ChESS (Change Event based Sensor Sampling) picks up speed.

ChESS (Change Event based Sensor Sampling) picks up speed. The three research institutions DFKI, University of Oldenburg and Jade University of Applied Sciences are working towards one goal in this joint project. In the event of a storm tide 💦🌪 for example, #AI in the future could automatically detect system changes in real-time and trigger actions at the moment they occur. Our photo shows the ChESS team (from left) Elmar Berghöfer, Daniel Lukats (both DFKI), Janina Schneider, Oliver Zielinski (both University of Oldenburg and DFKI), Lars Nolle (Jade University) and Frederic Stahl (DFKI). Iring Paulenz (Jade University) is missing in the picture.

Foto: © DFKI  👉 https://www.dfki.de/en/web/news/chess

ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification

my newest publication on Explainable AI (XAI) and Ensemble Learning. You can read it for free here. Any questions to contact me or leave a comment 🙂

What is Data Mining?

Learn what Data Mining is all about in my new short video. You can also follow me on Twitter (@fred_stahl) for regular updates.

Machine Learning for Big Data (MLBD) track at 35th INTERNATIONAL ECMS CONFERENCE ON MODELLING AND SIMULATION

Invitation to submit a paper to the Machine Learning for Big Data   track of the 35rd INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION in Wildau, Germany (near Berlin)

This Track’s area of interest is on the Volume and Velocity dimensions of Big Data. Volume, referring to the size of the data, Velocity, referring to the data that is generated rapidly and needs to be analysed in real-time. The workshop accepts papers on the application of Machine Learning and Data Mining Algorithms on large, complex or data generated in real-time. Applications are for example Simulation monitoring, Time Series Analysis, Network Intrusion Detection, Health Monitoring, Trend Detection in Twitter, Financial Monitoring, etc. The conference track also encourages the submission of papers that introduce new techniques, algorithms, systems and workflows, for large quantities of data, data streams and/or Time Series Analysis. Track-Chair: Dr. Frederic Stahl, (German Research Center for Artificial Intelligence (DFKI), University of Reading UK) Track-Co-Chairs: Professor Dr. Mohamed Gaber (Birmingham City University, UK) Dr. Marwan Hassani (Eindhoven University of Technology, Netherlands) Topics of interest include but are not limited to:           Data Mining and Machine Learning algorithms, models and techniques           Data Mining and Machine Learning applications           Data Mining and Machine Learning on simulation data           Data Mining of Big Data streams           Data Mining of large quantities of data           Explainable Data Mining models           Scalability of Data Mining techniques           Real-time data stream analytics of simulation data           Data stream analytics systems           Concept Drift Detection techniques           Outlier Detection           Time Series Processing and Analytics           Visualisation of real-time data streams            Analytics of IoT data streams

Machine Learning for Big Data (MLBD) track at 34th INTERNATIONAL ECMS CONFERENCE ON MODELLING AND SIMULATION

Invitation to submit a paper to the Machine Learning for Big Data   track of the 33rd INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION in Wildau, Germany (near Berlin)

This Track’s area of interest is on the Volume and Velocity dimensions of Big Data. Volume, referring to the size of the data, Velocity, referring to the data that is generated rapidly and needs to be analysed in real-time. The workshop accepts papers on the application of Machine Learning and Data Mining Algorithms on large, complex or data generated in real-time. Applications are for example Simulation monitoring, Time Series Analysis, Network Intrusion Detection, Health Monitoring, Trend Detection in Twitter, Financial Monitoring, etc. The conference track also encourages the submission of papers that introduce new techniques, algorithms, systems and workflows, for large quantities of data, data streams and/or Time Series Analysis.

