The Experts below are selected from a list of 13623 Experts worldwide ranked by ideXlab platform
Yannis Manolopoulos - One of the best experts on this subject based on the ideXlab platform.
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hierarchical bitmap index an efficient and scalable Indexing Technique for set valued attributes
Advances in Databases and Information Systems, 2003Co-Authors: Mikolaj Morzy, Tadeusz Morzy, Alexandros Nanopoulos, Yannis ManolopoulosAbstract:Set-valued attributes are convenient to model complex objects occurring in the real world. Currently available database systems support the storage of set-valued attributes in relational tables but contain no primitives to query them efficiently. Queries involving set-valued attributes either perform full scans of the source data or make multiple passes over single-value indexes to reduce the number of retrieved tuples. Existing Techniques for Indexing set-valued attributes (e.g., inverted files, signature indexes or RD-trees) are not efficient enough to support fast access of set-valued data in very large databases.
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hierarchical bitmap index an efficient and scalable Indexing Technique for set valued attributes
Lecture Notes in Computer Science, 2003Co-Authors: Mikolaj Morzy, Tadeusz Morzy, Alexandros Nanopoulos, Yannis ManolopoulosAbstract:Set-valued attributes are convenient to model complex objects occurring in the real world. Currently available database systems support the storage of set-valued attributes in relational tables but contain no primitives to query them efficiently. Queries involving set-valued attributes either perform full scans of the source data or make multiple passes over single-value indexes to reduce the number of retrieved tuples. Existing Techniques for Indexing set-valued attributes (e.g., inverted files, signature indexes or RD-trees) are not efficient enough to support fast access of set-valued data in very large databases. In this paper we present the hierarchical bitmap index-a novel Technique for Indexing set-valued attributes. Our index permits to index sets of arbitrary length and its performance is not affected by the size of the indexed domain. The hierarchical bitmap index efficiently supports different classes of queries, including subset, superset and similarity queries. Our experiments show that the hierarchical bitmap index outperforms other set Indexing Techniques significantly.
Phalguni Gupta - One of the best experts on this subject based on the ideXlab platform.
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Indexing fingerprint database with minutiae based coaxial gaussian track code and quantized lookup table
International Conference on Image Processing, 2015Co-Authors: Kamlesh Tiwari, Phalguni GuptaAbstract:Large scale adaptation of a fingerprint based recognition system results in expansion of its database which increases the cost of identification and degrades the system performance. The paper proposes an efficient Indexing Technique that can scatter the effect of database escalation and maintains the system performance. It has proposed a fixed length feature vector built from each minutia, known as Coaxial Gaussian Track Code (CGTC). The proposed Technique inserts feature vector into a Quantized Lookup Table (QLT) only once. As a result, it reduces both computational and memory costs. Since minutiae of all fingerprint images in the database are found to be well distributed in the quantized lookup table, it does not need rehashing. Experiments have been conducted over three fingerprint databases viz. FVC2002, FVC2004 and IITK-Sel500FP containing fingerprints from 100, 100 and 500 subjects respectively. Results have proven the superiority of the proposed Indexing Technique against well known geometric based Indexing Techniques.
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boosted geometric hashing based Indexing Technique for finger knuckle print database
Information Sciences, 2014Co-Authors: Umarani Jayaraman, Aman Kishore Gupta, Phalguni GuptaAbstract:Abstract This paper makes use of a boosted geometric hashing to propose an efficient Indexing Technique for finger-knuckle-print (FKP) images. Local feature extractors are used to extract features from each FKP image and each feature is inserted exactly once into the hash table which reduces memory and computational cost significantly. Features of all FKP images in the database are found to be well distributed in the hash table. It has been tested on publicly available PolyU FKP database [11] which consists of 7920 FKP images of 660 subjects. Further, the Technique has been compared with a well known geometric hashing based Indexing Technique [6] and it is found to be better in terms of its performance.
