Changes

In Discover, different Meresco components are configured to work together in an efficient observer pattern, defined in what is called Meresco DNA (written in Python). The process is as follows: metadata is harvested from different sources using the Meresco harvester, . It is then cross-walked into (any format you like, but we chose) MODS, then normalized, stored and indexed in three separate distinct but dependent integrated indexes: a full-text Lucene index, a custom Burst Trie facet index and N-gram index for suggestions and fixing spelling mistakes. The facet index supports multiple algoritmes: drilldown, Jaccard, Mutual Information (or Information Gain) and Χ². One of the facets is used to cluster the search results by subject by using the Jaccarda and uses the Jaccard, Mutual Information and Χ² algorithms to dynamically create a list of keywords which are relevant to the query. <br/>The query parser component automatically detects and supports Google-like, Boolean and field-specific queries. Different XML documents describing the same content item coalesce to provide the user interface with an easy way to access metadata from either the original or normalized metadata or from user generated metadata such as ratings or tags. Other Meresco components provide an SRU and a RSS interface.<br/>Discover currently holds all catalogue records, the institutional repository metadata, an architecture bibliography and a test-set of Science Direct articles. In 2010, it is expected to grow to over 10 million records with content from Elsevier, IEEE and Springer (subject to negotiatons with these publishers) and various open access resources. We will also add the university’s collection of multimedia content, ranging from digitized historical maps, drawing and photographs to recent (vod- and) podcasts.<br/>In the proposed session, we would like to show you some examples of above mentioned functionality and explain how Meresco components work together to create this flexible system.