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A distributed World Wide Web cache was designed to support efficient and scalable solutions at high hierarchy levels. The cache used filtering algorithms for determining the location of objects in main memory caches. It also featured a two-level routing scheme that promotes load sharing among components through routing arrays. Moreover, the main memory of the Web cache's frontends was managed using a mechanism of popularity counters.

Research was conducted to study the use of filtering algorithms in Web caches. A model of a proxy cache was utilized to analyze long traces from servers in a cache hierarchy. It was also assumed that users utilize infinite browser caches supporting a time-to-live based object expiration framework. Results indicated that filtering algorithms supported lower disk load and high hit rates. Findings also showed that aggressive caching policies maximized hit rate through the storage of cacheable objects that do not support the best performance.

The Wikipedia project has created one of the largest and best-known open knowledge communities. This community is a model for several similar efforts, both public and commercial, and even for the knowledge economy of the future e-society. For these reasons, issues of quality, social processes, and motivation within the Wikipedia knowledge community have attracted attention of researchers. Research has often used Social Network Analysis applied to networks created based on behavioral data available from the edit history of the Wikipedia. This paper asks the following question: are the popular assumptions about the social interpretations of networks created from the edit history valid? We verify commonly assumed interpretations of four types of networks created from discussions on Wikipedia talk pages, co-edits and reverts in Wikipedia articles, and edits of articles in various topics, by comparing these networks with results from a survey of editors of the Polish Wikipedia community. The results indicate that while the behavioral networks are strongly related to the declarations of respondents, only in one case of the network created from talk pages and interpreted as acquaintance we can observe a near equivalence. The article next describes improved definitions of behavioral indicators obtained through machine learning. The improved networks are much closer to their declarative counterparts. The main contribution of the article is a validated model of an acquaintance network among Wikipedia editors that can be derived from behavioral data and validly interpreted as acquaintance. Other contributions are improved versions of behavioral networks based on editing behavior and discussion history on the Wikipedia. [ABSTRACT FROM AUTHOR]