Final Report Summary - INFER (Computational Intelligence Platform for Evolving and Robust Predictive Systems)
INFER was a major EU-funded project involving 38 researchers, for varied duration of time, from organisations in three different countries. This included: i) Evonik Industries from Germany, one of the world's leading companies in the process industry; ii) Research & Engineering Centre (REC) from Poland, a highly innovative software engineering company; and iii) the Smart Technology Research Centre (STRC) from Bournemouth University in the UK, an interdisciplinary and integrative centre conducting research in the field of automated intelligent technologies. STRC was the initiator and coordinator of the project.
INFER stands for Computational Intelligence Platform for Evolving and Robust Predictive Systems and was a project funded by the European Commission within the Marie Curie Industry-Academia Partnerships & Pathways (IAPP) programme, with the funding of 1.55 Million Euro and a runtime from July 2010 until June 2014.
INFER project's research programme and partnership focused on pervasively adaptive software systems for the development of an open, modular software platform for predictive modelling applicable in different industries and a next generation of adaptive soft sensors for on-line prediction, monitoring and control in the process industry. Within this research programme, through a series of secondments, 16 industrial researchers got the opportunity to gather knowledge in academia and 10 academic researchers absorbed knowledge in industry while 2 new experienced researchers were directly recruited to the project.
The main project goals were achieved by pursuing the following objectives within three overlapping research and partnership programme areas:
1. Area: Computational Intelligence – Objective 1: Research and development of advanced mechanisms for adaptation, increased robustness and complexity management of highly flexible, multi-component, multi-level evolving predictive systems.
2. Area: Software Engineering – Objective 2: Development of professionally coded INFER software platform for robust predictive systems building and intelligent data analysis.
3. Area: Process Industry / Control Engineering – Objective 3: Development of self-adapting and monitoring soft sensors for process industry.
When the project was starting in 2010, there were several freely accessible general purpose data mining and intelligent data analysis software packages and libraries on the market which could be used to develop predictive models, but one of their main drawbacks was that advanced knowledge of how to select and configure available algorithms was required. A number of commercial data mining/predictive modelling software packages were also available. These tools attempted to automate some steps of the modelling process (e.g. data pre-processing, handling of missing values or even model complexity selection) thus reducing required expertise of the user. Most of them were however either front-ends for a single data mining/machine learning technique or they were specialised tools designed specifically for use by a single industry. All these tools had one thing in common – generated models were static and the lack of full adaptability implied the need for their periodic manual tuning or redesign.
The main innovation of the INFER project was therefore the creation and investigation of a novel type of environment in which the ‘fittest’ predictive model for whatever purpose would emerge – either autonomously or by user high-level goal-related assistance and feedback. In this environment, the development of predictive systems would be supported by a variety of automation mechanisms, which would take away as much of the model development burden from the user as possible. Once applied, the predictive system should be able to exploit any available feedback for its performance monitoring and adaptation.
There were (and still are) a lot of fundamental research questions related to the automation of data driven predictive models building, ensuring their robust behaviour and development of integrated adaptive/learning algorithms and approaches working on different time scales from real time adaptation to life long learning and optimisation. All of these questions provided the main thrust of advanced research conducted in the project and resulted in contributions to a large number (over 70) of high impact publications in top journals and international conferences. A variety of application areas and contexts have been used to illustrate the performance of developed approaches and/or to understand the mechanisms governing their behaviour. One of the key applications considered and tackled was that of adaptive soft sensors needed in the process industry.
The INFER software platform, developed with the creation of highly flexible, multi-component, multi-level evolving predictive systems in mind, supports parallel training, validation and execution of multiple predictive models, with each of them potentially being in a different state. Moreover, various optimization tasks can also be run in the background, taking advantage of idle computational resources. The predictive models running within the INFER platform are inherently adaptive. This means that they constantly evolve towards more optimal solutions as new data arrives. The importance of this feature stems from the fact, that real data is seldom stationary – it often undergoes various changes, which affect the relationships between inputs and outputs, rendering fixed predictive models unusable. A distinguishing feature of the INFER software is an intelligent automation of the predictive model building process, allowing non-experts to create well-performing and robust predictive systems with a minimal effort. At the same time, the system offers full flexibility for the expert users in terms of the choice, parameterisation and operation of the predictive methods as well as efficient integration of domain knowledge. While there is still a substantial development effort required before a viable commercial software product could be delivered the strong foundations have been created and it is our intention to build on them in the future.
An interdisciplinary area of Data Science, which has been at the heart of the INFER project's very successful research, training and dissemination activities, has been identified as the 'transforming and growth driving force across all sectors of economy' and related to it Big Data has been named as one of the ‘eight great technologies’ by UK Government. With an unprecedented growth in digital content and data, as the digital universe in 2020 is estimated to be 50 times as big as in 2010, we have entered a new era of predictive analytics and data intensive computing. Data scientists are expected to play a key role in this data revolution and their job has even been referred to as "the sexiest job of the 21st century". In recognition of the excellent research and the huge importance of this area, a new Data Science Institute has been launched at Bournemouth University within which the long term goals initiated in the INFER project will be further pursued.
