Abstract. The constantly growing amount of Web content and the success of the Social Web lead to increasing needs for Web archiving. These needs go beyond the pure preservation of Web pages. Web archives are turning into "community memories" that aim at building a better understanding of the public view on, e.g., celebrities, court decisions, and other events. In this paper we present the ARCOMEM architecture that uses semantic information such as entities, topics, and events complemented with information from the social Web to guide a novel Web crawler. The resulting archives are automatically enriched with semantic metainformation to ease the access and allow retrieval based on conditions that involve high-level concepts.
In this paper we present the Brown Dwarf, a distributed system designed to efficiently store, query and update multidimensional data over an unstructured Peer-to-Peer overlay, without the use of any proprietary tool. Brown Dwarf manages to distribute a highly effective centralized structure among peers on-the-fly. Both point and aggregate queries are then naturally answered on-line through cooperating nodes that hold parts of a fully or partially materialized data cube. Updates are also performed on-line, eliminating the usually costly over-night process. Our initial evaluation on an actual testbed proves that Brown Dwarf manages to distribute the structure across the overlay nodes incurring only a small storage overhead compared to the centralized algorithm. Moreover, it accelerates cube creation up to 5 times and querying up to several tens of times by exploiting the capabilities of the available network nodes working in parallel.
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