This document provides information about building a basic web crawler, including:
- Crawlers find and download web pages to build a collection for search engines by starting with seed URLs and following links on downloaded pages.
- Crawlers use techniques like multiple threads, politeness policies, and robots.txt files to efficiently and politely crawl the web at scale.
- Crawlers must also handle challenges like duplicate pages, updating freshness, focused crawling of specific topics, and accessing deep web or private pages.
Portable Lucene Index Format & Applications - Andrzej Bialeckilucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
This talk will present a design and implementation of a flexible, version-independent serialization format for Lucene indexes and its applications in index upgrades / downgrades, in distributed document analysis, in distributed indexing, and in integration with external indexing pipelines. This format enables submitting pre-analyzed documents to Lucene/Solr, and transferring parts of indexes between nodes in a distributed setup.
Presented by Adrien Grand, Software Engineer, Elasticsearch
Although people usually come to Lucene and related solutions in order to make data searchable, they often realize that it can do much more for them. Indeed, its ability to handle high loads of complex queries make Lucene a perfect fit for analytics applications and, for some use-cases, even a credible replacement for a primary data-store. It is important to understand the design decisions behind Lucene in order to better understand the problems it can solve and the problems it cannot solve. This talk will explain the design decisions behind Lucene, give insights into how Lucene stores data on disk and how it differs from traditional databases. Finally, there will be highlights of recent and future changes in Lucene index file formats.
Apache Lucene's next major release, 4.0, will introduce lots of flexibility into indexing, but also fundamental changes to the well-known APIs: It features a new and consistent, 4-dimensional iteration API on top of a low-level, pluggable codec API giving applications full control over the postings data.
Portable Lucene Index Format & Applications - Andrzej Bialeckilucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
This talk will present a design and implementation of a flexible, version-independent serialization format for Lucene indexes and its applications in index upgrades / downgrades, in distributed document analysis, in distributed indexing, and in integration with external indexing pipelines. This format enables submitting pre-analyzed documents to Lucene/Solr, and transferring parts of indexes between nodes in a distributed setup.
Presented by Adrien Grand, Software Engineer, Elasticsearch
Although people usually come to Lucene and related solutions in order to make data searchable, they often realize that it can do much more for them. Indeed, its ability to handle high loads of complex queries make Lucene a perfect fit for analytics applications and, for some use-cases, even a credible replacement for a primary data-store. It is important to understand the design decisions behind Lucene in order to better understand the problems it can solve and the problems it cannot solve. This talk will explain the design decisions behind Lucene, give insights into how Lucene stores data on disk and how it differs from traditional databases. Finally, there will be highlights of recent and future changes in Lucene index file formats.
Apache Lucene's next major release, 4.0, will introduce lots of flexibility into indexing, but also fundamental changes to the well-known APIs: It features a new and consistent, 4-dimensional iteration API on top of a low-level, pluggable codec API giving applications full control over the postings data.
Zoe Slattery's slides from PHPNW08:
The ability to store large quantities of local data means that many applications require some form of text search and retrieval facility. From the point of view of the application developer there are a number of choices to make, the first is whether to use a complete packaged solution or whether to use one of the available information libraries to build a custom information retrieval (IR) solution. In this talk I’ll look at the options for PHP programmers who choose to embed IR facilities within their applications.
For Java programmers there is clearly a good range of options for text retrieval libraries, but options for PHP programmers are more limited. At first sight for a PHP programmer wishing to embed indexing and search facilities in their application, the choice seems obvious - the PHP implementation of Lucene (Zend Search Lucene). There is no requirement to support another language, the code is PHP therefore easy for PHP programmers to work with and the license is commercially friendly. However, whilst ease of integration and support are key factors in choice of technology, performance can also be important; the performance of the PHP implementation of Lucene is poor compared to the Java implementation.
In this talk I’ll explain the differences in performance between PHP implementation of Lucene and the Java implementation and examine the other options available to PHP programmers for whom performance is a critical factor.
Finite-State Queries in Lucene:
* Background, improvement/evolution of MultiTermQuery API in 2.9 and Flex
* Implementing existing Lucene queries with NFA/DFA for better performance: Wildcard, Regex, Fuzzy
* How you can use this Query programmatically to improve relevance (I'll use an English test collection/English examples)
Quick overview of other Lucene features in development, such as:
* Flexible Indexing
* "More-Flexible" Scoring: challenges/supporting BM25, more vector-space models, field-specific scoring, etc.
