Project Archive | The ARC Centre for Information Resilience (CIRES)

Defining and Measuring Analytics Value

About the Project

Investments in data analytics technology are still growing strongly around the world. The 2019 SIM IT Trends and Issues study rated analytics technologies as the largest IT investment decision for companies for the last 10 years. While existing industry reports and preliminary information systems research highlight the strategic role of data in creating strategic value for organizations, much of the research in this area is anecdotal, without systematic understanding of value creation mechanisms and rigorous measurement approaches to gauge the value that can be solely attributed to data assets and capabilities.

This project, in collaboration with the highly successful Brisbane-based analytics startup, Aginic, will examine the strategic processes and outcomes of data-driven value creation, and will answer two intertwined research questions: What are the mechanisms of data-driven value creation and capture in contemporary organizational contexts? And how can organizations capture and objectively measure analytics-driven value?

This project commenced in February 2022 with the recruitment of PhD Researcher Daisy Xu who is based at The University of Queensland. Daisy will become part of Aginic’s multidisciplinary analytics teams, or squads, and will collaborate and participate in industry-led analytics projects, as part of the process of conducting engaged research. This project will develop a systematic methodology to define and measure the value of data and analytics for organisations. The methodology can provide Aginic and other firms investing in analytics with an evidence-based approach for ongoing value creation and measurement from data. Daisy is supervised by CIRES Research Director and Chief Investigator Professor Marta Indulska, Chief Investigator Dr Ida Asadi Someh (both from the UQ Business School) and Emma Freya, DesignOps Manager at Aginic.

This project is one of two CIRES projects with Aginic related to organisational and transformational aspects of data, algorithms, and AI. The second project, Developing Analytics-Driven Organisations, commenced in April 2023.

Aginic is an innovative organisation making a difference to businesses and communities across Australia by transforming the way people experience data. Aginic has seen rapid success since it was founded in 2014, and has grown to 100+ amazing people across Brisbane, Sydney and Melbourne. The organisation has become a trusted data analytics, cloud technology, agile delivery and design service for clients across a range of sectors. The word Aginic is a composition of “Agile Analytics” and that remains our focus. The CIRES projects co-designed with Aginic relate to organisational and transformational aspects of data, algorithms, and AI. The focus on “value creation with data” and “data-driven transformations” align with Aginic’s strategic objectives in ensuring business customers and communities are empowered to effectively use their data assets for value creation; better understand the best approach to embed effective data analytics teams across an enterprise; and justify ongoing investment in data related initiatives.

Developing Analytics-Driven Organisations

About the Project

Amidst the digitization wave, digital business leaders are investing in new data lake platforms, teaching their employees advanced data science skills, re-engineering business processes to run with analytics, and encouraging data-informed experimentation and risk taking. These activities infuse contemporary data analytics knowledge and skills across organizations, leading to the pervasive use of data and more efficient and/or more effective decisions, and ultimately to the development of data-driven organizations.

A lack of data-driven progress, in part, is rooted in knowledge integration challenges. Traditional forms of organizing encourage domain boundaries, which create deep specializations but also inhibit the combination and integration of firm knowledge. Domain boundaries pose challenges specific to the assimilation of analytics technologies that can engender new and different data-driven ways of working. This research takes a holistic approach to study patterns of interaction between analytics and business groups, which ultimately leads to integration of their knowledge and development of an effective and efficient data-driven organization.

In collaboration with the highly successful Brisbane-based analytics startup, Aginic, this project will seek to answer: How can data-driven organizations effectively integrate the specialized knowledge of analytics and business groups, diffuse it across the organization and use it pervasively for value creation?

This project commenced in April 2023 with the recruitment of PhD Researcher Jorge Retamales who is based at The University of Queensland. This project is focused on how data analytics can drive organisational transformation. In line with CIRES’s industry engagement objectives, the position is defined and co-funded in close collaboration with Aginic, an emerging leader in the field of analytics that employs agile and user-centric approaches to build their competitive solutions.

