Data Science: Heuristic (Definitions)

"Problem solving or analysis by experimental and especially trial-and-error methods." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"The mode of analysis in which the next step is determined by the results of the current step. Used for decision support processing." (Margaret Y Chu, "Blissful Data ", 2004)

"A type of analysis in which the next step is determined by the results of the current step of analysis." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)

"The mode of analysis in which the next step is determined by the results of the current step of analysis. Used for decision-support processing." (William H Inmon, "Building the Data Warehouse", 2005)

"An algorithmic technique designed to solve a problem that ignores whether the solution can be proven to be correct." (Omar F El-Gayar et al, "Current Issues and Future Trends of Clinical Decision Support Systems", 2008)

"General advice that is usually efficient but sometimes cannot be used; also it is a validate function that adds a number to the state of the problem." (Attila Benko & Cecília S Lányi, "History of Artificial Intelligence", 2009) 

"These methods, found through discovery and observation, are known to produce incorrect or inexact results at times but likely to produce correct or sufficiently exact results when applied in commonly occurring conditions." (Vineet R Khare & Frank Z Wang, "Bio-Inspired Grid Resource Management", Handbook of Research on Grid Technologies and Utility Computing, 2009)

"Refers to a search and discovery approach, in which we proceed gradually, without trying to find out immediately whether the partial result, which is only adopted on a provisional basis, is true or false. This method is founded on a gradual approach to a given question, using provisional hypotheses and successive evaluations." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)

"'Rules of thumb' and approximation methods for obtaining a goal, a high quality solution, or improved performance. It sacrifices completeness to increase efficiency, as some potential solutions would not be practicable or acceptable due to their 'rareness' or 'complexity'. This method may not always find the best solution, but it will find an acceptable solution within a reasonable timeframe for problems that will require almost infinite or longer than acceptable times to compute." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An experience-based technique for solving problems that emphasizes personal knowledge and quick decision making." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

[heuristic process:] "An iterative process, where the next step of analysis depends on the results attained in the current level of analysis" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"An algorithm that gives a good solution for a problem but that doesn’t guarantee to give you the best solution possible." (Rod Stephens, "Beginning Software Engineering", 2015)

"Rules of thumb derived by experience, intuition, and simple logic." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Problem-solving technique that yields a sub-optimal solution judged to be sufficient." (Karl Beecher, "Computational Thinking - A beginner's guide to problem-solving and programming", 2017)

"An algorithm to solve a problem simply and quickly with an approximate solution, as compared to a complex algorithm that provides a precise solution, but may take a prohibitively long time." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)