Track-Chair:
Dr. Frederic Stahl, (German Research Center for Artificial Intelligence (DFKI), University of Reading UK)
Track-Co-Chairs:
Professor Dr. Mohamed Gaber (Birmingham City University, UK)
Dr. Marwan Hassani (Eindhoven University of Technology, Netherlands)

Topics of interest include but are not limited to:

• Data Mining and Machine Learning algorithms, models and techniques.
• Data Mining and Machine Learning applications.
• Data Mining and Machine Learning on simulation data.
• Data Mining of Big Data streams
• Data Mining of large quantities of data
• Explainable Data Mining models
• Scalability of Data Mining techniques
• Real-time data stream analytics of simulation data
• Data stream analytics systems
• Concept Drift Detection techniques
• Outlier Detection
• Signal Processing and Analytics
• Visualisation of real-time data streams
• Analytics of IoT data streams

 

Started new position at DFKI GmbH

I am very proud to start my new position in the German Research Center  for Artificial Intelligence (DFKI GmbH) as Teamleader and Senior Reseracher in their Marine Perception Research Department.

 

Thames Valley Artificial Intelligence meetup (19th September 2019)

Will be attending the 3rd edition of the TVAI meetup in Thames Valley Science Park, hope to see you there.

https://www.meetup.com/Thames-Valley-Artificial-Intelligence-Meetup/events/263027968/

Data Mining in Non-Stationary Environments

Join me at the Thames Valley AI meetup on the 18th of July. Here is a summary of my talk:

PhD Studentship in Data Science

Project title:  Unsupervised Predictive Algorithms for Data Streaming Mining coping with unlabelled classes input

Supervisors:  Dr Frederic Stahl, Prof. Atta Badii

Project Overview:   The field of Data Stream Mining is concerned with the analytics of high velocity Big Data Streams. A data stream is a sequence of consecutive data instances that is infinite and generated in real-time. Thus applications, such as data mining can only read the sequence once using limited computing and storage capabilities. Predictive analytics is one of the most important types of data mining techniques, where an unknown variable in a dataset is predicted. For example, imagine a sequence of twitter posts that is generated in real-time. One application could be to predict if a tweet is related to a specific topic, e.g. politics. A data stream predictor would learn a model that can then be applied to new tweets in order to predict whether they are related to politics. Particular challenges here are the generation of data mining models that automatically adapt to changes of the pattern encoded in the stream (concept drift). In the example a concept drift could be “breaking news” related to politics which influences the topics which are being discussed on twitter.  Further application examples are detection of performance bottlenecks in computer networks or traffic congestion forecasting in smart cities.

The aim of this PhD project is to develop new cutting edge predictive analytics methods/algorithms for Big Data Streams that can forecast events ahead of time and adapt to concept drift. The project is in collaboration with StreamCentral Data Insights Limited (www.streamcentraldata.com), an industry partner that will contribute real-world case studies and data stream processing infrastructure.

Eligibility: 

• Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree or equivalent in Computer Science, Mathematics or related subject.
• Due to restrictions on the funding this studentship is open to UK/EU students.

Funding Details:

• Starts September 2019
• 3 – year award
• Tuition fees plus RCUK stipend

How to apply:   

To apply for this studentship please submit an application for a PhD in Computer Science at http://www.reading.ac.uk/graduateschool/prospectivestudents/gs-how-to-apply.aspx.

*Important notes*

• Please quote the reference ‘GS19-025’ in the ‘Scholarships applied for’ box which appears within the Funding Section of your on-line application.
• When you are prompted to upload a research proposal, please omit this step.

Further Enquiries: 

Please note that, where a candidate is successful in being awarded funding, this will be confirmed via a formal studentship award letter; this will be provided separately from any Offer of Admission and will be subject to standard checks for eligibility and other criteria.

For further details please contact Dr Frederic Stahl: F.T.Stahl@reading.ac.uk, tel. +44(0)118 378 8983

Data Stream Analytics (DSM) track of the 33rd INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION

Invitation to submit a paper to the Data Stream Analytics (DSM) track of the 33rd INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION in Napoli, Italy

http://www.scs-europe.net/conf/ecms2019/dsm.html