In this study, we propose to explore multi-layered clustering to provide a context-aware data analytics tool for monitoring the network behavior of subsystems, such as a district heating (DH) network. Multi-layer clustering, in contrast to multi-view clustering, does not assume conditional independence of layers. The main idea of our approach is based on the integration of clustering models produced by considering different perspectives that capture information about the monitored subsystems’ operational behavior or performance as well as their contextual environment. The initial clustering layer can reflect a static context, which is important for the subsystems’ performance. It will be used as a base on which clustering models produced with respect to other analyzed operational characteristics and contexts will be layered. This will facilitate analysis and comparison of the subsystems’ behavior in two comparable time periods and, eventually, identification of deviations that need attention. The proposed approach is evaluated and validated in a use case from the DH domain. The multi-layered clustering is applied and demonstrated to be robust for continuous context-aware analysis of the performance of a network of DH substations.

In this study, we propose a data-driven survival risk analysis approach in support of predictive maintenance management of a large portfolio of industrial assets. The concrete use case considered is a large portfolio of industrial vehicles (trucks). However, the approach is generic (i.e., asset-type agnostic) in nature and can be applied in different industrial contexts. It is able to employ different data sources in the risk analysis workflow, e.g., time series operation data collected via a multitude of sensor measurements combined with tabular data recording the technical specifications of the assets (vehicles). Subsequently, several different risk assessment strategies can be considered: 1) operation-related risk at each time step for any asset computed on the operation data across the whole portfolio; 2) the failure predisposition of each asset determined by its technical specification; 3) hybrid risk analysis, which innovatively combines the different data types to estimate overall risk at any time in the future for any asset. Our validation, conducted on real-world data, demonstrates that the hybrid approach provides a realistic temporal risk assessment during vehicle operation that also reflects adequately the inherent (contextual) risk predisposition of the vehicle due its technical specification. The proposed approach derives diverse survival risk estimations, which are interpretable by design and in this way facilitate both prognostic health monitoring and root cause analysis of the factors impacting vehicles’ risk of failure.

This paper proposes a novel federated learning methodology, called FedRepo, that copes with concept drift issues in a statistically heterogeneous distributed learning environment. The proposed horizontal federated learning methodology, based on random forest (RF), can be used for collaborative training and maintenance of a dynamic repository of federated RF models, each one customized to a group of clients/devices. The clients are grouped together if their performance patterns with respect to the global RF model are similar. The performance of the customized RF global models is continuously monitored during the inference phase and the repository is accordingly adapted to mitigate the detected concept drift. The proposed methodology is studied and evaluated against an electricity consumption forecasting use case. The evaluation results demonstrate clearly that the proposed methodology is able to deal with concept drift issues in an efficient and adequate fashion without compromising the overall performance of the distributed environment.

In this study, we propose a multi-view clustering approach for mining and analysing multi-view network datasets. The proposed approach is applied and evaluated on a real-world scenario for monitoring and analysing district heating (DH) network conditions and identifying substations with sub-optimal behaviour. Initially, geographical locations of the substations are used to build an approximate graph representation of the DH network. Two different analyses can further be applied in this context: step-wise and parallel-wise multi-view clustering. The step-wise analysis is meant to sequentially consider and analyse substations with respect to a few different views. At each step, a new clustering solution is built on top of the one generated by the previously considered view, which organizes the substations in a hierarchical structure that can be used for multi-view comparisons. The parallel-wise analysis on the other hand, provides the opportunity to analyse substations with regards to two different views in parallel. Such analysis is aimed to represent and identify the relationships between substations by organizing them in a bipartite graph and analysing the substations’ distribution with respect to each view. The proposed data analysis and visualization approach arms domain experts with means for analysing DH network performance. In addition, it will facilitate the identification of substations with deviating operational behaviour based on comparative analysis with their closely located neighbours.

We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques.

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient. In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin parameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.