Jorge will become part of Aginic’s multidisciplinary analytics teams, or squads, and will collaborate and participate in industry-led analytics projects, as part of the process of conducting engaged research. This project will develop a systematic and organized approach to data-driven transformations and will help Aginic and other companies progress data-driven transformation journeys. Jorge is supervised by CIRES Chief Investigator Dr Ida Asadi Someh, and Research Director and Chief Investigator Professor Marta Indulska (both from the UQ Business School) and Emma Freya, DesignOps Manager at Aginic.

This project is one of two CIRES projects with Aginic related to organisational and transformational aspects of data, algorithms, and AI. The other project, Defining and Measuring Analytics Value, commenced in February 2022.

Customer Data Stories

About the Project

This project will investigate how the availability of collective and personalized data summaries impacts end-users and customers, and what behavioural changes are enacted as a result. Improving customer decision making is an under-studied area. In collaboration with global insurance company, Allianz Worldwide Partners Australia, (AWP Australia) this research will explore the use of data stories as an approach to improve customer engagement and decision making. A crowd-sourced approach will be utilised, and further in-the-wild studies will be conducted to validate and improve the method with the help of Allianz customer networks that span international health and life, travel, and automotive insurance.

The project commenced in January 2023 with the recruitment of PhD Researcher Elyas Meguellati, who is based at The University of Queensland. Elyas is working to improve data curation through a crowd-sourced approach. This project will require data analysis in a data-sparse environment focusing on personalised marketing, with the proposed model and research tasks able to be adapted and applied to other human-in-the-loop tasks. Elyas is supervised by CIRES Chief Investigator Associate Professor Gianluca Demartini, Centre Director Professor Shazia Sadiq, and Shane Downey, General Manager, Enterprise Data Management, at AWP Australia.

The project is one of two CIRES projects with AWP Australia related to organisational and transformational aspects of data, algorithms, and AI. The second project Value Measurement of Data Products commenced in October 2023.

Allianz Worldwide Partners Australia (AWP Australia) is a world leader in insurance and assistance, offering global solutions that span health and life, travel, and automotive. Customer driven, AWP Australia’s innovative experts redefine insurance services by delivering future-ready, high-tech high-touch products and solutions that go beyond traditional insurance.

As a data-driven company, AWP Australia utilises sound data management and practices backed by scientific research to effectively apply identified improvements. As a result, they are constantly uncovering ways of continuously improving data management and, in particular, goals surrounding smart automation and strong governance around automated decision making. Another key goal is to develop greater insights and frameworks based on understanding how data can be assessed, valued, and applied including perceptions and expectations of the consumers of insurance products. AWP Australia and CIRES have co-designed the collaborative projects as way to further these goals.

Value Measurement of Data Products

About the Project

Evaluating data products is a complex problem that transcends technical, organisational and customer perspectives. The provenance and transformations of data products through methods such as information extraction, fusion and aggregation add to the challenge. To date there is no rigorously developed framework for evaluating data products and the monetary, or public good value, it creates.

This project is a collaboration with Allianz Worldwide Partners Australia (AWP Australia) and will challenge and extend the current body of knowledge on value of data products including computation, human effort and perceived value, and deliver a domain-agnostic framework that will build business capacity on informing and prioritising data science projects. It will be led by Dr Rocky Chen, and focuses on how to maximise data driven value creation and capture. This project commenced in October 2023 with the recruitment of Zirui (Alice) Tan at The University of Queensland. Zirui is supervised by CIRES Chief Investigator Dr Rocky Chen, Affiliate Investigator Dr Wen Hua, Centre Director Professor Shazia Sadiq, and Partner Investigator from AWP Australia Mr Shane Downey.

This is one of two CIRES projects with AWP Australia related to organisational and transformational aspects of data, algorithms, and AI. The other project, Customer Data Stories, commenced in January 2023.

Information Architecture and Forensic Data Analysis

About the Project

The focus of this specific PhD project is Information Architecture and Forensic Data Analysis. How can we improve information architectures to support more effective data discovery and the responsible sharing of data? What methodologies can best support the effective exchange of information inherent in enterprise information architecture?

