One of the most commonly used ways to monitor execution of software applications is by analyzing logs. Logs are execution foot-print of software applications that are produced and stored for real-time or post-executio...
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In recent years, the demand for artificial intelligence applications has increased dramatically. Complex models can promote machine learning to achieve excellent results, but computing efficiency has gradually reached...
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This proceedings contains 14 papers. The Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. This conference issue focuses on the breadth of...
This proceedings contains 14 papers. The Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. This conference issue focuses on the breadth of the data management field. The topics include view updates, query compilation, concurrency control, data parsing, data cleaning;tackle more recent problems in particular graph processing;on road networks;failure handling techniques from data processing to distributed application programming;breadth of technical problems as well as the breadth of solutions spanning the hardware-software stack and going from theoretical to systems-oriented are an important quality of the database;etc. The key terms of this proceedings include dynamic road networks, massively parallel parsing, contended main-memory multicore transactions, LSH framework, homogeneous network embedding, data cleaning, MDedup, debuggable dataflow system, dynamic speculative optimizations, imputation of missing values techniques.
Edge computing is an emerging technique for enhancing the computing experience on mobile devices by offloading computationally demanding jobs to the edge cloud. One of the critical scientific issues in edge computing ...
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Biclustering refers to simultaneous clustering of objects and their features. Use of biclustering is gaining momentum in areas such as text mining, gene expression analysis and collaborative filtering. Due to requirem...
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ISBN:
(纸本)9781615671090
Biclustering refers to simultaneous clustering of objects and their features. Use of biclustering is gaining momentum in areas such as text mining, gene expression analysis and collaborative filtering. Due to requirements for high performance in large scale data processingapplications such as Collaborative filtering in E-commerce systems and large scale genome-wide gene expression analysis in microarray experiments, a high performance prallel/distributed solution for biclustering problem is highly desirable. Recently, Ahmad et al [1] showed that Bipartite Spectral Partitioning, which is a popular technique for biclustering, can be reformulated as a graph drawing problem where objective is to minimize Hall's energy of the bipartite graph representation of the input data. They showed that optimal solution to this problem is achieved when nodes are placed at the barycenter of their neighbors. In this paper, we provide a parallel algorithm for biclustering based on this formulation. We show that parallel energy minimization using barycenter heuristic is embarrassingly parallel. The challenge is to design a bicluster identification algorithm which is scalable as well as accurate. We show that our parallel implementation is not just extremely scalable, it is comparable in accuracy as well with serial implementation. We have evaluated proposed parallel biclustering algorithm with large synthetic data sets on upto 256 processors. Experimental evaluation shows large superlinear speedups, scalability and high level of accuracy.
The proceedings contains 66 papers. The following topics are dealt with: object-oriented database issues;concurrency control;query languages;views and representations;concurrency in advanced systems;query languages an...
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ISBN:
(纸本)0818620250
The proceedings contains 66 papers. The following topics are dealt with: object-oriented database issues;concurrency control;query languages;views and representations;concurrency in advanced systems;query languages and processing;resolving semantic heterogeneity;distributed control algorithms;query processingtechniques;object-oriented engineering applications;database operations;knowledge structuring and modeling database structures and access;data security;large knowledge-based systems;files and file structures;security and databases;replication management;database machines;extensions of relational models and systems;update policies and propagation;database design issues;transaction models;and issues in data engineering.
Recent years have seen an explosion in the academic and commercial applications of digital assistants. These technologies have become increasingly prolific, and their use has resulted in fairly rigid and standardized ...
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ISBN:
(纸本)9783030503437;9783030503444
Recent years have seen an explosion in the academic and commercial applications of digital assistants. These technologies have become increasingly prolific, and their use has resulted in fairly rigid and standardized techniques for achieving a desired result. Whether this includes physical actions, or direct voice queries to the system via pre-defined wake words and queries, there is a stark boundary between the human and the system. We aim to explore a shift in the paradigm of these current implementations, to that of an Ambient Intelligent (AmI) environment in which users can interface with the system in a more natural, seamless, and multi-modal manner. applications of this type of technology range from assisted living, to smart conference rooms and meeting spaces. In this paper we introduce an architectural framework for building an ambient intelligent platform using a combination of video and audio sensors to capture and process the data in a given area of interest.
GPUs have recently become important computational units on mobile devices, resulting in heterogeneous devices that can run a variety of parallelprocessingapplications. While developing and optimizing such applicatio...
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Stream processing is a new computing paradigm that enables continuous and fast analysis of massive volumes of streaming data. Debugging streaming applications is not trivial, since they are typically distributed acros...
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ISBN:
(纸本)9783642166112
Stream processing is a new computing paradigm that enables continuous and fast analysis of massive volumes of streaming data. Debugging streaming applications is not trivial, since they are typically distributed across multiple nodes and handle large amounts of data. Traditional debugging techniques like breakpoints often rely on a stop-the-world approach, which may be useful for debugging single node applications, but insufficient for streaming applications. We propose a new visual and analytic environment to support debugging, performance analysis, and troubleshooting for stream processingapplications. Our environment provides several visualization methods to study, characterize, and summarize the flow of tuples between stream processing operators. The user can interactively indicate points in the streaming application from where tuples will be traced and visualized as they flow through different operators, without stopping the application. To substantiate our discussion, we also discuss several of these features in the context of a financial engineering application.
This paper presents an efficient strategy to implement parallel and distributed computing for image processing on a neuromorphic platform. We use SpiNNaker, a many-core neuromorphic platform inspired by neural connect...
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ISBN:
(纸本)9781509052523
This paper presents an efficient strategy to implement parallel and distributed computing for image processing on a neuromorphic platform. We use SpiNNaker, a many-core neuromorphic platform inspired by neural connectivity in the brain, to achieve fast response and low power consumption. Our proposed method is based on fault-tolerant fine-grained parallelism that uses SpiNNaker resources optimally for process pipelining and decoupling. We demonstrate that our method can achieve a performance of up to 49.7 MP/J for Sobel edge detector, and can process 1600 x 1200 pixel images at 697 fps. Using simulated Canny edge detector, our method can achieve a performance of up to 21.4 MP/J. Moreover, the framework can be extended further by using larger SpiNNaker machines. This will be very useful for applications such as energy-aware and time-critical-mission robotics as well as very high resolution computer vision systems.
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