The document outlines the daily schedule and habits of Lionel Messi. It details that Messi wakes up at 8:00 am, has breakfast at 8:30 am and a shower at 9:30 am before going to practice at FC Barcelona's stadium, Camp Nou, at 11:00 am. After practice, Messi returns home at 1:30 pm to eat lunch at 2:00 pm. His afternoon includes shopping, dining out with his wife, and walking before returning home for the evening where he shops, visits his mother, showers, eats dinner, and goes to bed by 11:00 pm.
The document outlines the daily schedule and habits of Lionel Messi. It details that Messi wakes up at 8:00 am, has breakfast at 8:30 am and a shower at 9:30 am before going to practice at FC Barcelona's stadium, Camp Nou, at 11:00 am. After practice, Messi returns home at 1:30 pm to eat lunch at 2:00 pm. His afternoon includes shopping, dining out with his wife, and walking before returning home for the evening where he shops, visits his mother, showers, eats dinner, and goes to bed by 11:00 pm.
Wrapper induction construct wrappers automatically to extract information f...George Ang
Wrapper induction is a technique to automatically generate wrappers to extract information from web sources. It involves learning extraction rules from labeled examples to construct a wrapper as a finite state machine or set of delimiters. Two main wrapper induction systems are WIEN, which defines wrapper classes including LR, and STALKER, which uses a more expressive model with extraction rules and landmarks to handle structure hierarchically. Remaining challenges include selecting informative examples, generating label pages automatically, and developing more expressive models.
This document summarizes a tutorial given by Bing Liu on opinion mining and summarization. The tutorial covered several key topics in opinion mining including sentiment classification at the document and sentence level, feature-based opinion mining and summarization, comparative sentence extraction, and opinion spam detection. The tutorial provided an overview of the field of opinion mining and abstraction as well as summaries of various approaches to tasks such as sentiment classification using machine learning methods and feature scoring.
Wrapper induction construct wrappers automatically to extract information f...George Ang
Wrapper induction is a technique to automatically generate wrappers to extract information from web sources. It involves learning extraction rules from labeled examples to construct a wrapper as a finite state machine or set of delimiters. Two main wrapper induction systems are WIEN, which defines wrapper classes including LR, and STALKER, which uses a more expressive model with extraction rules and landmarks to handle structure hierarchically. Remaining challenges include selecting informative examples, generating label pages automatically, and developing more expressive models.
This document summarizes a tutorial given by Bing Liu on opinion mining and summarization. The tutorial covered several key topics in opinion mining including sentiment classification at the document and sentence level, feature-based opinion mining and summarization, comparative sentence extraction, and opinion spam detection. The tutorial provided an overview of the field of opinion mining and abstraction as well as summaries of various approaches to tasks such as sentiment classification using machine learning methods and feature scoring.
The document provides an overview of Huffman coding, a lossless data compression algorithm. It begins with a simple example to illustrate the basic idea of assigning shorter codes to more frequent symbols. It then defines key terms like entropy and describes the Huffman coding algorithm, which constructs an optimal prefix code from the frequency of symbols in the data. The document discusses how the algorithm works, its advantages in achieving compression close to the source entropy, and some limitations. It also covers applications of Huffman coding like image compression.
Do not crawl in the dust different ur ls similar textGeorge Ang
The document describes the DustBuster algorithm for discovering DUST rules - rules that transform one URL into another URL that contains similar content. The algorithm takes as input a list of URLs from a website and finds valid DUST rules without requiring any page fetches. It detects likely DUST rules based on a large support principle and small buckets principle. It then eliminates redundant rules and validates the remaining rules using a sample of URLs to identify rules that transform URLs with similar content. Experimental results on logs from two websites show that DustBuster is able to discover DUST rules that can help improve crawling efficiency.
The document discusses techniques for optimizing front-end web performance. It provides examples of how much time is spent loading different parts of top websites, both with empty caches and full caches. The "performance golden rule" is that 80-90% of end-user response time is spent on the front-end. The document also outlines Yahoo's 14 rules for performance optimization, which include making fewer HTTP requests, using content delivery networks, adding Expires headers, gzipping components, script and CSS placement, and more.
48. 教育网问题分析-QZone为例
广东较周边
省份差
学校 访问次数 平均延时
广州大学 2033 9075.8047
华南理工大学 1553 8758.7646
华南师范大学 1508 14237.0137
中山大学 1219 8939.3193
PING 222.201.68.10 (222.201.68.10) 56(84) bytes of data.
64 bytes from 222.201.68.10: icmp_seq=1 ttl=119 time=227 ms
64 bytes from 222.201.68.10: icmp_seq=2 ttl=119 time=213 ms
64 bytes from 222.201.68.10: icmp_seq=3 ttl=119 time=211 ms
64 bytes from 222.201.68.10: icmp_seq=4 ttl=119 time=221 ms
64 bytes from 222.201.68.10: icmp_seq=5 ttl=119 time=238 ms
64 bytes from 222.201.68.10: icmp_seq=6 ttl=119 time=244 ms
traceroute to 222.201.68.10 (222.201.68.10), 30 hops max, 40 byte packets
1 222.202.96.130 (222.202.96.130) 0.688 ms 0.357 ms 0.453 ms
2 210.39.19.5 (210.39.19.5) 0.240 ms 0.219 ms 0.187 ms
3 202.112.53.129 (202.112.53.129) 4.227 ms 4.117 ms 4.134 ms
4 * * *
5 202.112.19.102 (202.112.19.102) 3.298 ms 3.669 ms 3.664 ms
6 222.200.253.5 (222.200.253.5) 3.865 ms 3.856 ms 3.865 ms
7 222.200.252.14 (222.200.252.14) 10.405 ms 10.408 ms 10.535 ms
8 222.200.129.22 (222.200.129.22) 236.715 ms 236.832 ms
50. 内容被cache后
0 0.5 1 1.5 2 2.5 3
image
image
html
Expires header
1
user requests
www.yahoo.com
2
user requests
other web pages
3
user re-requests
www.yahoo.com
with a full cache
64. GSLB工作原理
Root DNS Server
LocalDns Server
上海电信用户
QQ DNS Server
1
2 3
4
5
6
7
8
9
SLB
Server
&Cache
网通IDC1
SLB
Server
&Cache
深圳电信IDC2
SLB
Server
&Cache
上海电信IDC3
GSLB控制器
IP地址库