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.
1. Windows Azure is a cloud computing platform that provides a hosted environment for building, deploying and managing applications and services through a global network of Microsoft-managed data centers.
2. The platform provides compute, storage and networking services that together support development and hosting of applications scaled for the internet.
3. Key components include the Azure Services Platform for .NET and SQL-based services, Live Services for user-centric experiences, and support for building applications that integrate on-premises and cloud-hosted services using standard protocols.
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.
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.
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.
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.
1. Windows Azure is a cloud computing platform that provides a hosted environment for building, deploying and managing applications and services through a global network of Microsoft-managed data centers.
2. The platform provides compute, storage and networking services that together support development and hosting of applications scaled for the internet.
3. Key components include the Azure Services Platform for .NET and SQL-based services, Live Services for user-centric experiences, and support for building applications that integrate on-premises and cloud-hosted services using standard protocols.
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.
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.
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.