Fault-tolerant context development and requirement validation in ERP systems

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Highlights

  • Context-aware classification methodology for Enterprise Resource Planning (ERP)

  • Semantics for ERP interactions regarding requirements of different stakeholders

  • Contextual requirement validity by comparing requirement model of the users with ERP

  • Decision trees to predict successful consignment deliveries for a local oil company

  • Decision trees to predict conflict-free feature models for configuring oil products

Abstract

This paper presents context development and requirement validation to overcome maintenance problems in Enterprise Resource Planning (ERP) systems. Using ERP data of a local petroleum firm, we employ knowledge integration to dynamically validate users' requirements, and to gather, analyze, and represent context through knowledge models. We also employ context-awareness to model the ERP context, along with a user requirement model. We employ context affinity to determine impact of these models on requirements' validation. We apply fault-tolerance on these models by using data mining to pre-identify delays in delivery of petroleum products, and to predict faulty contextual ERP product configuration.

Section snippets

Background and motivation

Context is a critical component in the interaction between humans and computers. It is the outcome of situations associated with participants of an interaction, and generally describes the facts that are relevant in a particular environment of interaction [10]. It can be generally categorized as a physical context (e.g., location), a computational context (e.g., a website analyzing the every-changing census information), and a user context (e.g., the demographic information about the user).

Research methodology

According to Raban and Garner, the main challenge in developing information systems is to transform the original user requirements into a working system that meets the complete set of these requirements [1]. This is a complex proposition and entails the development of a comprehensive context and its representation to create a system model. A pioneering work in this regard has been done by Sowa and Zachmann [13], who propose a taxonomy that relates real-world concepts to the concepts that

Fault tolerance in ERPs through data mining

In this section, we will apply data mining techniques to a particular business process of PSO, in order to pre-identify faults in this process, i.e., before they actually occur. In fact, the concept of “fault-tolerance” in the domain of ERP is quite limited. Specifically, the primary focus of many companies is to provide robust computer hardware that is capable of effectively and efficiently managing the workload of the ERP activities, e.g., the IBM Tivoli Workload Scheduler [14], Stratus

Predicting non-conflicting and contextual feature selections in ERP-based software product lines

A well-known ERP solution is a software product line (SPL) which is used to manage the configuration of one or more products [17], [18]. An SPL divides a contextual product configuration into different modules, with each module being configured separately. The contextual (product-based) configuration proceeds through the selection of a set of available features in each module, and the collection of all individual feature set selections is typically called a feature model. All available features

Conclusions

This paper has suggested a methodology for Enterprise Resource Planning (ERP) systems, based on context classification and context awareness. Our aim is to support the development of a comprehensive context which can be used to add “completeness” and verify requirements. We also present a classification of context, which marks an important extension to the definition of context as presented by Sowa and Zachman [13]. In our work, context awareness has been used as an enabler to add semantics to

Acknowledgment

This work is partially supported by three entities: 1) Pakistan State Oil (PSO), a well-known petroleum-based company based in Karachi, who provided us with ERP-related data under an NDA, 2) an anonymous local ERP-based software product line organization who also provided us ERP data under an NDA and 3) the Center for Research in Ubiquitous Computing (CRUC), which provided us the necessary hardware and software resources. In this regard, we would like to especially acknowledge Mr. Hisham and

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