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42nd International Conference on Computer Design

作者： Peng, Wenyu Xie, Tao Siegel, Paul H. San Diego State Univ Engn Sci Joint Doctoral Program San Diego CA 92182 USA San Diego State Univ Comp Sci San Diego CA USA Univ Calif San Diego Elect & Comp Engn La Jolla CA USA

ISBN: (纸本)9798350380415;9798350380408

The advent of byte-addressable persistent memory (PM) has led to a resurgence of interest in adapting existing dynamic hashing schemes to PM. Compared with its two wellknown peers (extendible hashing and linear hashing), spiral storage has received little attention due to its limitations. After an indepth analysis, however, we discover that it has a good potential for PM. To show its strength, we develop a persistent spiral storage called PASS (Persistence-Aware Spiral Storage), which is facilitated by a group of new/existing techniques. Further, we conduct a comprehensive evaluation of PASS on a server equipped with Intel Optane DC Persistent Memory Modules (DCPMM). Experimental results demonstrate that compared with two state-of-the-art schemes it exhibits better performance.

关键词： dynamic hashing persistent memory spiral storage key-value store indexing data structure

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Hybrid fragmentation of medical images' attributes for multidimensional indexing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第1期25卷 215-230页

作者： Safaei, Ali Asghar Tarbiat Modares Univ Fac Med Sci Dept Med Informat Tehran Iran

Medical images are growing dramatically both in quantity and application in the data era and the emerging Big data problem. Searching and finding the proper medical image among such a huge number of medical images is not possible unless using a proper medical search engine. indexing is the backend process in information retrieval systems in which documents are annotated with the index entry to be retrieved more accurately and efficiently. Most of the indexing techniques for medical images are content-based that are more complex and time consuming than the text-based ones. In this paper, a text-based medical image indexing technique is proposed that use medical images' attributes and fragments them with the hybrid fragmentation approach (that are used in distributed database design) and re-form each of such attribute fragments into a hierarchy, constructing a multidimensional index. Hybrid fragmentation approach uses both Horizontal and vertical fragmentation of medical images' attributes provided in header of medical image standard formats (such as the DICOM). Horizontal fragmentation uses values of image attributes (i.e., image content and properties dependent), whilst the vertical fragmentation uses pairwise affinity and correlation of the attributes in the application domain (i.e., application dependent). So, the proposed hybrid fragmentation approach based indexing of medical images aim to consider both the image properties and application statistics together to provide a better functionality. As the experimental performance evaluation results illustrate, the proposed multidimensional indexing can provide better precision of information retrieval rather that a single index or a set of multiple indexes, since that considers semantic relationship of the medical image's attributes via the hybrid (horizontal and vertical) fragmentation. Moreover, the hybrid fragmentations approach based indexing also outperforms the vertical fragmentation-based multidimensional medi

关键词： indexing technique Information retrieval Medical images Hybrid fragmentation indexing data structure Text-based image retrieval Distributed database design

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Multidimensional indexing technique for medical images retrieval

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INTELLIGENT data ANALYSIS 2021年第6期25卷 1629-1666页

作者： Safaei, Ali Asghar Habibi-Asl, Saeede Tarbiat Modares Univ Fac Med Sci Dept Med Informat Tehran Iran

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve t

关键词： indexing technique information retrieval medical images indexing data structure text-based retrieval clustering

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Diamond: multi-dimensional indexing technique for medical images retrieval using vertical fragmentation approach

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JOURNAL OF SUPERCOMPUTING 2021年第7期77卷 7089-7148页

作者： Safaei, AliAsghar HabibiAsl, Saeede Tarbiat Modares Univ Fac Med Sci Dept Med Informat Tehran Iran

Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions;roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set,

关键词： indexing technique Information retrieval Medical images Vertical fragmentation indexing data structure Clustering Text-based retrieval

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Compressed Cache-Oblivious String B-Tree

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ACM TRANSACTIONS ON ALGORITHMS 2016年第4期12卷 52-52页

作者： Ferragina, Paolo Venturini, Rossano Univ Pisa Dept Comp Sci Largo Bruno Pontecorvo 3 I-56127 Pisa Italy

In this article, we study three variants of the well-known prefix-search problem for strings, and we design solutions for the cache-oblivious model which improve the best known results. Among these contributions, we close (asymptotically) the classic problem, which asks for the detection of the set of strings that share the longest common prefix with a queried pattern by providing an I/O-optimal solution that matches the space lower bound for tries up to a constant multiplicative factor of the form (1 + epsilon), for epsilon > 0. Our solutions hinge upon a novel compressed storage scheme that adds the ability to decompress prefixes of the stored strings I/O-optimally to the elegant locality-preserving front coding (Bender et al. 2006) still preserving its space bounds.

