Defining personalized concepts for XBRL using iPAD‐drawn fuzzy sets
Date | 01 April 2018 |
Author | Marek Z. Reformat,Nhuan D. To,Ronald R. Yager |
Published date | 01 April 2018 |
DOI | http://doi.org/10.1002/isaf.1426 |
RESEARCH ARTICLE
Defining personalized concepts for XBRL using iPAD‐drawn
fuzzy sets
Marek Z. Reformat
1
|Ronald R. Yager
2
|Nhuan D. To
1
1
University of Alberta, Edmonton, Canada
2
Iona College, New Rochelle, NY, USA
Correspondence
Ronald R. Yager, Iona College, New Rochelle,
NY, USA.
Email: yager@panix.com
Summary
An efficient and effective analysis of business data requires a better understanding of
what the data represents, and to what degree. A human‐like way of accomplishing
that without being too detailed yet learning more about data content is to summarize
and map the data into concepts familiar to a person performing analysis. Processes of
summarization help identify the most essential facts that are embedded in the data.
All this is of significant importance for analysis of large amounts of business data
required to make good and sound financial decisions.
There are two aspects enabling more comprehensive yet easier processing of data: a
standardized representation format of financial data; and a human‐friendly way of
defining concepts and using them for building personalized models representing pro-
cessing data. The first of the aspects has been addressed by the eXtensible Business
Reporting Language (XBRL)—a standardized format of defining, representing and
exchanging corporate and financial information. The second aspect is related to pro-
viding individuals with the ability to gain understanding of data content via determin-
ing a degree of truth of statements summarizing data based on their own perception
of concepts they are looking for.
In this paper, we introduce a tablet application—Tablet‐based input of Fuzzy Sets (TiFS)
—and demonstrate its usefulness for entering personalized definitions of concepts
and terms that enable a quick analysis of financial data. Such analysis means utilization
of soft queries and operations of aggregation that extract and summarize the data and
present it in a form familiar to analysts. The application allows for defining concepts
and terms with ‘finger‐made’drawings representing a person's perception of con-
cepts. Further, these definitions are used to build summarization statements for
exploring XBRL data. They are equipped with ‘drawn’definitions of linguistic terms
(e.g. LARGE,SMALL,FAST) and linguistic quantifiers (e.g. ALL,MOSTLY), and enable
summarization of data content from the perspective of a user's interests. The ‘drawn’
linguistic terms and quantifiers represent membership functions of fuzzy sets. Utiliza-
tion of fuzzy sets allows for performing operations of data summarization in a human‐
like way. The application of TiFS illustrates ease of inputting personalized definitions
of concepts and their influence on the interpretation of data. This introduces aspects
of personalization and adaptation of artificial intelligence systems to perceptions and
views of individuals. The proposed application is used to perform a basic analysis of
an XBRL document.
Received: 15 November 2017 Accepted: 12 February 2018
DOI: 10.1002/isaf.1426
Intell Sys Acc Fin Mgmt. 2018;25:73–85. Copyright © 2018 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/isaf 73
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