* Improvements to analysis
Bonus:
* Lucene / Solr merger explanation and future plans
About the presenter:
Robert Muir is a super-active Lucene developer. He works as a software developer for Abraxas Corporation. Robert received his MS in Computer Science from Johns Hopkins and BS in CS from Radford University. For the last few years Robert has been working on foreign language NLP problems - "I really enjoy working with Lucene, as it's always receptive to better int'l/language support, even though everyone seems to be a performance freak... such a weird combination!"
Presented by Fotolog. Lucene is a powerful, high-performance, full-featured text search engine library that is written entirely in Java and provides a technology suitable for all size applications requiring full-text search in heterogeneous environments.
In this presentation, Frank Mash shows you how you can use Lucene with MySQL to offer powerful searching capabilities to your stakeholders. The presentation will cover installation, usage. optimization of Lucene, and how to interface a Ruby on Rails application with Lucene using a custom Java server. This session is highly recommended for those looking to add full-text cross-platform, database independent search capability to their application.
Faceted search is a powerful technique to let users easily navigate the search results. It can also be used to develop rich user interfaces, which give an analyst quick insights about the documents space. In this session I will introduce the Facets module, how to use it, under-the-hood details as well as optimizations and best practices. I will also describe advanced faceted search capabilities with Lucene Facets.
Zoe Slattery's slides from PHPNW08:
The ability to store large quantities of local data means that many applications require some form of text search and retrieval facility. From the point of view of the application developer there are a number of choices to make, the first is whether to use a complete packaged solution or whether to use one of the available information libraries to build a custom information retrieval (IR) solution. In this talk I’ll look at the options for PHP programmers who choose to embed IR facilities within their applications.
For Java programmers there is clearly a good range of options for text retrieval libraries, but options for PHP programmers are more limited. At first sight for a PHP programmer wishing to embed indexing and search facilities in their application, the choice seems obvious - the PHP implementation of Lucene (Zend Search Lucene). There is no requirement to support another language, the code is PHP therefore easy for PHP programmers to work with and the license is commercially friendly. However, whilst ease of integration and support are key factors in choice of technology, performance can also be important; the performance of the PHP implementation of Lucene is poor compared to the Java implementation.
In this talk I’ll explain the differences in performance between PHP implementation of Lucene and the Java implementation and examine the other options available to PHP programmers for whom performance is a critical factor.
Finite-State Queries in Lucene:
* Background, improvement/evolution of MultiTermQuery API in 2.9 and Flex
* Implementing existing Lucene queries with NFA/DFA for better performance: Wildcard, Regex, Fuzzy
* How you can use this Query programmatically to improve relevance (I'll use an English test collection/English examples)
Quick overview of other Lucene features in development, such as:
* Flexible Indexing
* "More-Flexible" Scoring: challenges/supporting BM25, more vector-space models, field-specific scoring, etc.
* Improvements to analysis
Bonus:
* Lucene / Solr merger explanation and future plans
About the presenter:
Robert Muir is a super-active Lucene developer. He works as a software developer for Abraxas Corporation. Robert received his MS in Computer Science from Johns Hopkins and BS in CS from Radford University. For the last few years Robert has been working on foreign language NLP problems - "I really enjoy working with Lucene, as it's always receptive to better int'l/language support, even though everyone seems to be a performance freak... such a weird combination!"
Presented by Fotolog. Lucene is a powerful, high-performance, full-featured text search engine library that is written entirely in Java and provides a technology suitable for all size applications requiring full-text search in heterogeneous environments.
In this presentation, Frank Mash shows you how you can use Lucene with MySQL to offer powerful searching capabilities to your stakeholders. The presentation will cover installation, usage. optimization of Lucene, and how to interface a Ruby on Rails application with Lucene using a custom Java server. This session is highly recommended for those looking to add full-text cross-platform, database independent search capability to their application.
Faceted search is a powerful technique to let users easily navigate the search results. It can also be used to develop rich user interfaces, which give an analyst quick insights about the documents space. In this session I will introduce the Facets module, how to use it, under-the-hood details as well as optimizations and best practices. I will also describe advanced faceted search capabilities with Lucene Facets.
Bagi calon guru maupun para guru diseluruh dunia... silahkan mencoba untuk menggunakan media pembelajaran yang saya buat sebagai bahan media pembelajaran mengenai materi " Nama-nama Malaikat Allah".
Selamat Mencoba
Semoga bermanfaat...
