SOA Governance – Road into Maturity

There is a general consensus that SOA benefits could be reached but it is unclear how to achieve this. Research shows that the problems with SOA governance in practice are among the major reasons of SOA failures. Based on a literature review, this study first proposes a list of SOA aspects to be considered when implementing SOA governance. By adopting an interpretive research methodology based on interviews, this research paper makes two contributions: it addresses the practical matters that are major concerns for organisations to achieve a higher maturity level with their SOA, and it reveals the importance of the key SOA aspects in building strong governance and consequently reaching a higher maturity level. The expected result should deliver a theoretical contribution to SOA maturity in relation to SOA governance; it could provide organisations with new awareness in assessing their level of maturity and provide recommendations

Using Service Oriented Computing for Competitive Advantage

Research literature in strategic management indicates that firms may gain a competitive advantage in rapidly changing market environments by concentrating on their dynamic capabilities – i.e., product flexibility and agility in organizational transformation in response to rapidly changing market conditions and customer requirements. Service-oriented computing (SOC) has emerged as an architectural approach to flexibility and agility, not just in systems development but also in business process management. There is, however, a lack of critical research assessing the strategic impact of SOA on the competitiveness of organizations. The intent of this paper is to empirically examine the conduits through which serviceoriented architectures (SOAs) may exert influence on dynamic capabilities within firms. The results could potentially assist in evaluating if and how the adoption of service-oriented architecture may help achieve key dynamic capabilities, giving the enterprise a competitive edge

Modelling Exploratory Analysis Processes for eResearch

Financial markets produce high-frequency data and analysing it involves transforming the data, detecting patterns and testing financial models. These actions or steps form an exploratory analysis process (EAP). ADAGE is an open SOA incorporating a BPMS that allows users to model EAPs by composing analysis services. A typical application scenario is used to evaluate ADAGE’ s ability to express an EAP as a business process. It is shown that current BPMS technology cannot satisfactorily represent EAPs as fully executable business processes. Using the theory of situation awareness, EAPs are shown to be dynamic in nature. Hence three extensions based on the late composition technique are proposed: (1) a dynamic process representation of EAPs; (2) a process execution model; and (3) process templates to automate repetitive steps of EAPs

Cloud Computing Adoption: An Effective Tailoring Approach

Many organisations are currently moving their legacy systems to the cloud as it offers on-demand, elastic, and pay-as-you-go service models. However, different cloud migration scenarios can involve different activities during the migration process using one of many existing migration methods to make legacy cloud-compliant. There is no universally superior or applicable method for all cloud migration scenarios. In situations like this, designing situation-specific methods that fit several existing migration scenarios would be beneficial to the industry. The literature review reveals that issues surrounding the method tailoring for the cloud migration have not been addressed yet. To effectively harness this shortcoming, the idea of situational method engineering approach is applied to develop a framework for designing and maintaining bespoke methods for moving legacies to the cloud. The paper demonstrates the applicability of the framework via presenting two scenarios of creating, configuring, and sharing situational methods

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources

Enhancing Automated Decision Support across Medical and Oral Health Domains with Semantic Web Technologies

Research has shown that the general health and oral health of an individual are closely related. Accordingly, current practice of isolating the information base of medical and oral health domains can be dangerous and detrimental to the health of the individual. However, technical issues such as heterogeneous data collection and storage formats, limited sharing of patient information and lack of decision support over the shared information are the principal reasons for the current state of affairs. To address these issues, the following research investigates the development and application of a cross-domain ontology and rules to build an evidence-based and reusable knowledge base consisting of the inter-dependent conditions from the two domains. Through example implementation of the knowledge base in Protege, we demonstrate the effectiveness of our approach in reasoning over and providing decision support for cross-domain patient information.Comment: The paper has been published at the 24th Australasian Conference on Information Systems, 4-6 Dec 2013, Melbourne. The paper can be found at: http://mo.bf.rmit.edu.au/acis2013/382.pd