[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-194813-105":53,"doc-detail-194813-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","document-metadata-extraction-multimodal-194813","Document Metadata Extraction (Multimodal)","","This document details a multimodal approach to extracting metadata from documents, combining text and image analysis. It outlines a system called 'DMiner' for Feature Engineering and Design Rule Mining, focusing on creating effective dashboard visualizations. The system generates candidate visualizations, filters them based on defined rules, and presents a refined set with confidence scores. Performance evaluation metrics such as Logic, Aesthetics, Helpfulness, and Overall Ratings are presented graphically, comparing different DMiner configurations ('Basic', 'Partial', 'Full') against default settings, human baselines, and designer-generated outputs across various dashboard sizes. The analysis includes statistical significance testing and confidence intervals, demonstrating the effectiveness of the DMiner system in improving dashboard design and user experience.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/document-metadata-extraction-multimodal-194813/194813/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/document-metadata-extraction-multimodal-194813/194813.png","ImageObject",442,249,{"name":88,"@type":89},"Himbo","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-02","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":79},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the primary goal of the DMiner system described in the document?","Question",{"text":108,"@type":109},"The primary goal of the DMiner system is to automate and improve the design of data visualizations, particularly in dashboards, by mining design rules and recommending effective arrangements and coordinations.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the DMiner system generate candidate visualizations and what happens next?",{"text":113,"@type":109},"The DMiner system first generates candidate visualizations. These candidates are then filtered and scored based on predefined design rules and criteria, with the aim of identifying the most effective and aesthetically pleasing options.",{"name":115,"@type":106,"acceptedAnswer":116},"What criteria are used to evaluate the effectiveness of different visualization designs and DMiner configurations?",{"text":117,"@type":109},"The effectiveness of visualization designs and DMiner configurations is evaluated based on several criteria including Logic, Aesthetics, Helpfulness, and Overall Ratings, with statistical significance testing to compare different approaches.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},194813,1789960426,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":79,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":25,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":47},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","| No. | Rule | Score |\n| --- | --- | --- |\n| 1 | If View A is Text (not Text table), then View A should be of the height or width of 1 . | 6.5 |\n| 2 | If View A and View B are of the same chart type, and they use the same fields on Y-axis, then View A should be to the left or right of View B. | 6.5 |\n| 3 | If View A and View B share more than 50% of the same fields, and they use color for the same fields, then View A should brush View B or View B should brush View A. | 6.25 |\n| 4 | If View A is Text (not Text table), then View A should be on the top right, top left, or top of other view types. | 6 |\n| 5 | If View A has more fields than View B, then View A should be on the bottom right, bottom left, or bottom of View B. | 5.5 |","cbCaicHzlN2jJ6bu","https://ap.wps.com/l/cbCaicHzlN2jJ6bu","pdf",10800041,"English","# DMiner: Draft Revised\n## A: Feature Engineering\n### Single-view Features\n### Pairwise-view Features\n## B: Design Rule Mining\n## C: Recommender\n## Visualizations and Performance Evaluation","[{\"question\":\"What is the primary goal of the DMiner system described in the document?\",\"answer\":\"The primary goal of the DMiner system is to automate and improve the design of data visualizations, particularly in dashboards, by mining design rules and recommending effective arrangements and coordinations.\"},{\"question\":\"How does the DMiner system generate candidate visualizations and what happens next?\",\"answer\":\"The DMiner system first generates candidate visualizations. These candidates are then filtered and scored based on predefined design rules and criteria, with the aim of identifying the most effective and aesthetically pleasing options.\"},{\"question\":\"What criteria are used to evaluate the effectiveness of different visualization designs and DMiner configurations?\",\"answer\":\"The effectiveness of visualization designs and DMiner configurations is evaluated based on several criteria including Logic, Aesthetics, Helpfulness, and Overall Ratings, with statistical significance testing to compare different approaches.\"}]","Document Metadata Extraction (Multimodal) | PDF",1788442918]