Sharing trusted, mission-critical data between organisations is a significant business challenge. This PhD research project is a collaboration with industry partner Astral Consulting and will tackle that challenge by investigating how organisations can leverage human & AI/ML approaches to improve the forensic discovery and analysis of organisational data across multiple and complex information architectures. Expected benefits of this research include improved resilience through improved data curation and discovery with a focus beyond search, moving towards provenance, trust, custody, traceability and the governance of information.

This project commenced in March 2022 with the recruitment of PhD researcher Lufan Zhang who is based at Swinburne University of Technology. Lufan will have the opportunity to work with the leading information management team at Astral, and collaborate and participate in industry-led projects, conducting engaged research and advancing information systems at the edge. The supervisory team is led by Chief Investigator Dr Paul Scifleet, Associate Professor Amir Aryani, and partner Investigator Marie Felsbourg from Astral Consulting. This research project will:

Provide new insights into modern Information Governance, Information Architecture and Information Management Strategy;
Develop methodologies for the design and sharing of Information Architectures to better support data discovery and responsible use;
Propose new software solutions for information search retrieval and exchange of structured and unstructured data based on information architectures; and
Participate in consultation, undertake interviews and develop use cases with Industry partner and key stakeholders.

This project is one of two CIRES projects with Astral. The second project, Curating Systems of Engagement, commenced in April 2023.

Astral, established in 2000, are industry experts in Information Management delivering services to leading Australian and Global 2000 organisations. Astral address the business challenges of their clients by implementing systems and services that manage the full lifecycle of Information Assets to leverage them across all business processes and add value to the bottom line. Astral provide Information Management and Governance consulting services and support clients by leveraging IP and experience developed in 20 plus years of business. The projects co-designed with CIRES will assist Astral in the objective of being able to advise and support clients in leading technologies and approaches to information management. The research will improve understanding of solution design and implementation options available to clients, and will enable delivery of improved outcomes to clients, providing them with real business advantages. The partnership with CIRES presents opportunities for driving a greater awareness of challenges and potential solutions, stimulating a sense that information controls are not only possible but essential and will add to an organisation’s competitive edge.

Curating Systems of Engagement

About the Project

This project tackles the critical issue of how information from an organisation’s public systems of engagement can be captured, developed and leveraged as information assets of value and will contribute to methodologies for improving enterprise information and knowledge management. Working with partner Astral Consulting, the research will investigate how organisations can leverage human and Artificial Intelligence/Machine Learning (AI/ML) approaches to curate and incorporate the uncontrolled data accumulating in an organisations systems of engagement into its formal systems of record. A system of record is the legal, auditable system that tracks records while a system of engagement is where end-users carry out their tasks, such as collaborate on and complete business processes.

The project commenced in April 2023 with the appointment of PhD Researcher Pa Pa Khin, who is based at Swinburne University of Technology. This project will investigate how AI/ML can meet the demand of utilising the information within systems of engagement, including the full range of communication and collaboration technologies in use but currently outside of existing organisational control. Pa Pa is supervised by Chief Investigators Dr Paul Scifleet, Associate Professor Amir Aryani, and Partner Investigator Marie Felsbourg from Astral Consulting.

This project is one of two CIRES projects with Astral. The other project, Information Architecture and Forensic Data Analysis, commenced in April 2023.

Using Data to Overcome Wellbeing Challenges Across the Life Spectrum

About the Project

This project applies predictive modelling to datasets for useful insights into urban, rural, and remote communities to drive innovation and change in clinical settings.

The co-designed research will build new data platforms, using obesity data as an exemplar, across the life course and linking it with clinical data from hospital care (from Queensland’s new EMR implementation) as well as population health and community data. It will involve attention to the careful curation of data from different Government departments and services.

