{"_buckets": {"deposit": "4de84738-a8ab-4157-895a-fd075441092c"}, "_deposit": {"id": "7308", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "7308"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00007308", "sets": ["672"]}, "author_link": ["20127", "20128"], "control_number": "7308", "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2007-09", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "246", "bibliographicPageStart": "235", "bibliographicVolumeNumber": "4675", "bibliographic_titles": [{"bibliographic_title": "Lecture Notes in Computer Science", "bibliographic_titleLang": "en"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "On the Internet, a large number of documents such as news articles and online journals are delivered everyday. We often have to review major topics and topic transitions from a large time-series of documents, but it requires much time and effort to browse and analyze the target documents. We have therefore developed an information visualization system called T-Scroll (Trend/Topic-Scroll) to visualize the transition of topics extracted from those documents. The system takes periodical outputs of the underlying clustering system for a time-series of documents then visualizes the relationships between clusters as a scroll. Using its interaction facility, users can grasp the topic transitions and the details of topics for the target time period. 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