Incident Mining Using Structural Prototypes

Abstract

Abstract. Software and other technical products offered to a mass market have a high demand on support and help desks. A tool for automated classification of incident reports, errors and other customer requests which offers previous (successful) hints or solution procedures could efficiently decrease support costs. We propose an approach to mining incidents and other customer requests for support based on generalising structural prototypes from structured data. Retrieval can then be efficiently realised by matching incoming requests against prototypes. We present an application to incident reports in an SAP business information system. Several variants of structure generalisation algorithms were realised and performance for an example test base was evaluated with promising results.

ACUOS: A System for Modular ACU Generalization with Subtyping and Inheritance

“…For instance, the expression father(X,Y) is a generalizer of both father(john,sam) and father(tom,sam), but their least general generalizer, also known as most specific generalizer (msg) and least common anti-instance (lcai), is father(X,sam). Applications of generalization arise in many artificial intelligence areas, including case-based reasoning, analogy making, web and data mining, ontology learning, machine learning, theorem proving, and inductive logic programming, among others [5,12,13,16].…”