The project will look at case studies across three diverse settings – urban, rural and remote, to determine what datasets are useful in describing population health; what predictive modelling techniques are useful in describing the current and future state of communities and individuals; and how to best engage and impart information to and with clinicians – from primary through to tertiary care institutions – to utilise the knowledge to drive precision prevention practice change that will meet and overcome the growing burden of chronic disease in  our society.

This project commenced in April 2022 with the recruitment of PhD researcher Hechuan Wen who is based at The University of Queensland. Hechuan is collaborating closely with leading experts in Health & Wellbeing Queensland to apply predictive modelling to datasets for useful insights into communities to drive innovation and change in clinical settings. The supervisory team is led by Chief Investigators Dr Rocky Chen and Associate Professor Hongzhi Yin from UQ, and Partner Investigator Dr Li Kheng Chai from Health and Wellbeing Queensland.

Health and Wellbeing Queensland is the state’s dedicated public health agency, working to drive generational change that improves the health and wellbeing futures of Queenslanders, especially our kids. Their initial focus is obesity prevention and the reduction of chronic disease, through nutrition, physical activity, and wellbeing, and reducing health inequity. To do this they work across government, health environments, the private sector, and communities to partner, create, develop, and amplify policy and programs that achieve real and measurable improvements, so all Queenslanders have the best chance to live a healthier life, no matter who they are, or where they live.

This partnership with CIRES brings a fresh new lens on identifying and using datasets, with a focus on equity and the social determinants of health, to describe population health in urban, rural, and remote settings.  Using predictive modelling techniques, the current and future state of communities and individuals within the community can be described.  Most importantly, having a people-centred approach will be essential to ensure the story to be told resonates with those communities. It is only through a combination of prevention, population, and partnership that we can disrupt the cycle that leads to obesity and overweight where we live, work, learn and play.

Advancing Deep Neural Network Reliability During Dataset Shift

While Deep neural networks (DNNs) succeed in exploiting non-linear patterns in very large and high-dimensional datasets, they catastrophically fail without warning under dataset shift, i.e., changes in data distribution. This PhD project will study various ways to resolve this pitfall by characterising, detecting, and generalising against dataset shift. The research will theoretically unify sparse and inconsistent literature, and empirically validate that theory in the application of genomics, with results to inform ways to maximise reliability of learning systems under datasets shifts.

The project focuses on developing techniques and methodologies grounded in unsolved challenges in computational biology and multi-modal healthcare data. The project commenced in October 2021 with the recruitment of the Centre’s first PhD Researcher, Sam MacDonald, who is based at The University of Queensland. Sam is investigating the proposed methodologies on real datasets from different healthcare organisations, and is supervised by Chief Investigator Dr Fred Roosta-Khorasani from the School of Mathematics and Physics (UQ) and Dr Quan Nguyen from the Institute for Molecular Bioscience (IMB) at UQ.

The team collaborated with Max Kelsen, a Brisbane based artificial intelligence and software engineering agency, during 2021 to early 2023. Max Kelsen was acquired by Bain & Company in 2023.

Interpretable AI-Theory and Practice

A major bottleneck for enterprises adopting AI is the difficulty in applying and interpreting the correct method for a given problem.

This project will survey available interpretable methods in AI and communicate best practices in both lay and comprehensive terms and explore new theoretical landscapes to extend and innovate interpretable methods in AI, focusing on both uncertainty (aleatoric and epistemic), and causality. Emphasis will be on probabilistic inference, using graphical models, including both neural networks and more general approaches like neural wirings and directed acyclic graphs.

The project will investigate the proposed methodologies on real datasets from different healthcare organisations.

This project commenced in April 2023 with the recruitment of PhD researcher Eslam Zaher, who is based at The University of Queensland. Eslam is supervised by Chief Investigator Dr Fred Roosta-Khorasani, and Affiliate Investigators Dr Quan Nguyen, and Dr Maciej Trzaskowski.

The team collaborated with Max Kelsen, a Brisbane based artificial intelligence and software engineering agency, during early 2023. Max Kelsen was acquired by Bain & Company in 2023.

