Aggregate density-based concept drift identification for dynamic sensor data models

Abstract Background: Internet of Things (IoT), earth observation and big scientific experiments are sources of extensive amounts of sensor big data today. We are faced with large amounts of data with low measurement costs. A standard approach in such cases is a stream mining approach, implying that we look at a particular measurement only once during the real-time processing. This requires the methods to be completely autonomous. In the past, very little attention was given to the most time-consuming part of the data mining process, i.e. data pre-processing. Objectives: In this paper we propose an algorithm for data cleaning, which can be applied to real-world streaming big data. Methods/Approach: We use the short-term prediction method based on the Kalman filter to detect admissible intervals for future measurements. The model can be adapted to the concept drift and is useful for detecting random additive outliers in a sensor data stream. Results: For datasets with low noise, our method has proven to perform better than the method currently commonly used in batch processing scenarios. Our results on higher noise datasets are comparable. Conclusions: We have demonstrated a successful application of the proposed method in real-world scenarios including the groundwater level, server load and smart-grid data

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Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI

Neural Computing and Applications ◽

10.1007/s00521-021-06139-8 ◽

2021 ◽

Author(s):

Hemant Ghayvat ◽

Prosanta Gope

Keyword(s):

Healthy Subject ◽

Research Work ◽

Well Being ◽

Data Models ◽

Community Dwelling ◽

Sensor Data ◽

Elderly Subjects ◽

Healthy Elderly ◽

Early Dementia ◽

The Impact

AbstractReasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.

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Evaluation of Open Data Models for the Exchange of Sensor Data in Cognitive Building

2018 Workshop on Metrology for Industry 4.0 and IoT ◽

10.1109/metroi4.2018.8428349 ◽

2018 ◽

Cited By ~ 4

Author(s):

M. Scheffer ◽

M. Konig ◽

T. Engelmann ◽

L. C. Tagliabue ◽

A. L. C. Ciribini ◽

...

Keyword(s):

Open Data ◽

Data Models ◽

Sensor Data

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Challenging multi-sensor data models and use of 360 images. The Twelve Months Fountain of Valentino park in Turin.

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/949/1/012060 ◽

2020 ◽

Vol 949 ◽

pp. 012060

Author(s):

L Teppati Losè ◽

G Sammartano ◽

F Chiabrando ◽

A Spanò

Keyword(s):

Data Models ◽

Sensor Data

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Asynchronous Federated Learning for Sensor Data with Concept Drift

10.1109/bigdata52589.2021.9671924 ◽

2021 ◽

Author(s):

Yujing Chen ◽

Zheng Chai ◽

Yue Cheng ◽

Huzefa Rangwala

Keyword(s):

Concept Drift ◽

Sensor Data

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Multi-View Data Analysis Techniques for Monitoring Smart Building Systems

Sensors ◽

10.3390/s21206775 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6775

Author(s):

Vishnu Manasa Devagiri ◽

Veselka Boeva ◽

Shahrooz Abghari ◽

Farhad Basiri ◽

Niklas Lavesson

Keyword(s):

Clustering Algorithm ◽

Concept Drift ◽

Clustering Algorithms ◽

Sensor Data ◽

Integrated Analysis ◽

Domain Experts ◽

Smart Building ◽

Monitoring Task ◽

Wide Range ◽

Building Systems

In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems’ sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems’ behavior and how it can aid domain experts in the systems’ maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain.

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