In this work we proposed MementoMap, a flexible and adaptive framework to summarize holdings of a web archive efficiently. We described a simple, yet extensible, file format suitable for MementoMap. We used the complete index of the Arquivo.pt comprising 5B mementos (archived web pages files) to understand the nature and shape of its holdings. We generated MementoMaps with varying amount of detail from its HTML pages that have an HTTP status code of 200 OK. Additionally, we designed a single-pass, memory-efficient, and parallelization-friendly algorithm to compact a large MementoMap into a small one and an in-file binary search method for efficient lookup. We analyzed more than three years of MemGator (a Memento aggregator) logs to understand the response behavior of 14 public web archives. We evaluated MementoMaps by measuring their Accuracy using 3.3M unique URIs from MemGator logs. We found that a MementoMap of less than 1.5 Relative Cost (as compared to the comprehensive listing of all the unique original URIs) can correctly identify the presence or absence of 60 of the lookup URIs in the corresponding archive while maintaining 100 Recall (i.e., zero false negatives).
@cite_5: Frequently a user's information needs are stored in the databases of multiple search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. To support unified access to multiple search engines, a metasearch engine can be constructed. When a metasearch engine receives a query from a user, it invokes the underlying search engines to retrieve useful information for the user. Metasearch engines have other benefits as a search tool such as increasing the search coverage of the Web and improving the scalability of the search. In this article, we survey techniques that have been proposed to tackle several underlying challenges for building a good metasearch engine. Among the main challenges, the database selection problem is to identify search engines that are likely to return useful documents to a given query. The document selection problem is to determine what documents to retrieve from each identified search engine. The result merging problem is to combine the documents returned from multiple search engines. We will also point out some problems that need to be further researched.
@cite_3: Document sources are available everywhere, both within the internal networks of organizations and on the Internet. Even individual organizations use search engines from different vendors to index their internal document collections. These search engines are typically incompatible in that they support different query models and interfaces, they do not return enough information with the query results for adequate merging of the results, and finally, in that they do not export metadata about the collections that they index (e.g., to assist in resource discovery). This paper describes STARTS , an emerging protocol for Internet retrieval and search that facilitates the task of querying multiple document sources. STARTS has been developed in a unique way. It is not a standard, but a group effort coordinated by Stanford's Digital Library project, and involving over 11 companies and organizations. The objective of this paper is not only to give an overview of the STARTS protocol proposal, but also to discuss the process that led to its definition.
Query routing is a rigorously researched topic in various fields including, networked databases, meta-searching, and search aggregation @cite_3 @cite_5 . However, archive profiling and Memento lookup routing is a niche field that is not explored by many researchers beyond a small community.