Further details about the team, outputs, events, project organisation and progress can be found on the INFER project website at www.infer.eu or by contacting the project coordinator Prof. Bogdan Gabrys.
INFER stands for Computational Intelligence Platform for Evolving and Robust Predictive Systems and was a project funded by the European Commission within the Marie Curie Industry-Academia Partnerships & Pathways (IAPP) programme, with the funding of 1.55 Million Euro and a runtime from July 2010 until June 2014.
INFER project's research programme and partnership focused on pervasively adaptive software systems for the development of an open, modular software platform for predictive modelling applicable in different industries and a next generation of adaptive soft sensors for on-line prediction, monitoring and control in the process industry. Within this research programme, through a series of secondments, 16 industrial researchers got the opportunity to gather knowledge in academia and 10 academic researchers absorbed knowledge in industry while 2 new experienced researchers were directly recruited to the project.
The main project goals were achieved by pursuing the following objectives within three overlapping research and partnership programme areas:
1. Area: Computational Intelligence – Objective 1: Research and development of advanced mechanisms for adaptation, increased robustness and complexity management of highly flexible, multi-component, multi-level evolving predictive systems.
2. Area: Software Engineering – Objective 2: Development of professionally coded INFER software platform for robust predictive systems building and intelligent data analysis.
3. Area: Process Industry / Control Engineering – Objective 3: Development of self-adapting and monitoring soft sensors for process industry.
When the project was starting in 2010, there were several freely accessible general purpose data mining and intelligent data analysis software packages and libraries on the market which could be used to develop predictive models, but one of their main drawbacks was that advanced knowledge of how to select and configure available algorithms was required. A number of commercial data mining/predictive modelling software packages were also available. These tools attempted to automate some steps of the modelling process (e.g. data pre-processing, handling of missing values or even model complexity selection) thus reducing required expertise of the user. Most of them were however either front-ends for a single data mining/machine learning technique or they were specialised tools designed specifically for use by a single industry. All these tools had one thing in common – generated models were static and the lack of full adaptability implied the need for their periodic manual tuning or redesign.
The main innovation of the INFER project was therefore the creation and investigation of a novel type of environment in which the ‘fittest’ predictive model for whatever purpose would emerge – either autonomously or by user high-level goal-related assistance and feedback. In this environment, the development of predictive systems would be supported by a variety of automation mechanisms, which would take away as much of the model development burden from the user as possible. Once applied, the predictive system should be able to exploit any available feedback for its performance monitoring and adaptation.
There were (and still are) a lot of fundamental research questions related to the automation of data driven predictive models building, ensuring their robust behaviour and development of integrated adaptive/learning algorithms and approaches working on different time scales from real time adaptation to life long learning and optimisation. All of these questions provided the main thrust of advanced research conducted in the project and resulted in contributions to a large number (over 70) of high impact publications in top journals and international conferences. A variety of application areas and contexts have been used to illustrate the performance of developed approaches and/or to understand the mechanisms governing their behaviour. One of the key applications considered and tackled was that of adaptive soft sensors needed in the process industry.
The INFER software platform, developed with the creation of highly flexible, multi-component, multi-level evolving predictive systems in mind, supports parallel training, validation and execution of multiple predictive models, with each of them potentially being in a different state. Moreover, various optimization tasks can also be run in the background, taking advantage of idle computational resources. The predictive models running within the INFER platform are inherently adaptive. This means that they constantly evolve towards more optimal solutions as new data arrives. The importance of this feature stems from the fact, that real data is seldom stationary – it often undergoes various changes, which affect the relationships between inputs and outputs, rendering fixed predictive models unusable. A distinguishing feature of the INFER software is an intelligent automation of the predictive model building process, allowing non-experts to create well-performing and robust predictive systems with a minimal effort. At the same time, the system offers full flexibility for the expert users in terms of the choice, parameterisation and operation of the predictive methods as well as efficient integration of domain knowledge. While there is still a substantial development effort required before a viable commercial software product could be delivered the strong foundations have been created and it is our intention to build on them in the future.
An interdisciplinary area of Data Science, which has been at the heart of the INFER project's very successful research, training and dissemination activities, has been identified as the 'transforming and growth driving force across all sectors of economy' and related to it Big Data has been named as one of the ‘eight great technologies’ by UK Government. With an unprecedented growth in digital content and data, as the digital universe in 2020 is estimated to be 50 times as big as in 2010, we have entered a new era of predictive analytics and data intensive computing. Data scientists are expected to play a key role in this data revolution and their job has even been referred to as "the sexiest job of the 21st century". In recognition of the excellent research and the huge importance of this area, a new Data Science Institute has been launched at Bournemouth University within which the long term goals initiated in the INFER project will be further pursued.
Further details about the team, outputs, events, project organisation and progress can be found on the INFER project website at www.infer.eu or by contacting the project coordinator Prof. Bogdan Gabrys.