Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques

2015 ◽  
Vol 115 (6) ◽  
pp. 1151-1178 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Chengliang Wang ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Pattern Mining ◽  
Sensor Data ◽  
Abnormal Behavior ◽  
Sequential Data ◽  
Research Issues ◽  
Content Type ◽  
Performance Optimizations ◽  
And Performance

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Originality/value – 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.

Applications of Pattern Discovery Using Sequential Data Mining

2012 ◽  
pp. 1-23 ◽  
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Intrusion Detection ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

Approaches for Pattern Discovery Using Sequential Data Mining

Data Mining ◽  
2013 ◽  
pp. 1835-1851
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequence Data ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Stream Data ◽  
Dual Representation ◽  
Advantages And Disadvantages ◽  
Growth Methods

In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in literature. Apriori based methods and the pattern growth methods are the earliest and the most influential methods for sequential pattern mining. There is also a vertical format based method which works on a dual representation of the sequence database. Work has also been done for mining patterns with constraints, mining closed patterns, mining patterns from multi-dimensional databases, mining closed repetitive gapped subsequences, and other forms of sequential pattern mining. Some works also focus on mining incremental patterns and mining from stream data. We present at least one method of each of these types and discuss their advantages and disadvantages. We conclude with a summary of the work.

A survey on data-driven process monitoring and diagnostic methods for variation reduction in multi-station assembly systems

2019 ◽  
Vol 39 (4) ◽  
pp. 727-739 ◽  
Author(s):  
Yinhua Liu ◽  
Rui Sun ◽  
Sun Jin
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
Fault Diagnosis ◽  
Process Monitoring ◽  
Information And Communications Technology ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Assembly Systems ◽  
Content Type ◽  
Variation Reduction

PurposeDriven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.Design/methodology/approachThis paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.FindingsA classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.Originality/valueThis paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Fault Diagnosis and Reconfiguration for Mobile Robot Localization Based on Multi-Sensors Data Fusion

2021 ◽  
pp. 1-23
Author(s):  
Linda Hachemi ◽  
Mohamed Guiatni ◽  
Abedlkrim Nemra
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Mobile Robot ◽  
Fault Tolerant ◽  
Sensor Data ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Estimation System ◽  
And Performance ◽  
Robot Position

In this paper, we propose a new approach for fault tolerant localization using multi-sensors data fusion for a unicycle-type mobile robot. The main contribution of this paper is a new architecture proposal for fault diagnosis and reconfiguration for mobile robot localization using multi-sensors data fusion and the duplication/comparison approach. Four different sensors usually embedded in mobile robots (Camera, IMU, GPS, and Odometer) are considered, while six different sensors couples combinations are used for sensor data fusion and the duplication of the localization and estimation system. In order to reach this aim, three different filters (EKF, SVSF, and ASVSF) have been proposed and compared. For each selected filter, a comparison mechanism is then introduced to compute different residuals by comparing the estimated robot position for each sensor couples separately. Faults are then detected using the structural residual diagnosis method. This approach assumes the occurrence of a single fault at a given time. A reconfiguration mechanism is then applied by selected the healthy sensors couple and their corresponding fusion filter. Several scenarios are considered for navigation-based fault tolerant localization approaches. Simulation results are presented to illustrate the advantage and performance of the proposed architecture. The proposed solutions are implemented and validated successfully using the V-REP simulator.

scholarly journals Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data

2018 ◽  
Vol 8 (5) ◽  
pp. 3374
Author(s):  
Imam Mukhlash ◽  
Desna Yuanda ◽  
Mohammad Iqbal
Keyword(s):  
Data Mining ◽  
Smart Home ◽  
Pattern Mining ◽  
Technology Development ◽  
Sensor Data ◽  
Mining Machine ◽  
Periodic Pattern ◽  
Time Interval ◽  
Periodic Patterns ◽  
Using Data

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits.

