This arXiv paper by Dirk Lewandowski and Philipp Mayr provides a critical review of Bergman's 2001 study on the Deep Web. It introduces the concept of the Academic Invisible Web, defined as databases and collections relevant to academia but not searchable by general-purpose internet search engines, and reviews approaches for indexing it. The authors question Bergman's size estimate and suggest collaboration.
An arXiv paper on digging deeper into hidden web databases limited by the top-k output constraint. It presents the GetNext meta-algorithm, which retrieves the next ranked tuple through a restrictive interface without prior knowledge of the ranking function, and algorithms that generate and execute multiple reformulated queries. Theoretical analysis and experiments over synthetic and real-world databases are reported.
An arXiv paper on discovering skylines over hidden web databases whose search interfaces enforce a top-k output constraint. It examines how search interface controls affect discovery cost and develops algorithms for one-ended range, free range and point predicates, combined for mixed interfaces. Experiments over real-world online and offline databases are reported.
A library support page explaining the invisible web, also called the deep web or hidden web. It states that search engines cover about 16% of the internet and advises against relying on them alone, especially for academic information. It lists content search engines cannot find, such as dynamically changing content, password protected resources and sites requiring registration, and suggests web directories and asking a librarian.
A working paper by John Schriner that focuses on how researchers monitor the Dark Web. It defines onion services and Tor and discusses tools for monitoring and securing onion services.
An arXiv paper presenting a large-scale analysis of the Tor network using a crawler called Mimir, which collects and visits linked content. The authors report a dataset of pages from more than 25k sites and examine network topology, replicated content and content types.
Dizzy is an open-source crawling and analysis system for onion services. It implements techniques to explore, update, check, and classify onion services at scale without overwhelming the Tor network. It was deployed in April 2021 and used to analyze more than 63.3 million crawled onion webpages, focusing on domain operations, web content, cryptocurrency usage, and web graph.
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An arXiv paper on digging deeper into hidden web databases limited by the top-k output constraint. It presents the GetNext meta-algorithm, which retrieves the next ranked tuple through a restrictive interface without prior knowledge of the ranking function, and algorithms that generate and execute multiple reformulated queries. Theoretical analysis and experiments over synthetic and real-world databases are reported.
An arXiv paper on discovering skylines over hidden web databases whose search interfaces enforce a top-k output constraint. It examines how search interface controls affect discovery cost and develops algorithms for one-ended range, free range and point predicates, combined for mixed interfaces. Experiments over real-world online and offline databases are reported.
A library support page explaining the invisible web, also called the deep web or hidden web. It states that search engines cover about 16% of the internet and advises against relying on them alone, especially for academic information. It lists content search engines cannot find, such as dynamically changing content, password protected resources and sites requiring registration, and suggests web directories and asking a librarian.
A working paper by John Schriner that focuses on how researchers monitor the Dark Web. It defines onion services and Tor and discusses tools for monitoring and securing onion services.
An arXiv paper presenting a large-scale analysis of the Tor network using a crawler called Mimir, which collects and visits linked content. The authors report a dataset of pages from more than 25k sites and examine network topology, replicated content and content types.
Dizzy is an open-source crawling and analysis system for onion services. It implements techniques to explore, update, check, and classify onion services at scale without overwhelming the Tor network. It was deployed in April 2021 and used to analyze more than 63.3 million crawled onion webpages, focusing on domain operations, web content, cryptocurrency usage, and web graph.
