Extending an Index-Benchmarking Framework with Non-Invasive Visualization Capability

Finding a suitable multi-dimensional index structure for a data-intensive system is not a trivial task. The QuEval framework supports users in finding the best index structure from a list of candidates. Nevertheless, if an index structure shows itself superior to other index structures most oft the times, but fails for one data set, we want to know the reason for this phenomenon. To support an understanding of deficits, a visualization of the partitioning scheme is helpful. Consequently, we propose a visualization component which interacts with QuEval without affecting the performance evaluation. Thus, we use a modern software-engineering approach based on AspectJ to support Digital Engineering of complex solutions.

[1]  William G. Griswold,et al.  An Overview of AspectJ , 2001, ECOOP.

[2]  Hans-Peter Kriegel,et al.  VisDB: database exploration using multidimensional visualization , 1994, IEEE Computer Graphics and Applications.

[3]  Rudolf Bayer,et al.  The Universal B-Tree for Multidimensional Indexing: general Concepts , 1997, WWCA.

[4]  Christos Faloutsos,et al.  Fractals for secondary key retrieval , 1989, PODS.

[5]  T. H. Merrett,et al.  A class of data structures for associative searching , 1984, PODS.

[6]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[7]  Stephen Blott,et al.  An Approximation- Based Data Structure for Similarity Search , 2006 .

[8]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[9]  Ramesh C. Jain,et al.  Similarity indexing with the SS-tree , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[10]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[11]  Christos Faloutsos,et al.  The R+-Tree: A Dynamic Index for Multi-Dimensional Objects , 1987, VLDB.

[12]  Moni Naor,et al.  Anti-persistence: history independent data structures , 2001, STOC '01.

[13]  Heidrun Schumann,et al.  A scalable framework for information visualization , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[14]  Christian S. Jensen Review - R-Trees: A Dynamic Index Structure for Spatial Searching , 1999, ACM SIGMOD Digit. Rev..

[15]  Gunter Saake,et al.  Challenges in Finding an Appropriate Multi-Dimensional Index Structure with Respect to Specific Use Cases , 2012, Grundlagen von Datenbanken.

[16]  Oliver Günther,et al.  Multidimensional access methods , 1998, CSUR.

[17]  Gonzalo Navarro,et al.  Effective Proximity Retrieval by Ordering Permutations , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Hanan Samet,et al.  Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling) , 2005 .

[19]  Matthieu Cord,et al.  High-dimensional descriptor indexing for large multimedia databases , 2008, CIKM '08.

[20]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[21]  Hanan Samet,et al.  Foundations of multidimensional and metric data structures , 2006, Morgan Kaufmann series in data management systems.

[22]  Piotr Indyk,et al.  Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.

[23]  Shin'ichi Satoh,et al.  The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.

[24]  Claus Vielhauer,et al.  Latent fingerprint detection using a spectral texture feature , 2011, MM&Sec '11.

[25]  Pierre Dragicevic,et al.  Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation , 2008, IEEE Transactions on Visualization and Computer Graphics.

[26]  A. Guttman,et al.  A Dynamic Index Structure for Spatial Searching , 1984, SIGMOD 1984.