Recent Patents on Engineering  Send Orders for Reprints to reprints@benthamscience.net Recent Patents on Engineering, 2023, 17, e280322202673 PERSPECTIVE Wisdom Mining: Future of Data Mining 2 Salma Khan 1 and Muhammad Shaheen 2,* 1 Department of Software Engineering, Foundation University Islamabad, Rawalpindi Campus, Pakistan; 2 Faculty of Engineering & Information Technology, Foundation University Islamabad, Rawalpindi Campus, Pakistan Abstract: Data mining has been instrumental in the extraction of some useful knowledge from data. The purpose of data mining has always been to focus on searching for methods to extract instead of revealing the implicit models of the data. The outcome of the process of data mining is the knowledge that is represented by different visualization techniques. Knowledge obtained through data mining is not effective without the intervention of a domain expert who uses that knowledge to make a decision. On the other hand, human participation has the potential to influence and predis- pose decisions. Human participation in the process of data mining is still subjective and cannot be automated. A possibility to look into this quandary is the conversion of these subjective factors into some measurable parameters. This predicament leads to the development of an area that can be re- ferred to as "Wisdom Mining," which will consist of procedures to add wisdom to the extracted knowledge. Wisdom mining, if it is proposed as an extension to data mining, exhibits the need for certain factors, methods, and measures beyond the methods and measures used in the data mining process. The factors proposed in this article for a seamless transition from data to wisdom mining are context, utility, time, and location. There are two possibilities to use these factors for the extrac- tion of wisdom from data. One is to develop new algorithms for wisdom mining from scratch, keep- ing these four factors as major placeholders. The second approach is to add these four factors to the existing algorithms of data mining to get wise patterns as outcomes. The paper proposed a second approach for laying the foundation of this new domain of wisdom mining. A R T I C L E H I S T O R Y Received: December 16, 2021 Revised: December 30, 2021 Accepted: January 12, 2022 DOI: 10.2174/1872212116666220328121113 Keywords: Actionable knowledge, context, data mining, location, time, utility, wisdom mining, wisdom. 1. INTRODUCTION Digital revolution, or the third industrial revolution, as leading experts entitle it, started in the 1960s, when the cost- effectiveness of computers was realized, and the organiza- tions started using computerized databases. In the 1970s, a new database model subtly changed the way organizations used to think about the databases as the exclusive focus was now shifted from logical table structure to data applications. Since then, complex database management systems (DBMS) have become an integral part of every field of work, includ- ing but not limited to business, medical, financial, and household accounts. Since the 1990s, data mining has emerged as an interdis- ciplinary field, seamlessly blending the technologies of sta- tistics, artificial intelligence, information science, machine learning, etc., in order to extract implicit and useful patterns applicable to a wide range of disciplines related to market- ing, education, finance, planning, medical, and security, to name a few. Data mining increased its diffusion in enterprises *Address correspondence to this author at the Faculty of Engineering & Information Technology, Foundation University Islamabad, Rawalpindi Campus, Pakistan; E-mail: dr.shaheen@fui.edu.pk in the last couple of decades. The reason might be a rapid increase in organized multisource heterogeneous data from gigabytes to terabytes to petabytes. This also had prompted the need for technological and algorithmic development of data analysis methods that were duly recognized and many algorithms of data mining and big data are added to the exist- ing data mining body of knowledge. These sophisticated algorithms for analyzing exploratory data were based on statistical models which were used to extract patterns from homogenous and heterogeneous big data, but the growth of heterogeneous data has always limited the application of traditional data analysis methods. Fig. (1) shows some main milestones of digital data evolution. On the business side, the maturity of business processes and services over the past couple of decades has driven the need for intelligent information extraction. Today’s organi- zational structure is more sophisticated, and decisions are largely dependent on implied trends in the data. Pattern recognition techniques were intended to reproduce the dis- tinctive characteristics of the human brain involved in deci- sion-making. The pattern-based decisions in today’s enter- prises are characterized by decision-maker experience, ethi- cal standards, emotions, and cognitive bias. 2212-4047/23 $65.00+.00 © 2023 Bentham Science Publishers