Logistics systems in today’s world are characterized by increasing dynamicity which arises from the trends of global economy. Defence & Business are no exception to this trend & require high quality decision-making support systems to optimize their logistics operations & meet the challenges. Big Data provides a wealth of information from which data can be analysed real time and knowledge synthesized from it. However, before this can be achieved appropriate tools & techniques are needed first to understand the semantics of Big Data, integrate Big Data & apply predictive analytics on it[JMC1] . In this project, we develop the foundation blocks to achieve this aim.

Defence logistics underpin Defence capability. An essential component of successful logistics systems is interoperability – the capacity for diverse systems to work together. However, diverse and separately developed information systems currently comprise the Defence logistics approach. Army operates the Military Integrated Logistics Information System (MILIS). However, the stove-pipe nature of MILIS resulted in interoperability issues with other systems in defence such as SLIM (Ships Logistics Inventory Management System) and Air Log (Air force logistics systems). All three systems are independently auditable. However, there are no synchronised support functions to allow unified inventory and asset tracking. This project seeks to address this issue.

Data mining as a core capability is an intrinsic part of every intelligent organization and enterprise since 9-11 terrorist attacks in the USA in 2001. It is used not only for counter terrorism cyber security, fraud detection, but also as a core technology is used in domains, including commerce and science, such as business intelligence; human disease studies, energy, global warming and social networks[JMC2]. One of the core objectives of data mining is to discover new and useful patterns from data that gives rise to discovery of new and previously unknown information and knowledge. In this project we develop Data Mining techniques to achieve the Intelligence in Defence applications

Defence assets comprise approximately $40.3 billion of specialist military equipment, $21.1 billion of land building and infrastructure and $5.7 billion of inventory. These significant financial expenditures, along with some of the recent incidents that demonstrate poor Risk management in Asset sustainment practices, brings with it questions regarding Australian Army’s effectiveness in establishing, implementing, and improving risk management practice in its assets sustainment in land force operational support. Best practices on Asset Management and ISO standards in recent years have provided with a Total Life Cycle Asset Management (TLAM), which stresses on a clear definition of risk measurement dimensions and risk prediction techniques in the context of Asset management. The objective of this research project is to develop such a cognitive tool that will eventually lead the Army in better risk management of their assets in the future.

The Navy Ships as Warehouses in MILIS project was started in 2012 through contractors like Deloitte, KPMG and CSC Australia. It is intended to enhance Navy’s existing Logistics Warehouse System. However, it has not moved beyond the definition stage. This project is to review existing legacy systems, data misalignment, account structure and to develop new proposals and techniques for enhancements to data alignment, systems interfaces and improve the systems dependencies in ADF Logistics Information Systems environment. In addition, address the organisational change issues such as Ships crew workload, training and business process change is also required.

In this project, our objective is to investigate and identify the root causes for the non-achievement of the SWIM objectives. The significance of the project is that it does not consider the problem as an Information Technology related one but looks it as a conjoint one that arises from people, processes, personnel and systems. Taking those factors into consideration, we will develop a report that that comprises of our analysis and recommendations.

The aim of this project is to develop a Risk Assessment as a Service (RaS) module that enables the service users of Cloud Computing services to make informed service-based decisions with the service providers. Developing such a framework will address one of the major obstacles of Cloud Computing mentioned in the literature. A prototype system of the proposed framework will be developed in this project that can be applied and tested in situ

Large amounts of data is collected and stored by various, government, industrial, commercial or scientific organisations and consists of (1) structured data, eg Asset Databases and (2) semi-structured/unstructured content such as customer complaints. As the complexity and volume of the data continue to increase, the task of classifying new unseen data and extracting useful knowledge from the data is becoming practically impossible for humans to do. This project develops a methodology for performing advanced analytics from both data and content conjointly. The techniques developed will be used to address important issues in Defence enterprise, business organizations and health medicine domain

This project investigates and develops techniques for Intelligent Asset Management. It mines and tracks multiple information resources for accurate Asset information sharing, knowledge synthesis and financial accountability through the use of Ontologies and XML profiles. Novel techniques for conjoint mining of structured and semi-structured data and management will be developed. The results will be validated using large real-world data sets provided by the partners

MIMOS (called MiMorph) has been successfully applied to the merging and cleansing of large real databases for PERKASO/SOCSO (Social Security department of Malaysia). There are a number of research issues which must be addressed for the development of the next generation of Data Merging and Data Cleansing Techniques and Tools. This project is designed to address these issues to allow MIMOS to be at the cutting edge of this highly significant Technology and move MIMOS into a world leading position in Data Merging and Data Cleansing.