This is a tutorial about World wide web (www). In this tutorial we are going to discuss on:
History of WWW,
Components of WWW,
www Structure,
Uniform Resource Identifier,
HTTP Basics,
HTTP Request,
HTTP Response,
HTTP Headers,
HTML Basics,
HTML Example.
For more detail visit our Tech Blog:
https://msatechnosoft.in.blog/
The Anatomy of a Large-Scale Hypertextual Web Search EngineMehul Boricha
It explains the basics of Search Engine and How it Works (Google). It covers the following topics:
+ Introduction
+ Challenges
+ Design Goals
+ PageRank
+ Google Architecture Overview
+ Major Data Structures
Comet: by pushing server data, we push the web forwardNOLOH LLC.
According to the specification of HTTP, which is at the heart of all things web, a client must first request or “pull” information from the server and the server can only issue responses. It is never the other way around, with the server initiating the communication and “pushing” the data as it becomes available. Overcoming this limitation, actually an old and historical problem, would have remarkable applications, benefiting almost every page on the web to various degrees, and significantly enhancing the user experience. And the best part is: you can do it all right now, on any average server environment, and have it work on any standard browser! The modern, Web 2.0 -inspired collection of these solutions, design principles, and techniques for this “sever push technology” is sometimes referred to as “Comet.” I will discuss in detail: the numerous uses and benefits of Comet; the problems and difficulties that developers have to face; the variously accepted solution strategies that exist today including polling, long polling, streaming; their subcategories and their specific implementations, subcategories, advantages, disadvantages, and compatibility nuances; how HTML5 offers to address the issue; as well as outline some original research on the topic. Finally, I will illustrate these concepts and ideas through the live coding of a simple, Comet-based application using the help of a PHP framework with rich Comet support.
An alphabetical tour of digital media landscape terminology, covering concepts from Ajax to Usability. Designed for training of journalists entering the digital media landscape.
Slides for a college course based on "Incident Response & Computer Forensics, Third Edition" by by Jason Luttgens, Matthew Pepe, and Kevin Mandia.
Teacher: Sam Bowne
Twitter: @sambowne
Website: https://samsclass.info/121/121_F16.shtml
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
2. Assignment
( Due: Wednesday 18the Feb)
• Task 1: Find out At least Names of 10 unconventional Search Engines.
(not Including Google, Yahoo, MSN, Bing)
• Think Of or at least 3 random search queries
– Political
– Sports
– Technology
Search your same query string on at least 5 search Engines and analyses the
result.
Submission:
1. Query results snapshot
2. Your Analysis which search engine generated better results.
3. Describe How different the results from each search engine are? (No of same
website, Same Blogs Forums etc..) for each query category.
4. Which search engine in your perception generated results more relevant to your
requirements (i.e. what you needed, results based on your region/country).
5. Try using the same query on your friends PC and analyze the results. Do you get
Same results? Is there any difference in the Results?
Maximum in
Group (2)
3. Web Crawler
• Finds and downloads web pages automatically
– provides the collection for searching
• Web is huge and constantly growing
• Web is not under the control of search engine
providers
• Web pages are constantly changing
• Crawlers also used for other types of data
4. Retrieving Web Pages
• Every page has a unique uniform resource
locator (URL)
• Web pages are stored on web servers that use
HTTP to exchange information with client
software
• e.g.,
5. Retrieving Web Pages
• Web crawler client program connects to a
domain name system (DNS) server
• DNS server translates the hostname into an
internet protocol (IP) address
• Crawler then attempts to connect to server
host using specific port
• After connection, crawler sends an HTTP
request to the web server to request a page
– usually a GET request
7. Web Crawler
• Starts with a set of seeds, which are a set of
URLs given to it as parameters
• Seeds are added to a URL request queue
• Crawler starts fetching pages from the request
queue
• Downloaded pages are parsed to find link tags
that might contain other useful URLs to fetch
• New URLs added to the crawler’s request
queue, or frontier
• Continue until no more new URLs or disk full
8. Web Crawling
• Web crawlers spend a lot of time waiting for
responses to requests
• To reduce this inefficiency, web crawlers use
threads and fetch hundreds of pages at once
• Crawlers could potentially flood sites with
requests for pages
• To avoid this problem, web crawlers use
politeness policies
– e.g., delay between requests to same web server
9. Controlling Crawling
• Even crawling a site slowly will anger some
web server administrators, who object to any
copying of their data
• Robots.txt file can be used to control crawlers
11. Freshness
• Web pages are constantly being added,
deleted, and modified
• Web crawler must continually revisit pages it
has already crawled to see if they have
changed in order to maintain the freshness of
the document collection
– stale copies no longer reflect the real contents of
