Search engine (computing)

How search engines work and their categories
– Search engines provide an interface for users to specify criteria for finding matching items.
– Search queries are typically expressed as sets of words or natural language.
– Different search engines have varying syntax for search queries.
– Search engines rank items by relevance to reduce search time.
– Boolean search engines return exact matches, while probabilistic search engines use similarity measures.
– Indexing is the process of collecting metadata about items for quick retrieval.
– Indexed information requires less storage compared to storing full item content.
– Some search engines use caches to store copies of items for efficiency.
– Crawler or spider type search engines assess items dynamically during search queries.
– Meta search engines aggregate results from other search engines.
– Web search engines are designed for searching web pages, documents, and images.
– They follow a multi-stage process of crawling, indexing, and resolving user queries.
– Crawling involves discovering and parsing links to find relevant information.
– Continuous crawl methods are used instead of seed lists for discovery.
– Sophisticated scheduling algorithms determine when to revisit pages based on relevance.
– Some search engines do not store an index and assess items at the time of search queries.
– Crawler or spider type search engines collect and assess items dynamically.
– Meta search engines reuse index or results from other search engines.
– Database size is less emphasized, with relevancy ranking being the focus.
– Google’s Knowledge Graph has enhanced search engine experience but raised concerns about other websites’ traffic.

Factors affecting search engine ranking
– Speed of the web server
– Resource constraints like hardware and bandwidth
– Link map data structures
– Algorithms that compute the popularity score of web pages
– Differentiation between internal and external links

Database search engines
– Specialized search engines for text-based content in databases
– Challenges in solving complex queries
– Pseudo-logical queries in databases
– Indexing data in a more economized form
– Expeditious search in databases

Mixed search engines
– Search engines that handle both database content and web pages/documents
– Large web search engines like Google
– Crawling and indexing pages/documents in a separate index
– Compounding search results from multiple indices
– Generating search results based on rules

History and advancements in search technology
– Development of search engines over time
– Importance of hypertext and memory extension
– Vannevar Bush’s concept of the ‘memex’
– Associative indexing as a key contribution
– Development of new forms of encyclopedia
– Gerard Salton and the SMART informational retrieval system
– Important concepts in SMART like the vector space model
– String search engines for rapid text retrieval
– Novel string-search architecture combining FSA logic and CAM
– Performance despite the presence of errors in character codes

Note: The subtopics “Importance of Search Engines,” “Functionality of Search Engines,” “Challenges in Search Engine Development,” “Evolution of Search Engines,” and “Impact of Search Engines” have been incorporated into the respective groups.