Towards efficient search on unstructured data

Raghuveer, Aravindan; Jindal, Meera; Mokbel, Mohamed F.; Debnath, Biplob; Du, Di

doi:10.1145/1321440.1321583

Cited by 8 publications

(8 citation statements)

References 2 publications

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“…Another type of input is multimedia, such as images, video and sound [18]. This type of data is virtually not compressible and in most cases trying to apply any compression method on it actually increases the required storage space.…”

Section: General Considerationsmentioning

confidence: 99%

High Throughput Data-Compression for Cloud Storage

Nicolae

2010

Data Management in Grid and Peer-to-Peer Systems

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Abstract. As data volumes processed by large-scale distributed dataintensive applications grow at high-speed, an increasing I/O pressure is put on the underlying storage service, which is responsible for data management. One particularly difficult challenge, that the storage service has to deal with, is to sustain a high I/O throughput in spite of heavy access concurrency to massive data. In order to do so, massively parallel data transfers need to be performed, which invariably lead to a high bandwidth utilization. With the emergence of cloud computing, data intensive applications become attractive for a wide public that does not have the resources to maintain expensive large scale distributed infrastructures to run such applications. In this context, minimizing the storage space and bandwidth utilization is highly relevant, as these resources are paid for according to the consumption. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on I/O throughput when under heavy access concurrency. Our solution builds on BlobSeer, a highly parallel distributed data management service specifically designed to enable reading, writing and appending huge data sequences that are fragmented and distributed at a large scale. We demonstrate the benefits of our approach by performing extensive experimentations on the Grid'5000 testbed.

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Section: General Considerationsmentioning

confidence: 99%

High Throughput Data-Compression for Cloud Storage

Nicolae

2010

Data Management in Grid and Peer-to-Peer Systems

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“…It either consists of text (e.g., huge collections of documents, web pages and logs [25]) or multimedia (e.g., images, video and sound [26]). While text data is known to be highly compressible, multimedia data is virtually not compressible and in most cases trying to apply any compression method on it actually increases the required storage space and generates unnecessary computational overhead.…”

Section: General Considerationsmentioning

confidence: 99%

On the Benefits of Transparent Compression for Cost-Effective Cloud Data Storage

Nicolae

2011

Transactions on Large-Scale Data- And Knowledge-Centered Systems III

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Abstract. Infrastructure-as-a-Service (IaaS) cloud computing has revolutionized the way we think of acquiring computational resources: it allows users to deploy virtual machines (VMs) at large scale and pay only for the resources that were actually used throughout the runtime of the VMs. This new model raises new challenges in the design and development of IaaS middleware: excessive storage costs associated with both user data and VM images might make the cloud less attractive, especially for users that need to manipulate huge data sets and a large number of VM images. Storage costs result not only from storage space utilization, but also from bandwidth consumption: in typical deployments, a large number of data transfers between the VMs and the persistent storage are performed, all under high performance requirements. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on data access performance. Our solution builds on BlobSeer, a distributed data management service specifically designed to sustain a high throughput for concurrent accesses to huge data sequences that are distributed at large scale. Extensive experiments demonstrate that our approach achieves large reductions (at least 40%) of bandwidth and storage space utilization, while still attaining high performance levels that even surpass the original (no compression) performance levels in several data-intensive scenarios.

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“…Application data is mostly in unstructured form. It either consists of text (e.g., huge collections of documents, web pages and logs [25]) or multimedia (e.g., images, video and sound [26]). While text data is known to be highly compressible, multimedia data is virtually not compressible and in most cases trying to apply any compression method on it actually increases the required storage space and generates unnecessary computational overhead.…”

Section: General Considerationsmentioning

confidence: 99%

Transactions on Large-Scale Data- and Knowledge-Centered Systems III

Hameurlain¹,

Küng²,

Wagner³

2011

Lecture Notes in Computer Science

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Infrastructure-as-a-Service (IaaS) cloud computing has revolutionized the way we think of acquiring computational resources: it allows users to deploy virtual machines (VMs) at large scale and pay only for the resources that were actually used throughout the runtime of the VMs. This new model raises new challenges in the design and development of IaaS middleware: excessive storage costs associated with both user data and VM images might make the cloud less attractive, especially for users that need to manipulate huge data sets and a large number of VM images. Storage costs result not only from storage space utilization, but also from bandwidth consumption: in typical deployments, a large number of data transfers between the VMs and the persistent storage are performed, all under high performance requirements. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on data access performance. Our solution builds on BlobSeer, a distributed data management service specifically designed to sustain a high throughput for concurrent accesses to huge data sequences that are distributed at large scale. Extensive experiments demonstrate that our approach achieves large reductions (at least 40%) of bandwidth and storage space utilization, while still attaining high performance levels that even surpass the original (no compression) performance levels in several data-intensive scenarios.

show abstract

Towards efficient search on unstructured data

Cited by 8 publications

References 2 publications

High Throughput Data-Compression for Cloud Storage

High Throughput Data-Compression for Cloud Storage

On the Benefits of Transparent Compression for Cost-Effective Cloud Data Storage

Transactions on Large-Scale Data- and Knowledge-Centered Systems III

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