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Generative Kernels for Tree-structured data

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第10期29卷 4932-4946页

作者： Bacciu, Davide Micheli, Alessio Sperduti, Alessandro Univ Pisa Dipartimento Informat I-56127 Pisa Italy Univ Padua Dipartimento Matemat I-35121 Padua Italy

This paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Furthermore, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. This paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive, and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping.

关键词： Generative kernels hidden tree Markov models learning for structured domain structured data processing

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A Case Study of Optimizing Big data Analytical Stacks Using structured data Shuffling 5

A Case Study of Optimizing Big Data Analytical Stacks Using ...

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IEEE International Congress on Big data (Bigdata Congress)

作者： Tang, Dixin Liu, Taoying Lee, Rubao Liu, Hong Li, Wei Chinese Acad Sci Inst Comp Technol Beijing 100864 Peoples R China Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781509026227

Current major big data analytical stacks often consist of a general-purpose, multi-staged cluster computation framework (e.g. Hadoop) and a SQL query execution system (e.g. Hive) on its top. In such stacks, a key factor of query execution performance is the efficiency of data shuffling between two execution stages (e.g. Map/Reduce). However, current stacks often execute data shuffling in a data-oblivious way, which means that for structured data processing, various useful information about the shuffled data and the queries on the data is simply wasted. Specifically, this problem makes two optimization opportunities lost: i) unnecessary records cannot be filtered in advance;ii) column-oriented compression algorithms cannot be applied. To solve the problem, in this paper, we have designed and implemented a novel data shuffling mechanism in Hadoop, called structured data Shuffling (S-Shuffle), which avoids the low efficiencies of traditional data shuffling by carefully leveraging the rich information in data and queries provided by Hive. Our experimental results with industry-standard TPC-H benchmark show that by using S-Shuffle, the performance of SQL query processing on Hadoop can be improved by up to 2.4x.

关键词： Big data analytical stacks structured data processing MapReduce data shuffling Cross-layer optimization

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A Case Study of Optimizing Big data Analytical Stacks using structured data Shuffling

A Case Study of Optimizing Big Data Analytical Stacks using ...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Tang, Dixin Liu, Taoying Lee, Rubao Liu, Hong Li, Wei Chinese Acad Sci Inst Comp Technol Beijing 100864 Peoples R China Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781467365987

Current major big data analytical stacks often consist of a general purpose, multi-staged computation framework (e.g. Hadoop) and an SQL query system (e.g. Hive) on its top. A key factor of query performance is the efficiency of data shuffling between two execution stages (e.g. Map/Reduce). In current data shuffling, various useful information about the shuffled data and the query on the data is simply wasted. In this paper, we make a strong case of cross-layer optimizations for Hive/Hadoop stack: we have designed and implemented a novel data shuffling mechanism in Hadoop, called structured data Shuffling (S-Shuffle), which carefully leverages the rich information in data and queries to optimize the overall query processing. Our experimental results with industry-standard TPC-H benchmark show that, by using S-Shuffle, the performance of SQL query processing on Hadoop can be improved by up to 2.4x.

关键词： Big data analytical stacks structured data processing MapReduce data shuffling Cross-layer optimization

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An input-output hidden Markov model for tree transductions

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NEUROCOMPUTING 2013年 112卷 34-46页

作者： Bacciu, Davide Micheli, Alessio Sperduti, Alessandro Univ Pisa Dipartimento Informat I-56100 Pisa Italy Univ Padua Dipartimento Matemat I-35100 Padua Italy

The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model to non-homogeneous state transition and emission probabilities. We show how the proposed input-driven approach can be used to realize different types of structured transductions between trees. A thorough experimental analysis is proposed to investigate the advantage of introducing an input-driven dynamics in structured-data processing. The results of this analysis suggest that input-driven models can capture more discriminative structural information than homogeneous approaches in computational learning tasks, including document classification and more general substructure categorization. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Bottom-up hidden tree Markov model Input-driven generative model Learning tree transductions structured data processing

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Adaptive Tree Kernel by Multinomial Generative Topographic Mapping

Adaptive Tree Kernel by Multinomial Generative Topographic M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Bacciu, Davide Micheli, Alessio Sperduti, Alessandro Univ Pisa Dipartimento Informat Largo B Pontecorvo 3 Pisa Italy Univ Padua Dipartimento Matemat Pura & Applicata Padua Italy

ISBN: (纸本)9781424496365

Learning the kernel function from data is a challenging open issue in structured data processing. In the paper, we propose a novel adaptive kernel, defined over a generative learning model, that exploits a novel multinomial extension of the Generative Topographic Mapping for structured data (GTM-SD). We show how the proposed kernel effectively exploits the GTM-SD continuity and smoothness properties to provide dense kernels characterized by an high discriminative power even with small topographic maps. Experimental evaluations on challenging structured XML document repositories show the effectiveness of the proposed approach against state-of-the-art syntactic and adaptive convolutional kernels.

关键词： Computational modeling data models Hidden Markov models Kernel Lattices Neurons Syntactics XML adaptive tree kernel document handling extensible markup language generative learning model learning (artificial intelligence) multinomial generative topographic mapping structured XML document repository structured data processing

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VLSI AND WSI ASSOCIATIVE STRING PROCESSORS FOR structured data-processing

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IEE PROCEEDINGS-E COMPUTERS AND DIGITAL TECHNIQUES 1986年第3期133卷 153-162页

作者： LEA, RM Department of Electrical Engineering and Electronics Brunel University Uxbridge UK

A novel fine-grain parallel-processing microelectronic architecture is proposed as a cost-effective building-block for fifth-generation computer systems. Based on a fully programmable associative string processing computational structure, the architecture provides particularly flexible support and manipulation of abstract data structures over a wide range of information processing tasks. Moreover, the architecture is well suited to the technological constraints of VLSI chip and, especially, WSI device fabrication. The paper describes the ASP (associative string processor) architecture and its instruction set. The style of associative string processing is introduced with three algorithms, illustrating both relational and arithmetic operations. Current projects leading to VLSI ASP chips and WSI ASP devices are discussed and ‘ball-park’ performance figures are given.

关键词： WSI device fabrication associative string processor fully programmable associative string processing computational structure Digital signal processing ASP abstract data structures instruction set structured data processing VLSI ASP chips WSI ASP devices VLSI chip Semiconductor integrated circuits fine-grain parallel-processing microelectronic architecture information processing tasks WSI associative string processors signal processing information theory VLSI fifth-generation computer systems Information theory Signal processing and detection

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VLSI AND WSI ASSOCIATIVE STRING PROCESSORS FOR structured data-processing

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IEE PROCEEDINGS-I COMMUNICATIONS SPEECH AND VISION 1986年第3期133卷 113-122页

作者： LEA, RM Department of Electrical Engineering and Electronics Brunel University Uxbridge UK

关键词： abstract data structures Microprocessor chips instruction set VLSI fifth-generation computer systems structured data processing Microprocessors and microcomputers WSI microprocessor chips arithmetic operations Multiprocessing systems associative string processors fine-grain parallel-processing microelectronic architecture parallel processing

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