Web and knowledge-based decision support system for measurement uncertainty evaluation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIn metrology, measurement uncertainty is understood as a range in which the true value of the measurement is likely to fall in. The recent years have seen a rapid development in evaluation of measurement uncertainty. ISO Guide to the Expression of Uncertainty in Measurement (GUM 1995) is the primary guiding document for measurement uncertainty. More recently, the Supplement 1 to the "Guide to the expression of uncertainty in measurement" – Propagation of distributions using a Monte Carlo method (GUM SP1) was published in November 2008. A number of software tools for measurement uncertainty have been developed and made available based on these two documents. The current software tools are mainly desktop applications utilising numeric computation with limited mathematical model handling capacity. A novel and generic web-based application, web-based Knowledge-Based Decision Support System (KB-DSS), has been proposed and developed in this research for measurement uncertainty evaluation. A Model-View-Controller architecture pattern is used for the proposed system. Under this general architecture, a web-based KB-DSS is developed based on an integration of the Expert System and Decision Support System approach. In the proposed uncertainty evaluation system, three knowledge bases as sub-systems are developed to implement the evaluation for measurement uncertainty. The first sub-system, the Measurement Modelling Knowledge Base (MMKB), assists the user in establishing the appropriate mathematical model for the measurand, a critical process for uncertainty evaluation. The second sub-system, GUM Framework Knowledge Base, carries out the uncertainty evaluation process based on the GUM Uncertainty Framework using symbolic computation, whilst the third sub-system, GUM SP1 MCM Framework Knowledge Base, conducts the uncertainty calculation according to the GUM SP1 Framework numerically based on Monte Carlo Method. The design and implementation of the proposed system and sub-systems are discussed in the thesis, supported by elaboration of the implementation steps and examples. Discussions and justifications on the technologies and approaches used for the sub-systems and their components are also presented. These include Drools, Oracle database, Java, JSP, Java Transfer Object, AJAX and Matlab. The proposed web-based KB-DSS has been evaluated through case studies and the performance of the system has been validated by the example results. As an established methodology and practical tool, the research will make valuable contributions to the field of measurement uncertainty evaluation.Brunel Universit

Software-defined measurement to support programmable networking for SoyKB

Campuses are increasingly adopting hybrid cloud architectures for supporting big data science applications that require "on-demand" resources, which are not always available locally on-site. Policies at the campus edge for handling multiple such applications competing for remote resources can cause bottlenecks across applications. To proactively avoid such bottlenecks, we investigate the benefits in the integration of two complementary technology paradigms of software-defined measurement and programmable networking. The integration inherently allows flexible end-to-end application performance monitoring and dynamic control of big data application flows using: (a) software-defined networking for transit selection to remote sites, and (b) pertinent selection of local or remote compute resources. Using the Soybean Knowledge Base (SoyKB) as an exemplar application, we demonstrate the benefits of software-defined measurement to support programmable networking. As part of our study methodology, we first profiled the original data flows within SoyKB's use of iPlant public cloud resources, and identified bottleneck cases such as slow data transfer speeds, lack of performance information (e.g., such as cluster availability, job status and network health) and inflexible control of hybrid cloud resources to address application-specific needs. The profiling study motivated us to propose a new hybrid cloud architecture for SoyKB workflows that utilize end-to-end performance measurements to support a cost-optimized selection of sites for computation and effective traffic engineering at the campus-edge. We validate our approach for a SoyKB workflow use case that we setup on a wide-area overlay network testbed implementation across two geographically distributed campuses. Our performance results show a notable performance improvement in SoyKB remote data transfer flows that utilize iRODS and TCP tuning mechanisms in the presence of cross-traffic big data flows. Additionally, we implement a SoyKB system that provides: (i) flexible workflow performance analytics at a glance to SoyKB researchers handling big data, and (ii) web service mechanisms for interfacing with popular dynamic resource management technologies such as OpenStack, HTCondor and Pegasus

Transfer Learning for Improving Model Predictions in Highly Configurable Software

Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'17

Investigating the impact of networking capability on firm innovation performance:using the resource-action-performance framework

The author's final peer reviewed version can be found by following the URI link. The Publisher's final version can be found by following the DOI link.Purpose The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry. Design/methodology/approach Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses. Findings The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance. Originality/value Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research

A Framework proposal for monitoring and evaluating training in ERP implementation project

During the last years some researchers have studied the topic of critical success factors in ERP implementations, out of which 'training' is cited as one of the most ones. Up to this moment, there is not enough research on the management and operationalization of critical success factors within ERP implementation projects.Postprint (published version

Open innovation process via technology transfer and organizational innovation

Purpose: The main aim of the paper is to determine the relationship between technology transfer as a part of open innovation process on organizational innovation in surveyed firms, what has been investigated empirically. Design/Methodology/Approach: The study is based on a survey on firms (n=100) located in Poland. The research model defines the relationship between technology transfer and organizational innovation in the enterprises has been developed. The survey uses the multi stepwise regression modelling. Findings: The surveys has determined the positive relationship between technology transfer and organizational innovations of researched firms. A strong direct effect on surveyed firm’s organizational innovations have different channels of material and non-material technology transfer as well as coopetition for innovation. Practical implications: From the practical point of view it is important that practitioners as managers should invest more effort in innovation activity connected with software development in their companies and coopetition, firstly started from cooperation with the research centers and universities for innovation, based on common innovation projects. Originality/Value: Regarding its methodology, this survey is one of the first studies examining the relationship between technology transfer and technological innovation of firms based on individual-level data and according to the theory. This findings suggest that measurement of technology transfer and its specific channels should be developed further as it is important in firm competitiveness and innovativeness level of firms.peer-reviewe

Indicators of university-industry knowledge transfer performance and their implications for universities: evidence from the UK’s HE-BCI survey

Focusing on the measurement of universities’ performance in knowledge transfer, we outline some critical issues connected with the choice of appropriate indicators: in particular, we argue that, in order to allow universities to correctly represent their knowledge transfer performance, indicators should include a variety of knowledge transfer activities, reflect a variety of impacts, allow comparability between institutions, and avoid the creation of perverse behavioural incentives. To illustrate these issues empirically, we discuss the case of the United Kingdom’s Higher Education –Business and Community Interaction (HE-BCI) survey. We show that the indicators used to measure and reward universities’ engagement in knowledge transfer are not fully comprehensive, they are better suited to capture the impact of certain types of activities than others and they are influenced by institutional strategies and characteristics rather than simply reflecting different performances. The conclusions explore some promising directions to address some of these problems

Preliminary Results in a Multi-site Empirical Study on Cross-organizational ERP Size and Effort Estimation

This paper reports on initial findings in an empirical study carried out with representatives of two ERP vendors, six ERP adopting organizations, four ERP implementation consulting companies, and two ERP research and advisory services firms. Our study’s goal was to gain understanding of the state-of-the practice in size and effort estimation of cross-organizational ERP projects. Based on key size and effort estimation challenges identified in a previously published literature survey, we explored some difficulties, fallacies and pitfalls these organizations face. We focused on collecting empirical evidence from the participating ERP market players to assess specific facts about the state-of-the-art ERP size and effort estimation practices. Our study adopted a qualitative research method based on an asynchronous online focus group