In this project, the aim is to develop such an advanced Cyber Physical Systems for Intelligent Asset Tracking in Defence Logistics. Specifically it will develop a Cyber Physical Systems with enhanced Internet of Things, Auto ID Systems, Digital Pens and Smartphone Technologies for Intelligent Asset Tracking and Provenance of Goods and Assets for the future Australian Defence Force. It will result in considerable efficiency, productivity, capability development and lead to benefits for the Australian Defence and the commercial logistics sector as well.

In this research, we intend to develop such a framework on identifying, determining, assessing, mitigating and monitoring the source of risks, with the following 5 major steps:

1. identifying the risk sources in the logistics network;
2. determining the probability of failure or probability of the occurrences of those risks that may lead to failure in the logistics network;
3. ascertaining the adverse consequences in the logistics network as an occurrence of those risks;
4. assessing the impact of losses on financial, human and resources; 16 Centre of Excellence for Logistics Innovation and Information Dominance Research 2019 – 2020 Centre of Excellence for Logistics Innovation and Information Dominance Research 2019 – 2020 17

This thesis will address the issue by defining

• type of subtrees to be mined (embedded, induced or ordered)
• support definitions or constraints (transaction based support, occurrence- match support or hybrid-support) depending upon the aims of the query and the way how the information is organized in the augmented database.
• The association rules that are to be synthesized, extracted knowledge pattern should be analysed for their interestingness and relevance to remove any redundant, misleading and irrelevant rules. The aim is to increase the confidence in the determined association rules being a true reflection of the patterns that exist in the underlying augmented data.

Current research into purchase decisions in business markets suggest that commitment and switching costs are the primary reasons for customer loyalty. This study suggests that a third category also exists. “Relational inertia” occurs when repeat purchases from one supplier are due to habit or conservatism. This study considers two perspectives. At the individual level, purchase decisions are one of many demands that individuals face. Drawing on bounded rationality theory and job demands theory, it is likely that individuals consent to repeat purchases due to excess cognitive load. At the firm level, relational inertia may result from cultural conservatism, decision avoidance from senior managers and the presence of specific firm processes that align to the supplier. The overall objective of this study is to uncover the degree to which i) relational inertia is present in different purchase contexts (e.g. industry, product type, firm size, location) and, ii) to determine its relative effects on firm purchase decisions.

Most firms use ‘buying centres’ – groups of representative individuals – to make purchase decisions on behalf of the firm. These individuals exercise purchase authority on behalf of the firm as a whole and so this raises an important agency problem: do they serve all stakeholders’ interests equally well? This study seeks to investigate the degree of stakeholder perceived justice present in buying centre decisions. Three perspectives are of interest in this study. First, the formation of the buying centre shapes decisions, particularly in terms of membership stability, the definition of responsibility scope, and the parameters of the decision-making process. Second, the nature of the product/ service may influence the buying centre decision process; more complex products are likely to require greater expertise; services are likely to be more subjective. Third, user evaluations are likely to focus on engagement with products and services over time. These usage experiences are separate from the purchase process.

