Fast Compression of Large Semantic Web Data Using X10

Cheng, Long; Malik, Avinash; Kotoulas, Spyros; Ward, Tomás E.; Theodoropoulos, Georgios

doi:10.1109/tpds.2015.2496579

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…Theoretically, the first parallel approach will be more efficient than the second one, in terms of implementations using high performance computing (HPC) programming languages such as X10 [20], [21] and MPI [22]. This is because we can track the location of each subset of X and can manually assign them to a specified destination (i.e., sublog partition) according to our requirements.…”

Section: Candidate Pruningmentioning

confidence: 99%

Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

Cheng

Dongen

Aalst

2020

IEEE Trans. Serv. Comput.

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Process descriptions are used to create products and deliver services. To lead better processes and services, the first step is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs. Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful but complex, or creating informal models which are intuitive but lack semantics. In this work, we introduce a novel method that returns hybrid process models to bridge this gap. Moreover, to cope with today's big event logs, we propose an efficient method, called f -HMD, aims at scalable hybrid model discovery in a cloud computing environment. We present the detailed implementation of our approach over the Spark framework, and our experimental results demonstrate that the proposed method is efficient and scalable.

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Section: Candidate Pruningmentioning

confidence: 99%

Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

Cheng

Dongen

Aalst

2020

IEEE Trans. Serv. Comput.

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“…In recent year, a few scholars have begun to study parallel compression algorithms to speedup the running the operation [33][34][35][36]. Cheng [33] proposes an efficient algorithm for fast encoding large Semantic Web data, and present the detailed implementation of proposed approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. Urbani [34] researches and proposes a MapReduce algorithm that efficiently compresses and decompresses a large amount of Semantic Web data.…”

Section: Related Workmentioning

confidence: 99%

“…Here, We compare POICAG and DOICAS with the other compression algorithms according to effectiveness. SQUISH [28], APGAS [33] and SWSM [34] are proposed in recent years, which have been proved to be effective. From the graphs in Fig.…”

Section: Fig 12 Error Threshold Vs Compression Ratiomentioning

confidence: 99%

An Optimized Iterative Semantic Compression Algorithm And Parallel Processing for Large Scale Data

Jin¹,

Chen²,

Tung³

et al. 2018

KSII TIIS

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With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.

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“…Data skew occurs naturally in big data applications [5], [15], and transfer skewed data will bring in heavy network traffic and result in load imbalncing. Therefore, it is very important for practical data systems to perform efficiently in such contexts [5].…”

Section: Skew Handlingmentioning

confidence: 99%

A Coflow-Based Co-Optimization Framework for High-Performance Data Analytics

Cheng

Wang

Pei

et al. 2017

2017 46th International Conference on Parallel Processing (ICPP)

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Abstract-Efficient execution of distributed database operators such as joining and aggregating is critical for the performance of big data analytics. With the increase of the compute speedup of modern CPUs, reducing the network communication time of these operators in large systems is becoming increasingly important, and also challenging current techniques. Significant performance improvements have been achieved by using state-of-the-art methods, such as reducing network traffic designed in the data management domain, and data flow scheduling in the data communications domain. However, the proposed techniques in both fields just view each other as a black box, and performance gains from a co-optimization perspective have not yet been explored.In this paper, based on current research in coflow scheduling, we propose a novel Coflow-based Co-optimization Framework (CCF), which can co-optimize application-level data movement and network-level data communications for distributed operators, and consequently contribute to their performance in large distributed environments. We present the detailed design and implementation of CCF, and conduct an experimental evaluation of CCF using large-scale simulations on large data joins. Our results demonstrate that CCF can always perform faster than current approaches on network communications in large-scale distributed scenarios.

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Fast Compression of Large Semantic Web Data Using X10

Cited by 12 publications

References 37 publications

Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

An Optimized Iterative Semantic Compression Algorithm And Parallel Processing for Large Scale Data

A Coflow-Based Co-Optimization Framework for High-Performance Data Analytics

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