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Apromore mines the vast data contained in modern business IT systems.
German software vendor GBTEC is a major investor in Apromore, a University of Melbourne start-up company. Apromore raised $A6.84 million in its Series A funding round. Its open-source process-mining software analyses business operations to improve performance.
Software that allows businesses to analyse and improve their operations, performance and compliance has been developed by Apromore Pty Ltd. It is the first and only commercial process-mining software based on open-source code.
Apromore Pty Ltd was co-founded in September 2019 by a University of Melbourne researcher. It raised $A6.84 million in its first round of venture capital fundraising. Investors include GBTEC, Leonardo and the University of Melbourne.
The company has 15 employees located in Melbourne, Brisbane, and Tartu, Estonia.
Businesses are using more digital technologies to handle increasingly complex processes. As a result, they generate huge volumes of data. For example, enterprise systems record what tasks are performed, by whom, and in what order. They track how long tasks take and what resources are used. Enterprise systems include customer relationship management software, payroll systems, and project management software.
Businesses need process-mining software to extract useful process knowledge from this data. This software analyses the data to reconstruct the business processes. It can then find bottlenecks, repetition and waste that affect business performance. It can also help businesses to assess how well they comply with service-level agreements, their own standard operating procedures or regulatory frameworks.
The concept of process mining was introduced in academia in the early 2000s. Since then, no open-source software to analyse business processes has been developed for commercial use.
Any sector that relies on enterprise systems can use process mining. This includes education, government, healthcare, banking, insurance, IT, engineering and manufacturing, mining, transport, utilities and the service industry.
The global market for process-mining products is projected to be worth $US1.4 billion by 2023, with a year-on-year growth rate of around 50 per cent.
The process-mining software is based on research led by Professor Marcello La Rosa from the University of Melbourne.
Professor La Rosa began the project in 2009 at the Queensland University of Technology. He joined the University of Melbourne in February 2018, bringing with him a team of nine researchers. The research is a collaboration with Professor Marlon Dumas and his team from the University of Tartu, Estonia, as well as groups from other research institutions.
The researchers built the software using expertise in computer science, information systems, business process management, statistics, graph theory, data mining and machine learning.
The Apromore software – so named because it started as an ‘Advanced Process Model Repository’ – connects to a business’s enterprise systems. It automatically collects and analyses data, looking for weak spots. Based on these analyses, the business can adapt its processes and then use the software to track the results.
As part of consultancy services provided through the University, the researchers field-tested the software with customers in different sectors. The feedback helped the researchers to refine the software.
At the same time, Professor La Rosa’s team continued its research. They also extended the open-source software with proprietary features for commercial use.
The research has been supported by over $A10 million from Australia and European funding schemes and from University of Melbourne seed funding.
The open-source core of Apromore is shared under a free-software license called a GNU Lesser General Public License (LGPL 3.0). This gives researchers and professionals the freedom to use, change and share the code.
To further develop and commercialise the software, Professor La Rosa, Professor Dumas and two co-founders launched Apromore in September 2019. Professor La Rosa is CEO of the company.
The intellectual property developed by Professor La Rosa and his group at the University was transferred to Apromore in return for equity. As part of the agreement, the University also invested in the company.
In its Series A round, Apromore raised $A6.8 million from investors including the University, German software vendor GBTEC, and Australian consulting and technology firm Leonardo.
GBTEC also entered a strategic partnership with Apromore to incorporate the Apromore process-mining technology as a core component in GBTEC’s business process modelling and automation software, BIC Platform.
Apromore offers two versions of its software. The open-source Community Edition is free to download and use. The Enterprise Edition, which includes support and advanced features, is available on subscription. Over 10 years, contributors from universities, research institutes and private organizations have added updates and fixes to the Community Edition. Businesses can run the Enterprise Edition themselves in-house or use a cloud service provider.
The Series A investment is being used to further develop the software for business use. In particular, the company will make the software more robust, scalable and secure. They will also add features to enable the software to automatically find ways to improve processes, simulate scenarios to help businesses plan, and identify and analyse the causes of problems.
Apromore founders, from left: Prof. Marcello La Rosa, Prof. Marlon Dumas, Dr Simon Raboczi and Dr Ilya Verenich.
ARC Discovery Project (DP180102839) ‘Diagnosis and prediction of business process deviances’
ARC Discovery Project (DP150103356) ‘Improved business decision-making via liquid process model collections’
ARC Discovery Project (DP110100091) ‘Risk-aware business process management’
ARC Linkage Project (LP110100252) ‘Facilitating business process standardisation and reuse’
NICTA
Smart Services Cooperative Research Centre
European Research Council (ERC) Advanced Grant ‘PIX: Process Improvement Explorer’ to Professor Marlon Dumas
Estonian Research Council (IUT20-55) ‘Data-Driven Management of Business Processes’ to Professor Marlon Dumas
Melbourne School of Engineering Strategic Investment Fund, University of Melbourne
Reissner D et al (2020) Scalable alignment of process models and event logs: an approach based on automata and S-components. Information Systems, doi: 10.1016/j.is.2020.101561
Augusto A et al (2019) Automated discovery of process models from event logs: review and benchmark. IEEE Transactions on Knowledge and Data Engineering 31(4): 686–705. doi: 10.1109/TKDE.2018.2841877
La Rosa M et al (2011) APROMORE: An advanced process model repository. Expert Systems with Applications 38(6): 7029–7040. doi: 10.1016/j.eswa.2010.12.012
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