The conflict between economic (the profit motive) and customer needs is a well-documented phenomenon facing firms. However, few studies consider the nature of this trade-off across supply chain networks, particularly in life or death customer circumstances. This study seeks to understand this trade off across three primary dimensions. First, the nature of supply chain networks is such that the responsibility for fulfilling customer needs is distributed amongst supply chain network members. How does this distributed mode of responsibility influence end user outcomes? Second, most supply chain network members divide their focus between working collaboratively with other supply chain network members and on internal issues. To what extent does customer orientation exist in specific actors within the supply chain? How does this affect the economic-social trade-off? Third, what approaches or methods (i.e. resources, systems, capital) have supply chain members used to adapt to these circumstances? We have taken conscientious efforts to develop its core competencies in several key areas through internally funded projects. Projects such as data mining, data cleansing, business intelligence and SMEs, corporate social responsibility, assistive devices, smart homes, smart grids, ontologies, cloud computing, service oriented architectures, spam filters, social networks, regional education and skill shortages, carbon trading & many others, are ongoing & producing significant academic outputs & commercial benefits. We collaborate with numerous Government Public Sector, Defence and Industries in Australia and internationally. Our most recent key industry and government funded projects include:

In the modern world, e-commerce interactions are increasingly being carried out in virtual environments, thereby increasing the possibility of fraudulent transactions due to non-compliance by either communicating party. As such, there is an increased need for developing tools which help in making informed interaction-based decisions. Risk-based Decision Support System (RDSS) is a complete and comprehensive methodology developed at DEBII for assessing, expressing and managing risk in e-commerce interactions. RDSS is the first and only one of its type for risk assessment, measurement and management in e-commerce interactions. The objective of the project is to assess, analyse the level of risk as a function of its constituents in collaborative environments, and then manage it while decision-making. In order to achieve this, RDSS develops an architectural framework to ascertain and model the level of performance risk and financial risk according to the specific characteristics of the interaction, and according to their uncertainty when determining each of them at a certain point in time in the future. The risk in the interaction is then analysed and quantified according to its constituents by using two mathematical measures; the possibility theory and fuzzy logic. The output is then presented to the user in graphical format for better understanding. Based on the analysed risk, RDSS then carries out the steps of risk management and utilises it while making informed interaction-based decisions. For risk management, RDSS utilises a mathematical approach which determines the impact of the interaction initiating the agent’s risk propensity on the determined level of risk in an interaction, based on which it recommends an interaction-based decision. It provides the user with an effective decision-making methodology in an open interaction environment. The output of the project, when applied to interactions in virtual environments, would help the interacting agent to maximise its interaction experience and expected benefits.

Business interactions are the engine which drives the economy of the modern world. By business interactions, we mean the multi-disciplinary areas in that domain and which might have financial implications to either agent involved. The interactions in these domains are carried out with the aim of achieving certain specific outcomes which are consequential for the progression, advancement and sustenance for the particular business or individual. Failure to achieve those specific outcomes might have far reaching consequences to the business or individual. One of the important outcomes of the result of failure might be the risk of experiencing financial loss in such interactions. 18 Centre of Excellence for Logistics Innovation and Information Dominance Research 2019 – 2020 Centre of Excellence for Logistics Innovation and Information Dominance Research 2019 – 2020 19 In this project, we analyse and assess this risk as an important constituent of making informed decisions within a broader perspective of business interactions. One of the areas in that domain in this project for risk quantification is the probabilistic assessment of loss in revenue generation in Demand-Driven Production. In today’s competitive world, manufacturers are constantly subjected to massive pressure to reduce their operational costs and at the same time improve or increase their production efficiency. Cost reduction relating to production is not a bad thing for the manufacturers, but doing this implies that they have to shift or to adopt a new process for producing or manufacturing their goods. One such process that the manufacturers have to adopt is to produce and deliver the consumers’ orders within a fixed timeframe in order to obtain the required revenue from the transactions. In this project, we consider processes from the manufacturers’ perspective and assess the probabilistic level of risk incurred by not fulfilling the required demand within the given period. We also look at the level of financial consequences to the manufacturer as a result of not fulfilling the required demand. The output of this project will have far-reaching consequences when applied to the manufacturing industry.

