A new QuEval paper has been accepted at RCIS 2014 issuing the challenge of managing the large amount of tailored index implementation variants: Toward variability management to tailor high-dimensional index implementations .
Multimedia data, or high-dimensional data in general, have been subject to research for more than two decades and gain momentum even more in the communication technology age. From a database point of view, the myriads of gigabyte of data pose the problem of managing these data. In this course, query processing is a challenging task due to the high dimensionality of such data. In the past, dozens of index structures for high-dimensional data have been proposed and some of them are even standard-like referenc
This is where QuEval , a framework for quantitative comparison and evaluation of high-dimensional index structures comes into play. QuEval is a Java-based framework that supports comparison of index strucutres regarding certain characteristics such as dimensionality, accuracy, or performance. Currently, the framework contains of six different index structures. However, a main focus of the framework is its extensibility and we encourage people to contribute to QuEval by providing more index structures or oth