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an Indexing Technique for biometric database
International Conference on Wavelet Analysis and Pattern Recognition, 2008Co-Authors: Umarani Jayaraman, Surya Prakash, Phalguni GuptaAbstract:In this paper, an efficient Indexing Technique which can be used in an identification system with large biometric database has been proposed. The Technique is based on the modified B+ tree which reduces the disk accesses and is found to be suitable for large biometric database. In this Technique, first a multi-dimensional feature vector is projected to a lower dimensional feature space. Then, reduced dimensional feature vector is used to index the database by forming modified B+ tree. The proposed method decreases the data retrieval time along with possible error rates. This system is tested on Bath university and IITK iris databases with and without dimension reduction. It is observed that the system with reduced dimension performs almost equally well.
Jing Xiao - One of the best experts on this subject based on the ideXlab platform.
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parallel Indexing Technique for spatio temporal data
Isprs Journal of Photogrammetry and Remote Sensing, 2013Co-Authors: M J Kraak, Otto Huisman, Jing XiaoAbstract:Abstract The requirements for efficient access and management of massive multi-dimensional spatio-temporal data in geographical information system and its applications are well recognized and researched. The most popular spatio-temporal access method is the R-Tree and its variants. However, it is difficult to use them for parallel access to multi-dimensional spatio-temporal data because R-Trees, and variants thereof, are in hierarchical structures which have severe overlapping problems in high dimensional space. We extended a two-dimensional interval space representation of intervals to a multi-dimensional parallel space, and present a set of formulae to transform spatio-temporal queries into parallel interval set operations. This transformation reduces problems of multi-dimensional object relationships to simpler two-dimensional spatial intersection problems. Experimental results show that the new parallel approach presented in this paper has superior range query performance than R*-trees for handling multi-dimensional spatio-temporal data and multi-dimensional interval data. When the number of CPU cores is larger than that of the space dimensions, the insertion performance of this new approach is also superior to R*-trees. The proposed approach provides a potential parallel Indexing solution for fast data retrieval of massive four-dimensional or higher dimensional spatio-temporal data.
Mikolaj Morzy - One of the best experts on this subject based on the ideXlab platform.
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hierarchical bitmap index an efficient and scalable Indexing Technique for set valued attributes
Advances in Databases and Information Systems, 2003Co-Authors: Mikolaj Morzy, Tadeusz Morzy, Alexandros Nanopoulos, Yannis ManolopoulosAbstract:Set-valued attributes are convenient to model complex objects occurring in the real world. Currently available database systems support the storage of set-valued attributes in relational tables but contain no primitives to query them efficiently. Queries involving set-valued attributes either perform full scans of the source data or make multiple passes over single-value indexes to reduce the number of retrieved tuples. Existing Techniques for Indexing set-valued attributes (e.g., inverted files, signature indexes or RD-trees) are not efficient enough to support fast access of set-valued data in very large databases.
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hierarchical bitmap index an efficient and scalable Indexing Technique for set valued attributes
Lecture Notes in Computer Science, 2003Co-Authors: Mikolaj Morzy, Tadeusz Morzy, Alexandros Nanopoulos, Yannis ManolopoulosAbstract:Set-valued attributes are convenient to model complex objects occurring in the real world. Currently available database systems support the storage of set-valued attributes in relational tables but contain no primitives to query them efficiently. Queries involving set-valued attributes either perform full scans of the source data or make multiple passes over single-value indexes to reduce the number of retrieved tuples. Existing Techniques for Indexing set-valued attributes (e.g., inverted files, signature indexes or RD-trees) are not efficient enough to support fast access of set-valued data in very large databases. In this paper we present the hierarchical bitmap index-a novel Technique for Indexing set-valued attributes. Our index permits to index sets of arbitrary length and its performance is not affected by the size of the indexed domain. The hierarchical bitmap index efficiently supports different classes of queries, including subset, superset and similarity queries. Our experiments show that the hierarchical bitmap index outperforms other set Indexing Techniques significantly.
Donghong Han - One of the best experts on this subject based on the ideXlab platform.
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a hyperplane based Indexing Technique for high dimensional data
Information Sciences, 2007Co-Authors: Guoren Wang, Xiangmin Zhou, Bin Wang, Baiyou Qiao, Donghong HanAbstract:In this paper, we propose a novel hyperplane based Indexing method to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the key dimension based index. Compared with the key dimension concept, the hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Extensive experiments based on two types of real data sets are conducted and the results illustrate a significantly improved filtering efficiency. Because of the feature of hyperplane, the proposed Indexing method is only suitable to Euclidean spaces.