the web pages
12. Freshness
• HTTP protocol has a special request type
called HEAD that makes it easy to check for
page changes
– returns information about page, not page itself
13. Freshness
• Not possible to constantly check all pages
– must check important pages and pages that
change frequently
• Freshness is the proportion of pages that are
fresh
• Optimizing for this metric can lead to bad
decisions, such as not crawling popular sites
• Age is a better metric
15. Age
• Expected age of a page t days after it was last
crawled:
• Web page updates follow the Poisson
distribution on average
– time until the next update is governed by an
exponential distribution
16. Age
• Older a page gets, the more it costs not to
crawl it
– e.g., expected age with mean change frequency
λ = 1/7 (one change per week)
17. Focused Crawling
• Attempts to download only those pages that
are about a particular topic
– used by vertical search applications
• Rely on the fact that pages about a topic tend
to have links to other pages on the same topic
– popular pages for a topic are typically used as
seeds
• Crawler uses text classifier to decide whether
a page is on topic
18. Deep Web
• Sites that are difficult for a crawler to find are
collectively referred to as the deep (or hidden)
Web
– much larger than conventional Web
• Three broad categories:
– private sites
• no incoming links, or may require log in with a valid account
– form results
• sites that can be reached only after entering some data into
a form
– scripted pages
• pages that use JavaScript, Flash, or another client-side
language to generate links
19. Sitemaps
• Sitemaps contain lists of URLs and data about
those URLs, such as modification time and
modification frequency
• Generated by web server administrators
• Tells crawler about pages it might not
otherwise find
• Gives crawler a hint about when to check a
page for changes
21. Distributed Crawling
• Three reasons to use multiple computers for
crawling
– Helps to put the crawler closer to the sites it crawls
– Reduces the number of sites the crawler has to
remember
– Reduces computing resources required
• Distributed crawler uses a hash function to assign
URLs to crawling computers
– hash function should be computed on the host part of
each URL
22. Desktop Crawls
• Used for desktop search and enterprise search
• Differences to web crawling:
– Much easier to find the data
– Responding quickly to updates is more important
– Must be conservative in terms of disk and CPU
usage
– Many different document formats
– Data privacy very important
23. Document Feeds
• Many documents are published
– created at a fixed time and rarely updated again
– e.g., news articles, blog posts, press releases,
email
• Published documents from a single source can
be ordered in a sequence called a document
feed
– new documents found by examining the end of
the feed
24. Document Feeds
• Two types:
– A push feed alerts the subscriber to new
documents
– A pull feed requires the subscriber to check
periodically for new documents
• Most common format for pull feeds is called
RSS
– Really Simple Syndication, RDF Site Summary, Rich
Site Summary, or ...
25. RSS (Rich Site Summary)
• RSS (Rich Site Summary); originally RDF Site Summary; often
called Really Simple Syndication, uses a family of standard
web feed formats to publish frequently updated information:
blog entries, news headlines, audio, video.
• An RSS document (called "feed", "web feed", or "channel")
includes full or summarized text, and metadata, like
publishing date and author's name.
• RSS feeds enable publishers to syndicate data automatically.
A standard XML file format ensures compatibility with many
different machines/programs. RSS feeds also benefit users
who want to receive timely updates from favourite websites
or to aggregate data from many sites.
28. RSS
• ttl tag (time to live)
– amount of time (in minutes) contents should be
cached
• RSS feeds are accessed like web pages
– using HTTP GET requests to web servers that host
them
• Easy for crawlers to parse
• Easy to find new information
29. Conversion
• Text is stored in hundreds of incompatible file
formats
– e.g., raw text, RTF, HTML, XML, Microsoft Word, ODF,
PDF
• Other types of files also important
– e.g., PowerPoint, Excel
• Typically use a conversion tool
– converts the document content into a tagged text
format such as HTML or XML
– retains some of the important formatting information
30. Character Encoding
• A character encoding is a mapping between
bits and glyphs
– i.e., getting from bits in a file to characters on a
screen
– Can be a major source of incompatibility
• ASCII is basic character encoding scheme for
English
– encodes 128 letters, numbers, special characters,
and control characters in 7 bits, extended with an
extra bit for storage in bytes
31. Character Encoding
• Other languages can have many more glyphs
– e.g., Chinese has more than 40,000 characters, with
over 3,000 in common use
• Many languages have multiple encoding schemes
– e.g., CJK (Chinese-Japanese-Korean) family of East
Asian languages, Hindi, Arabic
– must specify encoding
– can’t have multiple languages in one file
• Unicode developed to address encoding
problems
32. Unicode
• Single mapping from numbers to glyphs that
attempts to include all glyphs in common use
in all known languages
• Unicode is a mapping between numbers and
glyphs
– does not uniquely specify bits to glyph mapping!
