Relative Lempel-Ziv Compression of Genomes for Large-Scale Storage and Retrieval

The availability of versatile multimedia processing software and the far-reaching coverage of the interconnected networks have facilitated flawless copying and manipulations of digital media. The ever-advancing storage and retrieval technologies also have smoothed the way for large-scale multimedia database applications. However, abuses of these facilities and technologies pose pressing threats to multimedia security management in general, and multimedia copyright protection and content integrity verification in particular. Although cryptography has a long history of application to information and multimedia security, the undesirable characteristic of providing no protection to the media once decrypted has limited the feasibility of its widespread use. For example, an adversary can obtain the decryption key by purchasing a legal copy of the media but then redistributing the decrypted copies of the original.

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Digital Watermarking for Multimedia Security Management

Encyclopedia of Multimedia Technology and Networking ◽

10.4018/978-1-59140-561-0.ch030 ◽

2011 ◽

pp. 213-218 ◽

Cited By ~ 1

Author(s):

Chang-Tsun Li

Keyword(s):

Digital Media ◽

Copyright Protection ◽

Large Scale ◽

Security Management ◽

Multimedia Security ◽

Multimedia Database ◽

Storage And Retrieval ◽

Undesirable Characteristic ◽

History Of ◽

The Media

The availability of versatile multimedia processing software and the far-reaching coverage of the interconnected networks have facilitated flawless copying and manipulations of digital media. The ever-advancing storage and retrieval technologies also have smoothed the way for large-scale multimedia database applications. However, abuses of these facilities and technologies pose pressing threats to multimedia security management in general, and multimedia copyright protection and content integrity verification in particular. Although cryptography has a long history of application to information and multimedia security, the undesirable characteristic of providing no protection to the media once decrypted has limited the feasibility of its widespread use. For example, an adversary can obtain the decryption key by purchasing a legal copy of the media but then redistributing the decrypted copies of the original.

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Large-Scale Pattern Storage and Retrieval Using Generalized Brain-State-in-a-Box Neural Networks

IEEE Transactions on Neural Networks ◽

10.1109/tnn.2010.2040291 ◽

2010 ◽

Vol 21 (4) ◽

pp. 633-643 ◽

Cited By ~ 8

Author(s):

Cheolhwan Oh ◽

Stanislaw H. Zak

Keyword(s):

Neural Networks ◽

Large Scale ◽

Brain State ◽

Storage And Retrieval

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MINIMIZING LATENCY AND JITTER FOR LARGE-SCALE MULTIMEDIA REPOSITORIES THROUGH PREFIX CACHING

International Journal of Image and Graphics ◽

10.1142/s0219467803000932 ◽

2003 ◽

Vol 03 (01) ◽

pp. 95-117 ◽

Cited By ~ 1

Author(s):

SUNIL PRABHAKAR ◽

RAHUL CHARI

Keyword(s):

Large Scale ◽

Data Access ◽

Multimedia Data ◽

Secondary Storage ◽

Storage And Retrieval ◽

Multiple User ◽

Large Size ◽

Tertiary Storage ◽

Hierarchical Storage ◽

Access Patterns

Multimedia data poses challenges for efficient storage and retrieval due to its large size and playback timing requirements. For applications that store very large volumes of multimedia data, hierarchical storage offers a scalable and economical alternative to store data on magnetic disks. In a hierarchical storage architecture data is stored on a tape or optical disk based tertiary storage layer with the secondary storage disks serving as a cache or buffer. Due to the need for swapping media on drives, retrieving multimedia data from tertiary storage can potentially result in large delays before playback (startup latency) begins as well as during playback (jitter). In this paper we address the important problem of reducing startup latency and jitter for very large multimedia repositories. We propose that secondary storage should not be used as a cache in the traditional manner — instead, most of the secondary storage should be used to permanently store partial objects. Furthermore, replication is employed at the tertiary storage level to avoid expensive media switching. In particular, we show that by saving the initial segments of documents permanently on secondary storage, and replicating them on tertiary storage, startup latency can be significantly reduced. Since we are effectively reducing the amount of secondary storage available for buffering the data from tertiary storage, an increase in jitter may be expected. However, our results show that the technique also reduces jitter, in contrast to the expected behavior. Our technique exploits the pattern of data access. Advance knowledge of the access pattern is helpful, but not essential. Lack of this information or changes in access patterns are handled through adaptive techniques. Our study addresses both single- and multiple-user scenarios. Our results show that startup latency can be reduced by as much as 75% and jitter practically eliminated through the use of these techniques.

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Large Scale Memory Storage and Retrieval (LAMSTAR) Network

Principles of Artificial Neural Networks - Advanced Series in Circuits and Systems ◽

10.1142/9789812385383_0013 ◽

1997 ◽

pp. 191-222 ◽

Cited By ~ 1

Keyword(s):

Large Scale ◽

Memory Storage ◽

Storage And Retrieval

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Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

Applied Computational Intelligence and Soft Computing ◽

10.1155/2017/9635348 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8

Author(s):

Lijuan Duan ◽

Chongyang Zhao ◽

Jun Miao ◽

Yuanhua Qiao ◽

Xing Su

Keyword(s):

Image Retrieval ◽

Large Scale ◽

Nearest Neighbor ◽

Feature Learning ◽

Index Method ◽

Storage And Retrieval ◽

Deep Hashing ◽

Indexing Method ◽

Hash Code ◽

Large Scale Image Retrieval

Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI), to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

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Massive Data Management and Sharing Module for Connectome Reconstruction

Brain Sciences ◽

10.3390/brainsci10050314 ◽

2020 ◽

Vol 10 (5) ◽

pp. 314

Author(s):

Jingbin Yuan ◽

Jing Zhang ◽

Lijun Shen ◽

Dandan Zhang ◽

Wenhuan Yu ◽

...

Keyword(s):

Data Management ◽

Data Storage ◽

Large Scale ◽

Rapid Development ◽

Massive Data ◽

Storage And Retrieval ◽

Server Side ◽

Large Scale Data ◽

Client Side ◽

Scale Data

Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability.

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