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Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular.

It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications.

The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works.

This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems.

DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Could “Economic Man” and “Moral Man” be integrated? Could “Business” and “Morality” contain each other? An attempt is made to verify, from the theoretical formation, the value rationality of economic behaviour, to seek the value co‐structure of ethics and economics.

The aim of this paper is reviewing the discipline development course of the history of socialist political economy with Chinese characteristics and recognising the changes of its development and its historic mission in the new stage will be beneficial to the construction of socialist political economy with Chinese characteristics from the perspective of doctrinal history.

In this paper from the aspect of discipline formation and development, the history of China’s socialist political economy has experienced two stages: emergence and formation (the first stage) and steady development (the second stage). It has explored new research fields and improved the quality of research levels. However, the role of studying the history of socialist political economy with Chinese characteristics has not been fully played regarding satisfying the needs of constructing socialist political economy with Chinese characteristics.

In this study when the construction of socialist political economy with Chinese characteristics entered a new era, the study of the history of socialist political economy also entered a new stage, showing new features in terms of research objectives, principles, scale and methods.

Therefore, the research on the history of socialist political economy with Chinese characteristics should be highly emphasised, and the focus on serving the construction of socialist political economy with Chinese characteristics should be its historic mission and core task. Also, researchers should pay attention to changing ideas, laying a good foundation, highlighting key points, building platforms and broadening horizons.

The purpose of this paper is to propose a comprehensive framework for integrating big data analytics (BDA) into cyber-physical system (CPS) solutions. This framework provides a wide range of functions, including data collection, smart data preprocessing, smart data mining and smart data visualization.

The architecture of CPS was designed with cyber layer, physical layer and communication layer from the perspective of big data processing. The BDA model was integrated into a CPS that enables managers to make sound decisions.

The effectiveness of the proposed BDA model has been demonstrated by two practical cases − the prediction of energy output of the power grid and the estimate of the remaining useful life of the aero-engine. The method can be used to control the power supply system and help engineers to maintain or replace the aero-engine to maintain the safety of the aircraft.

The communication layer, which connects the cyber layer and physical layer, was designed in CPS. From the communication layer, the redundant raw data can be converted into smart data. All the necessary functions of data collection, data preprocessing, data storage, data mining and data visualization can be effectively integrated into the BDA model for CPS applications. These findings show that the proposed BDA model in CPS can be used in different environments and applications.