Authors:
Patrick Roocks
and
Werner Kießling
Affiliation:
University of Augsburg, Germany
Keyword(s):
R, Preferences, Preference SQL, Text Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Management and Quality
;
Data Management for Analytics
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Semi-Structured and Unstructured Data
;
Symbolic Systems
;
Text Analytics
Abstract:
Preferences are a well-established framework for database queries with soft constraints. Such queries select the best objects from large data sets according to a strict partial order induced by intuitive and semantically rich preference constructors. Together with functionality like grouping and aggregation, adapted from well-known database mechanisms, a very flexible preference framework has emerged in the last decade. In this paper we present R-Pref, an implementation of the preference framework in the statistical computing language R. R-Pref comprises less than 1000 lines of code and adheres to the formal foundations of preferences. It allows rapid prototyping of new preferences and related concepts. Exemplarily we present a use case in which a simple text mining example based on pattern matching is enriched by preferences. We argue that R-Pref paves the way for rapidly exploring new fields of application for preferences. Especially new semantic constructs for preference related o
perations together with equivalences of preference terms, being highly important for optimization, can be quickly evaluated.
(More)