The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Multidimensional access methods
ACM Computing Surveys (CSUR)
Multidimensional binary search trees used for associative searching
Communications of the ACM
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Efficient Technique for Nearest-Neighbor Query Processing on the SPY-TEC
IEEE Transactions on Knowledge and Data Engineering
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Statistically rigorous java performance evaluation
Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications
Effective Proximity Retrieval by Ordering Permutations
IEEE Transactions on Pattern Analysis and Machine Intelligence
MESSIF: metric similarity search implementation framework
DELOS'07 Proceedings of the 1st international conference on Digital libraries: research and development
Latent fingerprint detection using a spectral texture feature
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
Reliable provenance information for multimedia data using invertible fragile watermarks
BNCOD'11 Proceedings of the 28th British national conference on Advances in databases
An efficient indexing method for nearest neighbor searches inhigh-dirnensional image databases
IEEE Transactions on Multimedia
Evaluation of Clusterings -- Metrics and Visual Support
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Creating and benchmarking a new dataset for physical activity monitoring
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Efficient co-processor utilization in database query processing
Information Systems
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In the recent past, the amount of high-dimensional data, such as feature vectors extracted from multimedia data, increased dramatically. A large variety of indexes have been proposed to store and access such data efficiently. However, due to specific requirements of a certain use case, choosing an adequate index structure is a complex and time-consuming task. This may be due to engineering challenges or open research questions. To overcome this limitation, we present QuEval, an open-source framework that can be flexibly extended w.r.t. index structures, distance metrics, and data sets. QuEval provides a unified environment for a sound evaluation of different indexes, for instance, to support tuning of indexes. In an empirical evaluation, we show how to apply our framework, motivate benefits, and demonstrate analysis possibilities.