The transportation system contributes 18% of the nation’s carbon emissions. Diesel powered logistics vehicle are a major contributor to this. Conversion of these diesel trucks to LNG (Liquefied Natural Gas) will help achieve better economies and emissions profiles. This poses challenges and requires strong evidence to convince truck companies of the benefits. This is achieved by real time monitoring of the Diesel to LNG converted trucks in actual operation and different contexts. This information is then aggregated in a repository using a web enabled Cyber Physical System. This is analysed using knowledge discovery techniques. This knowledge is used to develop and design an intelligent controller for the LNG/Diesel converted trucks.

The emergence of data with complex structures is evident in many domains such as, Bioinformatics, Chemistry, Web Intelligence applications, Business Process Modelling, scientific knowledge management, etc. When such data sources are used, the attributes of the domain are often organised in a hierarchical (tree) or graph structure to enable a more semantic representation of (complex) properties and relationships of data objects. Mining of such data poses additional challenges in the data mining field, as it requires the development of data mining methods capable of taking into account the complex structures of the data and preserving the relationships and structural properties in the extracted knowledge patterns. To enable association rule mining of tree-structured data (e.g. XML), we have developed a general framework for mining of frequent ordered and unordered induced / embedded subtrees from a database of rooted, ordered, labelled trees using any of the existing support definitions. A number of important applications of tree mining are also investigated, such as web mining, knowledge matching, protein structure analysis and mining of health information. More recent work is focused on the use of classification and clustering techniques on tree-structured data.

Traditional research on data quality and data cleaning focuses on individual records (i.e. intra-record) stored on the databases. A number of techniques have been proposed for data cleansing using common statistical, clustering or inference based techniques. All of these operate on the schema level and intra-instance levels only to clean existing data in the database. They do not provide any method to ensure that future data entered into the system is free from errors. Our focus in on a data cleansing approach that operates on the business logic, schema, record matching and intra-instance levels so as to not only clean existing data errors in a given database, but ensures that newly entered data are error free. Automated and scalable data mining techniques are used in each of the phases of data cleansing process, to identify common and unexpected types of errors in the database. Automated techniques are used to detect the source of the data quality problems through business activity and system logic monitoring, and prevent their re-occurrence by validation and standardisation of both data entry and business executions.

Digital information within an enterprise consists of (1) structured data and (2) semi-structured/unstructured content. The structured data includes enterprise and business data like sales, customer and product information, accounts, inventory, etc. while the content includes contracts, emails, customer opinions, transcribed calls, etc. The structured data is managed by the relational database system (RDB), the semi/ unstructured content is managed by the content manager, and these two exist as silos. This leads to information of different types being in their own silos being managed and queried separately. This is undesirable since the information content of these sources is complementary. This project is concerned with developing a methodology and techniques for deriving business intelligence and novel knowledge from structured, semi-structured, and unstructured information conjointly. This methodology would be useful in several business intelligence and information integration applications, such as managing customer attrition, targeted marketing, fraud detection and prevention, compliance and customer relationship management as well as a number of fields which involve information of high complexity such as Bioinformatics, Logistics, Business and Electrical Power Systems (EPS). The broad aims of the project are to:

• use structured data to disambiguate text segments and link them to records for BI investigations
• use ontologies to disambiguate and annotate content segments to permit BI investigations
• develop methods of BI analysis for conjoint analysis of linked structured and content elements
• Apply these techniques for investigation of business problems, biomedical problems and EPS.

This project develops methodologies and systems to allow logistic companies to form coalitions with worldwide logistic providers. Using this approach, supply chain services and physical resources are coupled and extended beyond their typical region of operation. It involves the coupling of e-Transportation, e-Warehousing, e-Hub Connector and one-stop-shop, P2P (Partner to Partner) and B2B (Business to Business). This has the potential of allowing medium sized, regionally based, logistics providers to compete on the world stage with giant logistic providers such as UPS and FedEx. The project builds up a virtual collaborative logistics consortium that is especially suited to SME (small medium enterprise) supply chain providers, partners and alliances. The proposed IT infrastructure will allow the seamless exchange of information between the international logistics partners wherever they are and whenever it is necessary. This enables global supply chain management for all national / international clients, partners, customers, suppliers and buyers around the world to track the details and movement of goods anywhere at any time. The system also supports the need for communication and co-ordination between all partner transporters and warehouses around the world.