– e.g., UTF-8, UTF-16, UTF-32
33. Unicode
• Proliferation of encodings comes from a need
for compatibility and to save space
– UTF-8 uses one byte for English (ASCII), as many as
4 bytes for some traditional Chinese characters
– variable length encoding, more difficult to do
string operations
– UTF-32 uses 4 bytes for every character
• Many applications use UTF-32 for internal text
encoding (fast random lookup) and UTF-8 for
disk storage (less space)
35. Storing the Documents
• Many reasons to store converted document
text
– saves crawling time when page is not updated
– provides efficient access to text for snippet
generation, information extraction, etc.
• Database systems can provide document
storage for some applications
– web search engines use customized document
storage systems
36. Storing the Documents
• Requirements for document storage system:
– Random access
• request the content of a document based on its URL
• hash function based on URL is typical
– Compression and large files
• reducing storage requirements and efficient access
– Update
• handling large volumes of new and modified
documents
• adding new anchor text
37. Large Files
• Store many documents in large files, rather
than each document in a file
– avoids overhead in opening and closing files
– reduces seek time relative to read time
• Compound documents formats
– used to store multiple documents in a file
– e.g., TREC Web
38. Compression
• Text is highly redundant (or predictable)
• Compression techniques exploit this redundancy
to make files smaller without losing any of the
content
• Compression of indexes covered later
• Popular algorithms can compress HTML and XML
text by 80%
– e.g., DEFLATE (zip, gzip) and LZW (UNIX compress,
PDF)
– may compress large files in blocks to make access
faster
39. BigTable
• Google’s document storage system
– Customized for storing, finding, and updating web
pages
– Handles large collection sizes using inexpensive
computers
40. BigTable
• No query language, no complex queries to
optimize
• Only row-level transactions
• Tablets are stored in a replicated file system that
is accessible by all BigTable servers
• Any changes to a BigTable tablet are recorded to
a transaction log, which is also stored in a shared
file system
• If any tablet server crashes, another server can
immediately read the tablet data and transaction
log from the file system and take over
41. • Most Relational databases store their data in
files that are constantly modified. In contrast,
BigTable stores its data in immutable
(unchangeable) files.
• Once File data is written to a BigTable file, it is
never changed.
• This helps in failure recovery.
42. BigTable
• Logically organized into rows
• A row stores data for a single web page
• The URL, www.example.com, is the row key,
which can be used to find this row.
• Combination of a row key, a column key, and a
timestamp point to a single cell in the row
43. BigTable
• BigTable can have a huge number of columns
per row
– all rows have the same column groups
– not all rows have the same columns
– important for reducing disk reads to access
document data
• Rows are partitioned into tablets based on
their row keys
– simplifies determining which server is appropriate
44. Detecting Duplicates
• Duplicate and near-duplicate documents
occur in many situations
– Copies, versions, plagiarism, spam, mirror sites
– 30% of the web pages in a large crawl are exact or
near duplicates of pages in the other 70%
• Duplicates consume significant resources
during crawling, indexing, and search
– Little value to most users
45. Duplicate Detection
• Exact duplicate detection is relatively easy
• Checksum techniques
– A checksum is a value that is computed based on the
content of the document
• e.g., sum of the bytes in the document file
– Possible for files with different text to have same
checksum
• Functions such as a cyclic redundancy check
(CRC), have been developed that consider the
positions of the bytes
46. Near-Duplicate Detection
• More challenging task
– Are web pages with same text context but
different advertising or format near-duplicates?
• A near-duplicate document is defined using a
threshold value for some similarity measure
between pairs of documents
– e.g., document D1 is a near-duplicate of
document D2 if more than 90% of the words in
the documents are the same
47. Near-Duplicate Detection
• Search:
– find near-duplicates of a document D
– O(N) comparisons required
• Discovery:
– find all pairs of near-duplicate documents in the
collection
– O(N2) comparisons
• IR techniques are effective for search scenario
• For discovery, other techniques used to
generate compact representations