A Declarative Pipeline Language for Complex Data Analysis

Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp; Petit, Matthieu

doi:10.1007/978-3-642-38197-3_3

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Thus, it is easier to locate the critical path to locate better parallelization and optimization scheme with PIR program than with Diderot. (Christiansen, Theil Have, Torp Lassen, & Petit, 2013) presents a logic-based scripting pipeline language, BANpipe, to model compositions of time consuming analysis. BANpipe creates a pipeline to represent the relayed processing of computation in the biological sequence analysis domain.…”

Section: A Staging-based Dslmentioning

confidence: 99%

Improving Gender Classification Using an Extended Set of Local Binary Patterns

Ardakany

Nicolescu

2014

International Journal of Multimedia Data Engineering and Management

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In this article, the authors designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, they proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. Traditional Local binary patterns include information about the relationship between a central pixel value and those of its neighboring pixels in a very compact manner. In the proposed method the authors incorporate into the descriptor more information from the neighborhood by using extra patterns. They have evaluated their approach on the standard FERET and CAS-PEAL databases and the experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. The results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy.

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Section: A Staging-based Dslmentioning

confidence: 99%

Improving Gender Classification Using an Extended Set of Local Binary Patterns

Ardakany

Nicolescu

2014

International Journal of Multimedia Data Engineering and Management

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PIR: A Domain Specific Language for Multimedia Retrieval

Huang

Zhao

Cao

2013

2013 IEEE International Symposium on Multimedia

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Abstract-Multimedia Information Retrieval (MIR) is a problem domain that includes programming tasks such as salient feature extraction, machine learning, indexing, and retrieval. There are a variety of implementations and algorithms for these tasks in different languages and frameworks, which are difficult to compose and reuse due to the interface and language incompatibility. Due to this low reusability, researchers often have to implement their experiments from scratch and the resulting programs cannot be easily adapted to parallel and distributed executions, which is important for handling large data sets.In this paper, we present Pipeline Information Retrieval (PIR) -a domain specific language for multi-modal feature manipulation. The goal of PIR is to unify the MIR programming tasks by hiding the programming details under a flexible layer of domain specific interface. PIR optimizes the MIR tasks by compiling the DSL programs into pipeline graphs, which can be executed using a variety of strategies (e.g. sequential, parallel, or distributed execution). We evaluated the performance of PIR applications on single machine with multiple cores, local cluster, and Amazon Elastic Compute Cloud (EC2) platform. The result shows that the PIR programs can greatly help MIR researchers and developers perform fast prototyping on single machine environment and achieve nice scalability on distributed platforms.

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Pir

Huang¹,

Zhao²,

Cao

2016

Web Design and Development

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Multimedia Information Retrieval (MIR) is a problem domain that includes programming tasks such as salient feature extraction, machine learning, indexing, and retrieval. There are a variety of implementations and algorithms for these tasks in different languages and frameworks, which are difficult to compose and reuse due to the interface and language incompatibility. Due to this low reusability, researchers often have to implement their experiments from scratch and the resulting programs cannot be easily adapted to parallel and distributed executions, which is important for handling large data sets. In this paper, we present Pipeline Information Retrieval (PIR), a Domain Specific Language (DSL) for multi-modal feature manipulation. The goal of PIR is to unify the MIR programming tasks by hiding the programming details under a flexible layer of domain specific interface. PIR optimizes the MIR tasks by compiling the DSL programs into pipeline graphs, which can be executed using a variety of strategies (e.g. sequential, parallel, or distributed execution). The authors evaluated the performance of PIR applications on single machine with multiple cores, local cluster, and Amazon Elastic Compute Cloud (EC2) platform. The result shows that the PIR programs can greatly help MIR researchers and developers perform fast prototyping on single machine environment and achieve nice scalability on distributed platforms.

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A Declarative Pipeline Language for Complex Data Analysis

Cited by 4 publications

References 13 publications

Improving Gender Classification Using an Extended Set of Local Binary Patterns

Improving Gender Classification Using an Extended Set of Local Binary Patterns

PIR: A Domain Specific Language for Multimedia Retrieval

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