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Projects

OpenSI projects

OpenSI is still in early stages of establishment, however, there are several initiatives and emerging projects that are currently being considered. Each project is described as part of one of our research themes and a Technology Readiness Level (TRL) is applied to projects to indicate the current state and intended state on completion.

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Leveraging Open-Source Technology for AI Automation with Apache Cassandra

THEME

AI automation

TIMEFRAME

3.5 years

PROJECT TYPE

PhD in Information Technology

TRL SCORE

TRL3 to TRL5

DESCRIPTION

This project aims to develop an AI automation platform by harnessing open-source technologies, with Apache Cassandra serving as the foundational database. The platform’s goal is to enable organisations to automate a wide range of tasks, processes, and workflows through the integration of artificial intelligence and machine learning algorithms. Specifically, it aligns with the objectives of the newly established OpenSI, a collaborative effort between NetApp Australia and the University of Canberra. This institute is dedicated to fostering collaboration between universities and industries, addressing real-world challenges using open-source technologies.

Embedded within the Ph.D. in Information Technology program at the University of Canberra, this opportunity is uniquely tailored for working professionals. The program’s flexibility accommodates career commitments while offering strong industry connections that provide valuable networking opportunities and insights into real-world applications. Participants engage deeply with major research categories, techniques, and methodologies within computer science, emphasising the use of open-source technology. Through hands-on experience, candidates master experimental methods, implementing small-scale research projects in specialised computer science areas, machine learning and AI.

Furthermore, the program places a strong emphasis on effective communication and dissemination of research findings within the open-source community. Participants refine their skills in presenting and reporting research outcomes in formats acceptable to both academic and industry audiences. With a focus on practical applicability and scholarly rigour, the PhD in Information Technology equips candidates with the knowledge and competencies essential for success in today’s dynamic IT landscape. Successful applicants will spend 45% of their PhD duration embedded within the Instaclustr R&D team.

SKILLS/EXPERTISE

The ideal PhD candidate for this project would possess a strong background in computer science, data engineering, or a related field. They should have experience and expertise in open-source technologies, particularly in AI, machine learning, and data management. The candidate should demonstrate proficiency in programming languages such as Python and familiarity with frameworks like TensorFlow.

Fast Fourier Transform (FFT) based compression

THEME

Data solutions at scale

TIMEFRAME

Less than 12 months

PROJECT TYPE

Embedded project

TRL SCORE

TRL5 to TRL7

DESCRIPTION

This project aims to leverage signal processing to compress time series data by encoding data as an audio wave and compressing it using lossless audio codecs.

An initial proof of concept has been able to demonstrate between 17 and 50 times a reduction in raw data size for time series-based data.

Successful applicant(s) will work embedded within the Instaclustr R&D team to help advance this initial proof of concept to a state of least technical demonstration.

SKILLS/EXPERTISE

Experience in signal processing and data compression.

Bloom filter encrypted indexes

THEME

Cyber security and privacy

TRL SCORE

TRL 6 to TRL 8

PROJECT TYPE

Embedded project

DESCRIPTION

This project aims to use an existing body of knowledge to further develop a Multidimensional Bloom Filter index for Apache Cassandra® that allows users to search sets without revealing the data they are searching for.

Some initial work on this project has been commenced and results can be found here.

SKILLS/EXPERTISE

Experience in encryption and set theory.

Fast horizontal scaling of distributed systems

THEME

Data solutions at scale

TIMEFRAME

6-12 months

PROJECT TYPE

Project

TRL SCORE

TRL3 to TRL5

DESCRIPTION

Horizontal scaling (adding or removing nodes) of distributed systems is typically a time consuming and computationally expensive operation requiring significant volumes of data to be copied across the network.

Initial research has identified that with many distributed data systems it may be possible to trade off the time and compute requirements of the horizontal scaling against other attributes of the system and that this would be beneficial in many use cases.

This project would investigate potential trade-offs and implementation approaches to at least proof of concept stage with a distributed data system such as Apache Cassandra® or Apache Kafka®.

SKILLS/EXPERTISE

Distributed data systems. Apache Cassandra® or Apache Kafka® knowledge particularly beneficial.

Our research themes

Data solutions at scale

Projects that aim to advance technical capability in effectively scaling large datasets and/or data infrastructure systems and software.

Cyber security and privacy

Projects that aim to increase cyber and/or privacy resilience or protect against various cyber threats.

Artificial intelligence and machine learning

Projects that aim to further advance capability in AI or ML.

Open source business models

Research into successful models and approaches to creating, licensing, managing and collaborating on open source projects and capabilities.

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