On-demand Dataset Builder

About the Project

This project will develop a prototype system to showcase the scalability, reliability, and usability of an AI-assisted dataset builder for effective and efficient discovery and curation of multi-source and multi-modal educational data. Working in close collaboration with domain experts and end users from the Queensland Department of Education, the project team will review and investigate best practices for constructing on-demand data sets and the concept of “data as a service”. The project will also develop new methods for AI-assisted on-demand dataset builder and will evaluate with a human-centred lens. The research will result in validated methods and a prototype implementation of the system, road-tested with end users.

This project aims to develop an on-demand dataset builder to facilitate effective and efficient discovery and curation of multi-source and multi-modal educational data. It will commence in late 2023 to early 2024 with the commencement of the PhD researcher for the project. They will be supervised by CIRES Chief Investigator Associate Professor Hassan Khosravi, Affiliate Investigator Associate Professor Wojtek Tomaszewski, Centre Director Professor Shazia Sadiq, and Partner Investigator from the Queensland Department of Education, Dr Angela Ferguson.

Queensland Department of Education are empowering confident and creative lifelong learners through a student-centred approach to learning and wellbeing. A partnership with CIRES would support the department in developing a robust data infrastructure pipeline, which can be applied to demonstrator projects to enhance internal data capability and help address policy questions.

Improving Sepsis Management through Better Data and Rapid Learning

About the Project

This project will investigate sepsis prediction algorithms for both children and adults. Recognising sepsis as a global threat, Queensland Health has recently created the Sepsis Breakthrough Collaborative. One focus of this initiative is to rely on the power of data and machine learning algorithms to develop a decision tool that supports early disease detection and treatment. While training and developing robust machine learning models are critical in detecting the disease with high accuracy, it is equally important that these models are effectively integrated into clinical workflows and effectively used by health practitioners such as doctors and nurses.

This project focuses on the effective use of sepsis detection algorithms and how the algorithmic challenges can be overcome. Overall, it will address the following research question: How can sepsis prediction algorithms be effectively integrated, used by health practitioners and adapted and diffused to different clinical settings?

The research will advance knowledge on the use of algorithms for sepsis detection in practice, and how algorithmic processes and tools should be designed, developed, and integrated to maximise value for patients, doctors and other stakeholders. The aim is to develop a theoretical model of effective use for sepsis prediction algorithms, as well as expert guidance on how algorithmic work processes should be designed and managed in practice.

This project commenced in February 2022 with the recruitment of PhD researcher Krishna Dermawan who is based at The University of Queensland. Krishna will have the opportunity to collaborate with leading experts in Queensland Health to understand how clinical teams can best leverage new AI risk prediction algorithms, and work with the Sepsis Breakthrough Collaborative, a new initiative at Queensland Health, aiming to utilize machine learning algorithms for early detection of sepsis in children and adults. The project will help and support the Queensland Health team to minimize the risks and maximize the value of algorithmic decision making. In addition to helping to understand and improve the rollout and use of new risk prediction tools for sepsis, the knowledge from this project will have implications for how clinicians use a range of new digital health tools, as the sepsis case is an instance of a general trend occurring across the clinical specialties. It is supervised by CIRES Research Director and Chief Investigator Professor Marta Indulska, Dr Ida Asadi Someh, Professor Andrew Burton-Jones, and Dr Adam Irwin (Queensland Health).

This project is one of three CIRES projects with Queensland Health related to paediatric sepsis management. Further details on the other projects can be found via the project pages below:

Queensland Health wants to constantly improve the health of Queenslanders and the care they receive and aims to capture all relevant longitudinal data on Queenslanders to understand about their health – past, current and anticipated, treatment decisions made, and outcomes experienced.  However, it is complicated to capture the right information once, curate it and access the information in an ethical and consented way that protects the individual privacy of Queenslanders.  A partnership with CIRES bridges a major existing gap and represents the glue that can enable the data scientists, the clinicians and consumer community to help get this right.

