A domain-specific decision support system for knowledge discovery using association and text mining

Abstract

We propose a novel association and text mining system for knowledge discovery (ASTEK) from the warranty and service data in the automotive domain. The complex architecture of modern vehicles makes fault diagnosis and isolation a non-trivial task. The association mining isolates anomaly cases from the millions of service and claims records. ASTEK has shown 86% accuracy in correctly identifying the anomaly cases. The text mining subscribes to the diagnosis and prognosis (D&P) ontology, which provides the necessary domain-specific knowledge. The root causes associated with the anomaly cases are identified by discovering frequent symptoms associated with the part failures along with the repair actions used to fix the part failures. The best-practice knowledge is disseminated to the dealers involved in the anomaly cases. ASTEK has been implemented as a prototype in the service and quality department of GM and its performance has been validated in the real life set up. On an average, the analysis time is reduced from few weeks to few minutes, which in real life industry are significant improvements.

Decision support for improved service effectiveness using domain aware text mining

“…From the useful set of rules, a fault diagnosis classifier was constructed and utilized for fault diagnosis of power transformers. Rajpathak et al [16] presented a domain specific Association and Text mining system for Knowledge discovery (AS-TEK) for identifying successful repair cases and the anomalies in the field (such as high time to repair, unnecessary part changes, and unnecessary repairs). Here, association mining is used to identify the symptom-repair patterns that are observed in the field along with the variations in costs associated with these patterns.…”

“…These either make use of the knowledge represented in some structured way (such as ontology and case base) for fault diagnosis [13][14][15], or address how new knowledge can be discovered, from the large historical data, to improve the fault diagnosis and troubleshooting process [16]. From a decision making point-of-view, however, the objective is not only to diagnose the problem, but to repair the device or cure the patient [17].…”

Aircraft interior failure pattern recognition utilizing text mining and neural networks

“…There are many interesting fields of research such as detection of similarities between patent documents and scientific publications (Magerman et al 2010); examining mobile learning trends (Hung and Zhang 2011); discovering a multi-functional metal-binding glycoprotein that exhibits many biological functions of interest to many researchers from the fields of clinical medicine, dentistry, pharmacology, veterinary medicine, nutrition and milk science (Shimazaki and Kushida 2010); identifying fall-related injuries in electronic medical record (Tremblay et al 2009); mining business policy texts for discovering process models (Li et al 2010); discovering knowledge by opinion mining from noisy text data (Dey and Haque 2009); tracking what people are saying, finding influencers, and using many social network analytic tools to analyze the underlying social networks embedded within the blogosphere (Macskassy 2011) and (Huang et al 2011) and with emails via clustering and pattern discovery (Manco et al 2008); identifying the anomaly cases for knowledge discovery from the warranty and service data in the automotive domain (Rajpathak et al 2011); discovering frequent musical patterns (motifs) that is a relevant problem in musicology (Jiménez et al 2011). In Biology, text mining has new challenges as can be seen in Dai et al (2010); a good example of text mining on language recognition can be seen in Al-Jumaily et al (2011), where Arabic, the most widely spoken language in the Arab World is identified on the web.…”

Development of a knowledge base based on context analysis of external information resources

“…A variety of software automation of human activities are used to solve this problem. However, it is necessary to adapt them to the specifics of a particular problem area (PrA) and its contexts for the effective operation of these tools [1, 2,7,10,18,19,20].…”