Query Processing

Basic data for this project

Type of project: Own resources project
Duration: since 01/04/2014

Description

Given a very large database, how to process queries efficiently? While the sequential scan is one of the most obvious solutions for small-to-moderate databases, it becomes practically infeasible when the database size grows. Concomitant with the volume and velocity of data, databases are frequently endowed with a complex distance-based similarity model that supports content-based data access in an adjustable and adaptive manner. Typical for many adaptive similarity models is an at least quadratic computation time complexity for a single distance evaluation between two data objects. The search for the most query-like data objects thus requires efficient and scalable query processing algorithms. These algorithms optimize access to the data stored in the underlying database by making use of similarity model approximations and additional index structures with the ultimate aim to support higher-level retrieval and mining algorithms for complex analytical tasks. The objective of this research project is to develop and investigate query processing algorithms for efficient information access in large-scale data collections. We are particularly focusing on optimal and exact, i.e. non-approximate, k-nearest-neighbor query processing and the major research question of how to mitigate the number of I/O and CPU operations.

Keywords: Query Processing