Recognising that sepsis is a leading cause of preventable harm in children, Queensland Health clinicians launched the Queensland Paediatric Sepsis Program. There are three CIRES projects with Queensland Health which focus on how clinicians can best implement tools derived from transparent technical solutions to improve the recognition of sepsis in children. In one of these we will apply the power of data and machine learning algorithms to develop a decision tool that supports early detection and management of sepsis in children.

Expanding Data Sets to Allow Improved Critical Care for Children – Inpatient Risk Prediction

About the Project

In collaboration with Queensland Health, this project aims to provide a platform-independent decision support framework using an interpretable machine learning approach for making effective risk predictions for paediatric patients at risk of sepsis. These predictions will be based on multi-source, readily available, critical care data. They are intended to be a practical electronic clinician assistant and provide new insights into clinical decision-making.

The challenges of the project include effective linkage of relevant data together from different medical data sources, computational analysis of the fused representations in a real-time manner, medical interpretability formulation, uncertainty regarding the reference standard diagnosis of sepsis and integration of interpretability and accurate prediction in a joint training scheme. The significance of this project centres on the novel linkage-analysis and usage of novel transparent algorithmic development to open the black-box ML algorithms in the medical domain.

The research aims to showcase medical analysis in a data-rich environment, with the proposed model and research tasks adapted and applied to other medical predictive tasks, for instance, sepsis prediction/monitor, linking genomics with risk prediction, and so on.

This project commenced in April 2023 with the recruitment of PhD researcher “Huy” Van Nhat Huy Nguyen who is based at The University of Queensland (UQ). Huy will collaborate closely with leading experts in Queensland Health on investigating algorithm use and organizational implications in inpatient settings. He is supervised by Dr Sen Wang and Dr Ruihong Qiu from UQ, and Associate Professor Kristen Gibbons and Dr Sainath Raman from Queensland Health. The project will develop a platform-independent decision support framework using an interpretable machine learning approach to make effective risk predictions for pediatric patients at risk of sepsis.

This project is one of three CIRES projects with Queensland Health related to paediatric sepsis management. Further details on the other projects can be found via the project pages below:

Expanding Data Sets to Allow Improved Critical Care for Children – Outpatient Risk Prediction

About the Project

The traditional hospital-focused model of care neglects monitoring and treating diseases at home. A number of intelligent monitoring systems exist for clinical abnormalities prediction for patients who are confined to hospital beds, but few attempts have been made to develop such a predictive system for home care that could prevent and minimize health-related risk at an early stage. As a result, recovering patients who leave the hospital after treatments are known to have adverse outcomes due to lack of efficient alert systems. In particular, children at risk of serious infection and sepsis are often discharged home with ‘safety-netting’ advice for parents and carers to subjectively observe signs of deterioration.

This project, in collaboration with Queensland Health, aims to develop a probabilistic cloud-based health monitoring and risk prediction system that can predict clinical abnormalities based on streaming data of vitals of children with possible serious infection at home.

The project commenced in April 2023 with the recruitment of PhD researcher Hrishi Patel, who is based at The University of Queensland. Hrishi is supervised by Dr Sen Wang, Dr Adam Irwin (Queensland Health), and Professor Shazia Sadiq. This project is a close collaboration with leading experts in Queensland Health on investigating machine learning techniques for designing risk predictive models in outpatient settings, to mitigate the risk of sepsis in children.

This project is one of three CIRES projects with Queensland Health related to paediatric sepsis management. Further details on the other projects can be found via the project pages below:

Bias Mitigation in Human in the Loop Decision Systems

About the Project

When humans label data to train AI models, their own biases and stereotypes may be reflected in the data, which consequently appear in the resulting trained models leading to unfair, biased, and non-transparent decisions. In collaboration with the Queensland Police Service (QPS), this project focuses on integrating fairness into learning algorithms used in the context of policing services and tasks and aims to observe if this leads to improved outcomes and experiences. The approach will include the development of human-in-the-loop AI, where humans help to increase transparency of automatic decision-making process, e.g., by generating natural language explanations on why a specific amount of police resources is required in a certain suburb.

