{"id":4768,"date":"2017-09-20T09:50:32","date_gmt":"2017-09-20T07:50:32","guid":{"rendered":"https:\/\/www.obsidiansoft.com\/?p=4768\/"},"modified":"2023-09-27T11:21:13","modified_gmt":"2023-09-27T09:21:13","slug":"articulo-investigacion-logica-difusa-obsidian","status":"publish","type":"post","link":"https:\/\/www.obsidiansoft.com\/en\/blog-en\/articulo-investigacion-logica-difusa-obsidian\/","title":{"rendered":"Research article about fuzzy logic applied to Obsidian"},"content":{"rendered":"

[vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern”][vc_column][vc_column_text]The USC team of researchers<\/a> involved in the development of Obsidian continues to increase its scientific production. They have just published a reasearch article\u00a0about fuzzy logic titled Using Fuzzy Sets in a Data-to-Text System for Business Service Intelligence<\/a>. This article was presented at the 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets.<\/p>\n

\"Research\"Obsidian'sThe research article describes the techniques of fuzzy logic used in the automatic interpretation into natural language of the indicators presented by Obsidian in dashboards and reports. This way, it is possible to incorporate fuzzy quantifiers, such as “significantly better” or “significantly improving”, when describing time series. This gives flexibility to temporal pattern detection algorithms, for example,\u00a0 in comparisons of monthly average values with their corresponding historical values. So, the automatically generated texts gain become more natural and easy to understand.<\/p>\n

Practical application of this research article about fuzzy logic<\/h2>\n


\nAs can it be seen in the following example, the text on the right is generated automatically from the four time series shown in the graphs on the left. The automatic generation of text uses the fuzzy logic algorithms presented in the article.<\/p>\n

\"\"\u00a0\u00a0\"\"<\/p>\n

You can also watch a\u00a0presentation about the automatic interpretation of indicators implemented in Obsidian<\/a>.<\/p>\n

Obsidian’s scientific production<\/h2>\n


\nThis new publication joins the one already from in 2016 about process mining in service management tools, Process Mining in IT Service Management: A Case Study<\/a>, published in ATAED@Petri Nets\/ACSD 2016: 16-30.<\/p>\n

Further information<\/h2>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern”][vc_column width=”1\/2″][vc_column_text]If you want more information about Obsidian’s scientific production or other features of our platform, please contact us via the attached form.<\/p>\n

NOTE<\/strong> Only professional e-mail addresses are accepted. Requests from gmail, yahoo, hotmail, etc. accounts will not be processed.<\/p>\n

The personal data collected via the form will be used to process your request. You may exercise your rights of access, rectification and cancellation by writing to Obsidian Soft, calle M\u00e9ndez \u00c1lvaro, 20 \u00b7 18045 \u00b7 Madrid.<\/p>\n

You can also subscribe to our newsletter<\/a> or to our YouTube channel<\/a>, which currently has 1570 subscribers. You may also be interested in reading our blog<\/a>, as well as the snews section<\/a> of our website in order to find our more about Obsidian. Lastly, you can follow us on LinkedIn<\/a>, Facebook<\/a> and Twitter<\/a>.<\/p>\n

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