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SAKE Semantical analysation of complex events
A Platform for the integration of big data streams with the support of machine learning
Type of content:
Assets
Type of asset:
Use case
Big data potential
Yes
Policy domains:
Innovation, Science & Technology
Phase in the policy cycle:
Agenda Setting
Policy Design and Analysis
Policy Implementation
Policy Monitoring and Evaluation
Open license availability
No
Ease of use
High
Tags:
Machine learning
Big data
Addresses:
| SWOT Analysis for SAKE Semantical analysation of complex events |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• Optimization of internal processes and reduction of production costs • Analysing vast streams of data • Reduction in the workload required of the system resources: data gathered by the sensors will be modularized, in order to process only the aspects of data relevant to the purpose of the analysis. • Automatic language generation provides high degree of user friendliness: Analytical results and the causes of errors to be processed in natural language available via a combination of modern learning methods and automatic language generation processes • Unsupervised learning in streaming is able to detect novel patterns in streaming data in real time without any re-analysis of previously examined data • To cope with the potentially large amount of data, the architecture utilizes state-of-the-art distributed cloud-based big data technologies |
Weaknesses• Enormous amounts of real time data generated • The technological advances related to real-time data analytics are moving and changing as rapidly as data itself. • Basic design of the architecture is considered complete, there is still rom for further developments on the module level |
External |
Opportunities• Facilitate the timely detection and data driven prediction of failures from event data • Increase of data: Increasing use of automation in machine and plant construction has led to a large growth in the amount of data generated from the number of industrial production processes being recorded and monitored by sensors. • Centrally evaluating the data in real time, could lead to optimization of internal processes and reduction of production costs • Strongly heterogeneous data streams can be consolidated and subsequently analysed using modern machine learning processes. • Development of a scalable distributed data storage layer relating to event descriptions in accordance with the Resource Description Framework (RDF) • Efficient supervised and unsupervised machine learning modules for modularised data to discover the causes of errors and to predict sensor configurations which can lead to errors • Development of intuitive user interfaces |
Threats• The technological advances related to real-time data analytics are moving and changing as rapidly as data itself. • Enormous amounts of real time data generated • Data quality |