Data Mining Techniques for Outlier Detection

Data Mining ◽  
2013 ◽  
pp. 159-178
Author(s):  
N Suri ◽  
M Murty ◽  
G Athithan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Data Sets ◽  
Data Set ◽  
Research Issues ◽  
Data Mining Techniques ◽  
Performance Requirements ◽  
Typical Data

Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting the objects in a data set with unusual properties is important as such outlier objects often contain useful information on abnormal behavior of the system described by the data set. Outlier detection has been popularly used for detection of anomalies in computer networks, fraud detection and such applications. Though a number of research efforts address the problem of detecting outliers in data sets, there are still many challenges faced by the research community in terms of identifying a suitable technique for addressing specific applications of interest. These challenges are primarily due to the large volume of high dimensional data associated with most data mining applications and also due to the performance requirements. This chapter highlights some of the important research issues that determine the nature of the outlier detection algorithm required for a typical data mining application. The research issues discussed include the method of outlier detection, size and dimensionality of the data set, and nature of the target application. Thus this chapter attempts to cover the challenges and possible research directions along with a survey of various data mining techniques dealing with the outlier detection problem.

Approaches for Pattern Discovery Using Sequential Data Mining

2012 ◽  
pp. 137-154 ◽  
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequence Data ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Stream Data ◽  
Dual Representation ◽  
Advantages And Disadvantages ◽  
Growth Methods

In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in literature. Apriori based methods and the pattern growth methods are the earliest and the most influential methods for sequential pattern mining. There is also a vertical format based method which works on a dual representation of the sequence database. Work has also been done for mining patterns with constraints, mining closed patterns, mining patterns from multi-dimensional databases, mining closed repetitive gapped subsequences, and other forms of sequential pattern mining. Some works also focus on mining incremental patterns and mining from stream data. We present at least one method of each of these types and discuss their advantages and disadvantages. We conclude with a summary of the work.

Spatial relativism in multinationality and performance research

2016 ◽  
Vol 31 (4) ◽  
pp. 187-205
Author(s):  
Keith J. Kelley
Keyword(s):  
Performance Measure ◽  
Research Issues ◽  
Content Type ◽  
Moderating Variables ◽  
Fine Grained ◽  
Body Of Knowledge ◽  
Methodological Rigor ◽  
Relativistic Approach ◽  
Measured Time ◽  
And Performance

Purpose The purpose of this paper is to explore the largely marginalized role of distance or “space” in multinationality and performance (MP) research. It argues that scholars should adopt a more fine-grained, or relativistic approach when studying the link between MP and that conceptualizing multinationality as space may facilitate this. Design/methodology/approach By treating space as though it is liability forming, the paper examines frequently contentious research issues in domain of MP research such as: how is multinationality measured; what are the moderating variables; and what is the performance metric appropriate, as a framework to help illustrate the relativistic nature of MP studies. Findings The paper illustrates that the choice of distance dimensions, along which the “space” is formed, alters the relationship between MP. Furthermore, this relationship is relative to moderating variables that include among others: when the space is measured (time), point of reference and perspective, the firm’s resources (e.g. human and technological capabilities), and the performance measure considered. Originality/value This paper suggests new avenues and approaches toward exploring the effects of multinationality to improve methodological rigor. It identifies several important methodological shortcomings of current and previous research and suggests areas in which current research gaps must be filled to advance this body of knowledge. It introduces the notion of spatial relativism as a concept that will facilitate more fine-grained and contextualized studies.

Data Mining Techniques for Outlier Detection

2011 ◽  
pp. 19-38 ◽  
Author(s):  
N Suri ◽  
M Murty ◽  
G Athithan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Data Sets ◽  
Data Set ◽  
Research Issues ◽  
Data Mining Techniques ◽  
Performance Requirements ◽  
Typical Data

Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting the objects in a data set with unusual properties is important as such outlier objects often contain useful information on abnormal behavior of the system described by the data set. Outlier detection has been popularly used for detection of anomalies in computer networks, fraud detection and such applications. Though a number of research efforts address the problem of detecting outliers in data sets, there are still many challenges faced by the research community in terms of identifying a suitable technique for addressing specific applications of interest. These challenges are primarily due to the large volume of high dimensional data associated with most data mining applications and also due to the performance requirements. This chapter highlights some of the important research issues that determine the nature of the outlier detection algorithm required for a typical data mining application. The research issues discussed include the method of outlier detection, size and dimensionality of the data set, and nature of the target application. Thus this chapter attempts to cover the challenges and possible research directions along with a survey of various data mining techniques dealing with the outlier detection problem.

Applications of Pattern Discovery Using Sequential Data Mining

Data Mining ◽  
2013 ◽  
pp. 947-969
Author(s):  
Manish Gupta ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Intrusion Detection ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Sequential Data ◽  
Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

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