关键词： Pattern matching data compression compressed index indexing data structure string dictionary

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The Compressed Permuterm Index

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ACM TRANSACTIONS ON ALGORITHMS 2010年第1期7卷 10-10页

作者： Ferragina, Paolo Venturini, Rossano Univ Pisa Dept Comp Sci I-56127 Pisa Italy ISTI CNR Pisa Italy

The Permuterm index [Garfield 19761 is a time-efficient and elegant solution to the string dictionary problem in which pattern queries may possibly include one wild-card symbol (called Tolerant Retrieval problem). Unfortunately the Permuterm index is space inefficient because it quadruples the dictionary size. In this article we propose the Compressed Permuterm Index which solves the Tolerant Retrieval problem in time proportional to the length of the searched pattern, and space close to the kth order empirical entropy of the indexed dictionary. We also design a dynamic version of this index that allows to efficiently manage insertion in, and deletion from, the dictionary of individual strings. The result is based on a simple variant of the Burrows-Wheeler Transform, defined on a dictionary of strings of variable length, that allows to efficiently solve the Tolerant Retrieval problem via known (dynamic) compressed indexes [Navarro and Makinen 2007]. We will complement our theoretical study with a significant set of experiments that show that the Compressed Permuterm Index supports fast queries within a space occupancy that is close to the one achievable by compressing the string dictionary via gzip or bzip2. This improves known approaches based on Front-Coding [Witten et al. 1999] by more than 50% in absolute space occupancy, still guaranteeing comparable query time.

关键词： Burrows-Wheeler transform compressed index indexing data structure permuterm string dictionary

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High Throughput Short Read Alignment via Bi-directional BWT

High Throughput Short Read Alignment via Bi-directional BWT

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IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009)

作者： Lam, T. W. Li, Ruiqiang Tam, Alan Wong, Simon Wu, Edward Yiu, S. M. Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Beijing Genom Inst Shenzhen Shenzhen 518083 Peoples R China

ISBN: (纸本)9780769538853

The advancement of sequencing technologies has made it feasible for researchers to consider many high-throughput biological applications. A core step of these applications is to align an enormous amount of short reads to a reference genome. For example, to resequence a human genome, billions of reads of 35 bp are produced in 1-2 weeks, putting a lot of pressure of faster software for alignment. Based on existing indexing and pattern matching technologies, several short read alignment software have been developed recently. Yet this is still strong need to further improve the speed. In this paper, we show a new indexing data structure called bi-directional BWT, which allows us to build the fastest software for aligning short reads. When compared with existing software (Bowtie is the best), our software is at least 3 times faster for finding unique best alignments, and 25 times faster for finding all possible alignments. We believe that bi-directional BWT is an interesting data structure on its own and could be applied to other pattern matching problems.

关键词： short read alignment BWT indexing data structure

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indexing compressed text

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JOURNAL OF THE ACM 2005年第4期52卷 552-581页

作者： Ferragina, P Manzini, G Univ Pisa Dipartimento Informat I-56127 Pisa Italy Univ Piemonte Orientale Alessandria Italy

We design two compressed data structures for the full-text indexing problem that support efficient substring searches using roughly the space required for storing the text in compressed form. Our first compressed data structure retrieves the occ occurrences of a pattern P [ 1, p] within a text T [1, n] in 0 (p + occ log(1+epsilon) n) time for any chosen epsilon 0 < E < 1. This data structure uses at most 5nH(k)(T) + o(n) bits of storage, where H-k(T) is the kth order empirical entropy of T. The space usage is 6(n) bits in the worst case and o(n) bits for compressible texts. This data structure exploits the relationship between suffix arrays and the Burrows-Wheeler Transform, and can be regarded as a compressed suffix array. Our second compressed data structure achieves O(p + occ) query time using O(nH(k)(T) log(epsilon) n) + o(n) bits of storage for any chosen E, 0 < E < 1. Therefore, it provides optimal output-sensitive query time using o(n log n) bits in the worst case. This second data structure builds upon the first one and exploits the interplay between two compressors: the Burrows-Wheeler Transform and the LZ78 algorithm.

关键词： algorithms design theory Burrows-Wheeler transform full-text indexing indexing data structure Lempel-Ziv compressor pattern searching suffix tree suffix array text compression

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