This project commenced in April 2023 with the recruitment of PhD researcher Hongliang Ni, who is based at The University of Queensland and will collaborate closely with leading experts in the Queensland Police Service (QPS) to generate more transparent, fair, and trustworthy decision-support systems driven by data and controlled by humans. Through this project, Hongliang will develop novel bias tracking, management, and reduction method over the entire Artificial Intelligence pipeline: from data collection and curation to model training and deployment with end users. It is one of two projects with Queensland Police Service related to the responsible use of sensitive data assets. The other project Data as a Service Architecture commenced in October 2023.

Hongliang is supervised by CIRES Chief Investigator Associate Professor Gianluca Demartini, Centre Director Professor Shazia Sadiq, and Nick Moss, Manager, Data Services at the Queensland Police Service.

The Queensland Police Service (QPS) aims to be a data driven organisation with an advanced data analytics capability to inform decision making via legally and ethically actionable analytics.

The QPS Organisational Insights Platform (OIP) was established as the QPS advanced data and analytics platform for tactical, operational, and strategic decision making.  A foundational building block of the OIP is the Data Lake to become the QPS centralised repository to store approved structured and unstructured data. The information resilience challenge is how to extract insights from, and the responsible use of, sensitive data assets.

QPS are looking at this partnership opportunity with CIRES to build, protect and sustain agile data pipelines, in addition to upskilling officers, and identifying and employing potential data scientists, to position QPS as an insight driven organisation. One key measure of success will be better informed tactical, operational, and strategic decision makers who can apply resources, develop policy, procedures, and practices so that the lives of Queenslanders benefit positively.

Data as a Service Architecture

About the Project

This project aims to develop a novel system for making efficient and effective queries and recommendations based on multi-source data from partner, the Queensland Police Service [QPS], by constructing an enterprise knowledge graph model for analysing complex objects from multiple data sources. The project will develop ranking algorithms and deep models for yielding efficient and effective data queries recommendations, which also consider the evolutionary, uncertain data, and human experts in the loop. The proposed techniques will be validated by conducting interactive experiments on the QPS data lake. The applicability of graph-based methods for generating complex network, ranking methods, and deep models for making queries and recommendations will be explored to discover relationships, rules and patterns previously unknown, and potentially useful for on-duty police officers.

This project commenced in October 2023 with the recruitment of PhD researcher Fidan Karimova at The University of Queensland. Fidan and the project team will collaborate closely with leading experts in the Queensland Police Service (QPS) to develop a prototype system to showcase the scalability, reliability, and usability of an AI-based data discovery system. Fidan is supervised by CIRES Chief Investigators Dr Rocky Chen and Associate Professor Hongzhi Yin, Affiliate Investigator Dr Wen Hua, Centre Director Professor Shazia Sadiq, and Partner Investigator from QPS Mr Nick Moss.

This is one of two CIRES projects with QPS related to the responsible use of sensitive data assets. The other project Bias Mitigation in Human in the Loop Decision Systems commenced in April 2023.

The Queensland Police Service (QPS) aims to be a data driven organisation with an advanced data analytics capability to inform decision making via legally and ethically actionable analytics.

Automated Horizon Scanning for Intellectual Property

About the Project

There is a continuous need to efficiently and accurately identify pre-existing technologies from patent documents and academic research papers, a process that is referred to as ‘horizon scanning’ or ‘technology landscaping’. This project aims to develop new robust information search and retrieval methods for technology landscaping based on large text corpus found in patent databases and research paper repositories. The new methods will support the strategic positioning of firms and research groups by enabling insight into the landscape of pre-existing technologies relevant to their proposed innovations, as well as help identify areas where gaps in the current technology landscape may exist. The project is expected to involve the design and development of new methods related to natural language processing, topic modelling and machine learning.

This project commenced in May 2023 with the recruitment of PhD Researcher Luhan Cheng who is based at Swinburne University of Technology. Luhan is supervised by CIRES Affiliate Investigator Dr Steve Petrie and Chief Investigator Associate Professor Amir Aryani.