The rapid expansion of Internet of Things (IoT) devices has led to an explosion of event data, posing significant challenges for traditional process model discovery techniques in terms of scalability and discovery accuracy. These techniques rely on centralized storage and processing, which are hindered by data transfer limitations, storage capacity, and computational overhead in distributed IoT environments. Edge-based model discovery techniques offer a promising solution for analyzing large-scale IoT data. However, existing techniques suffer from low efficiency and an inability to handle complex process structures. To address these challenges, we propose EdgeIM, an efficient edge-based process model discovery technique that enhances efficiency and model accuracy. EdgeIM operates in three key stages: preprocessing and feature-preserving sampling to eliminate redundant data, local processing at edge nodes to extract key structural features, and global feature aggregation at a central node for model discovery. EdgeIM has been implemented on the open-source process mining platform PM4Py, and experimental results on nine public event logs demonstrate that, compared to existing edge-based model discovery techniques, EdgeIM significantly improves discovery efficiency while maintaining high model quality.

Edge computing may improve the processing quality of big IoT stream data and reduce network operational cost by moving computation onto the edge. However, there are two challenges in integrating cloud and edge computing for big stream data. Firstly, edge equipment usually has very limited computing power as well as storage ability, and apparently cannot support all the processing of big and real-time stream data. A flexible division of such services between edge and cloud is needed. Secondly, edge-end collaboration continuously changes due to some intrinsic interaction of data stream. In this paper, we propose a service-based approach to seamlessly integrating cloud and edge equipment. Based on our service model, we split a cloud service into two parts running on cloud and edge respectively. Also, we propose a dynamic service scheduling mechanism based on the improved bipartite graphs. We can deploy a cloud service to the edge at the right time when a key node emerges. The effectiveness of the proposed approach is demonstrated by examining real cases of China's State Power Grid. Experimental results verify the effectiveness and efficiency of our approach.

Predictive industrial maintenance promotes proactive scheduling of maintenance to minimize unexpected device faults. A fault is not always isolated and may be formed by a propagation of trivial anomalies, which are regarded as service events herein. In this paper, we firstly propose an algorithm for generating service event correlation. Such correlations can show us lots of clues to the anomaly/fault propagation. The correlations are encapsulated into service hyperlinks as our previous works did, and thus we depict the anomaly/fault propagation as service event routing among services via the refined service hyperlinks. Our scenario illustrates that a trivial anomaly may propagate into different faults under different service event correlations. It indicates that the destination of a service event is often uncertain. Therefore, this paper further proposes a heuristic approach to handle the uncertainty problem. Extensive experiments have been made to verify the effectiveness of the approach.

A surge in sensor data volume has exposed the shortcomings of cloud computing, particularly the limitation of network transmission capability and centralized computing resources. The dynamic intervention among sensor streams also brings challenges for IoT applications to derive meaningful information from multiple sensor streams. To handle these issues, this paper proposes a service‐based method with fog computing paradigm based on our previous service abstraction, which can capture meaningful events from multiple sensor streams. In our service abstraction, we utilize correlation analysis method to capture events as variations of correlation among sensor streams. Facing inconsistent frequency and shift of correlation, we propose a Dynamic Time Warping‐ (DTW‐) based algorithm to obtain sensor streams’ lag‐correlation. For adaptively aggregating related events from different services, we also propose an event routing algorithm to assist the composition of cascaded events through service collaboration. This paper reports the tryout use of our method in Chinese power grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in power grid, we verified that our method can reduce the network transmission and computing resource with high accuracy.

Abnormality Detection in power plant is a typical IoT application which aims to identify anomalies in these routinely collected monitoring sensor data; intend to help detect possible faults in the equipment. However, on the development of abnormality detection, we find that there are three challenges. The first one is the lack of cooperation between sensors. It means that the physical sensors cannot share and interact with each other. Secondly, the rapid increase in volume of sensor data and dynamic situation of production result in challenges to predefine all possible associations between sensors. Thirdly, it is difficult to build IoT application for developers who have little or no professional knowledge about production process. In this paper, we proposed a proactive data service model to encapsulate stream sensor data into services. We spread events among the proactive data services. By analysis of event correlations, we have realized service hyperlinks which help to offer the proactive real-time interaction with services. Real application and experiments verified that our proactive data service based method is more effective compare with traditional rule-based methods to detect abnormalities in power plant.

Though the existing data service mashup tools are gaining acceptance, it is still challenging for developers with no or little programming skills to develop data service mashups for dealing with situational and ad-hoc business problems. The paper focuses on interactive recommendation in which assistance is provided in a context-sensitive manner when the mashup plan can't be determined in advance. The paper analyzes the problem with a motivating scenario of mashup building for criminal investigation. Inspired by the observation that there exist dataflow patterns for certain integration functionalities, a dataflow-pattern-based recommendation framework is proposed to solve the problems. The framework can not only recommend data services by discovering similar situations, but also recommend mashup patterns and target data services. We propose a method to analyze the relationships between data services and dataflow patterns through both mining history logs and matching the input/output parameters. Further, to recommend target data services, we propose a method to transform the data mashup plans into mixed graphs and apply the graph-based substructure pattern mining (gSpan) algorithm on them. Experiments show that the dataflow-pattern-based recommendation approach for data service mashup is effective and efficient.