1422977).
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


Given the growing demand for interactive data exploration using cloud-based infrastructure, the PIs propose a user-centric architecture designed specifically for cost-effective, ad-hoc, real-time analytical processing. The proposed architecture will use answer approximation techniques and distributed in-memory query evaluation to offer a fluid data interaction experience to the end user. The user-centric interaction paradigm has the potential to revolutionize future data processing platforms, and rapidly accelerate data-driven discovery and decision making in areas such as business management, bioinformatics, astronomy, finance, and more.

The PIs propose a novel interactive faceted exploration paradigm for analyzing multi-dimensional data cubes. By tightly integrating faceted exploration of data cubes within a distributed, main-memory interactive query engine, this project investigates three complementary avenues: cube exploration models geared towards interaction, query evaluation strategies for interactive main-memory online analytical processing (OLAP), and distributed architectures for parallel analysis that trade accuracy to meet user-defined response time constraints. The PIs will investigate a unique combination of techniques such as speculative execution, shared scans and sampling to serve user-centric aspects of OLAP workloads. The simultaneous design of faceted exploration models and underlying database engine primitives allows data exploration tasks to be performed in an intuitive and interactive manner, while leveraging modern hardware features for performance. By focusing on the user, this project will rethink aspects of database architectures for interactively exploring large datasets.