大数据存储与检索

高性能大数据流存储与检索技术

P1

性能和特点:

采用MongoDB自建存储集群,实验采用服务器软硬件配置如下:

Intel Xeon E5606 @ 2.13GHz
16GB memory
RAID1 SATA disk
100Mb bandwidth network
SuSE10 sp2
MongoDB version 2.0.0

tu1 

图1. 海量数据插入性能比较

上图纵轴数据体现了单位时间插入的文档数量,横轴为持续插入的文档数量(同时针对指定一整型字段建立索引)。SM表示采用单一MongoDB节点;MC表示采用MongoDB官方集群;AISM表示采用基于MongoDB自构建集群的性能。

 主要应用:

基于计算机集群日志信息存储与分析,微博短信等短文本流数据的存储及舆情分析等。

 应用案例:基于短文本数据流的分析系统。

近期文章

学术讲座通知​:深度结构建模及其在物体检测和姿态估计中的应用

题目:Modeling deep structures with application to object detection and pose estimation
报告人:欧阳万里 博士 香港中文大学
主持人:李春光
时间:2017年4月12日(星期三)16:00-17:20
地点:教三811会议室

摘要:Deep learning attempts to learn feature representation by multiple levels of abstraction. It is found to be useful in speech recognition, face recognition, image classification, biology, physics, and material science. In this talk, a brief introduction will be given on our recent progress in using deep learning as a tool for modeling the structure in visual data for object detection and human pose estimation. We show that observation in our problem are useful in modeling the structure of deep model and help to improve the performance of deep models for our problem.

报告人简介:
Wanli Ouyang received the PhD degree in the Department of Electronic Engineering, The Chinese University of Hong Kong, where he is now a research assistant professor. His research interests include image processing, computer vision and pattern recognition. He is the first/correspondence author of 6 papers on TPAMI and IJCV, and has published 26 papers on top tier conferences like CVPR, ICCV and NIPS. ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most important grand challenges in computer vision. The team led by him ranks No. 1 in the ILSVRC 2015 and ILSVRC 2016. He receives the best reviewer award of ICCV. He has been the reviewer of many top journals and conferences such as IEEE TPAMI, TIP, IJCV, TSP, TITS, TNN, CVPR, and ICCV. He is a senior member of the IEEE.
(更多信息请关注: http://www.ee.cuhk.edu.hk/~wlouyang/ )

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