Global warming is becoming a big problem and carbon emissions from a variety of sources are the cause of it. To control emission, a number of carbon emission reduction policies and schemes such as the Kyoto Protocol & COP15 treaty have been reached and put in place. Many accounting models have already been proposed 20 Centre of Excellence for Logistics Innovation and Information Dominance Research 2019 – 2020 Centre of Excellence for Logistics Innovation and Information Dominance Research 2019 – 2020 21 in the current literature to solve the problem of responsibility ambiguity. However, the current accountability models are proposed for the general industries and not for the aviation industry. We feel that these models cannot be applied directly to the aviation industry since factors of influence are significantly different. In Aviation, it involves a mix of international and national factors such as the accountability and the implications of members and non-members of the climate change treaties in different countries. Hence, taking into account all the determinant factors and different stakeholders involved in the process of the carbon accounting, we are proposing an efficacious and fair accountability model for the aviation industry in our research. This accountability model can be used to assist Australian government in coming up with a fair tax ‘relief/ subsidy’ scheme for the aviation companies for more sustainable tourism to the country; since inclusion of aviation into the carbon reduction scheme is going to be taxing to the aviation and tourism industry growth.

The aim of this research is to propose and validate a framework for carrying out defeasible argumentative reasoning in semantic web applications. Web is a source of huge amount of data and semantic web efforts are targeted towards making web data contents machines understandable. This will have significant impact on the way information is exchanged and businesses are conducted. As ontology layer of semantic web has got enough maturity (i.e. standards like RDF, RDFs, OWL, OWL 2) the next step is to work on logic layer for development of advance reasoning capabilities for knowledge extraction and efficient decision making. Adding logic to web means use of rules to make inferences. Rules are a way to express business processes, policies, contracts etc but most of the studies have focused on use of monotonic logics in layered development of semantic web which provides no mechanism for representing mechanism for representing incomplete information and handling of contradictory information. These limitations are inherited in Description Logics being subset of predicate logic. Defeasible logic programming is based on nonmonotonic logic and has been used in software agents for carrying out goal driven defeasible reasoning. Defeasible reasoning is a rule based approach to perform reasoning on incomplete, inconsistent and uncertain information and priorities are used to resolve conflicts among rules. Semantic web is source of defeasible knowledge as its open by nature and subject to inconsistencies deriving from multiple sources; therefore, it is not possible to define priorities in advance among conflicting rules. Additionally, quantitative approaches for reasoning on semantic web are also criticized for their in ability to generate easy to understand and logically clear result. We are interested in to exploiting the power of defeasible logic and argumentation for data driven reasoning on semantic web by identifying the issues involved in mapping of RDF/OWL ontologies to defeasible logic programming, how to carry out argumentative reasoning on semantic web, how DeLP rules can be shared on web and how tractable, customizable results can be represented to user.

This research aims to develop a framework to particularly assist Software Engineering Ontology evolution and management by using a semantic wiki based approach. Software Engineering Ontology encompasses common shareable software engineering knowledge and software engineering concepts, how and why they are related. However, most existing ontologies including the Software Engineering Ontology are derived from a single perspective which can be complicated for users, have a lack of maintainability, and become obsolete and impracticable ontologies. Additionally, the knowledge encoded in the ontologies is not static but should evolve. To overcome these impediments to the Ontology Evolution, this research will use a semantic wiki based approach which provides an environment for discussion and formalizing ontology issues, supports ontology evolution, and assist maintaining the versions of ontology. Ontology evolution is one of the main issues that Ontology users face today. The issues include lack of communication between offshore and onshore staff in the development and evolving ontology in Defence domain. Additionally, a lot of work for ontology engineers involves updating the ontology manually. The process of evolving the ontology requires work from the domain experts. There has not been a cost effective communication method to discuss and agree upon the changes. In this project, the use of a lightweight community-driven approach is developed in order to enhance the communication on the ontology evolution. This will enable a more efficient and effective method to communicate and take decisions in a better way.