A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

Liao, Zhifang; Kong, Ling-Yuan; Wang, Xiao; Zhao, Ying; Zhou, Fangfang; Liao, Zhining; Fan, Xiaoping

doi:10.3390/app7030254

Cited by 21 publications

(18 citation statements)

References 24 publications

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“…Future work includes the following: first, we aim to introduce local economic and population flow data to explore the influence of other factors on electricity consumption; second, we would like to explore a new method of enterprise users clustering which can classify users according to data distribution and different premodeling results; third, we would like to employ visualization techniques [50] in the presentation of our solution.…”

Section: Resultsmentioning

confidence: 99%

Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Zheng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability. Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals. In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment. By taking the advantages of Fog Computing framework, we first propose a prototypebased clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users' historical records of electricity consumption and identifying the most important features. Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user's electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used. The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.

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Section: Resultsmentioning

confidence: 99%

Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Zheng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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“…Unusual travel patterns are uncovered by filtering, configuration, and encoding to various visual forms. The anomaly grading view in SHVis [67] present anomaly scores of selected activities. Analysts click on different days and drag date intervals to compare the activities during the different periods of time.…”

Section: Interaction Methodsmentioning

confidence: 99%

Visual Analytics of Anomalous User Behaviors: A Survey

Shi

Liu

Tong

et al. 2020

IEEE Trans. Big Data

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The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behaviorrelated data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss findings and potential research directions.• We categorize four user behaviors, including social interaction, travel, network communication, and transaction based on the data collected from specific data sources. We extract four common data types from these four behaviors, including text, network, spatiotemporal information, and multidimensional data.• We review how research works use visualization techniques combined with interaction methods to analyze anomalous user behaviors. We extract six

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“…Encryption-based methods [12,[31][32][33][34][35][36][37]] make user's location completely invisible to the server by encrypting LBSS query. Although encryption-based methods have high privacy and high quality of service, the calculation and communication costs are large, the deployment is complex, and the optimization algorithm is needed.…”

Section: Related Workmentioning

confidence: 99%

An Improved Privacy-Preserving Framework for Location-Based Services Based on Double Cloaking Regions with Supplementary Information Constraints

Kuang

Wang

et al. 2017

Security and Communication Networks

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With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, while being exposed to the risk of disclosure of privacy on the other side. Attacker will make a fierce attack based on the probability of inquiry, map data, point of interest (POI), and other supplementary information. The existing location privacy protection techniques seldom consider the supplementary information held by attackers and usually only generate single cloaking region according to the protected location point, and the query efficiency is relatively low. In this paper, we improve the existing LBSs system framework, in which we generate double cloaking regions by constraining the supplementary information, and then k-anonymous task is achieved by the cooperation of the double cloaking regions; specifically speaking, k dummy points of fixed dummy positions in the double cloaking regions are generated and the LBSs query is then performed. Finally, the effectiveness of the proposed method is verified by the experiments on real datasets.

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A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

Cited by 21 publications

References 24 publications

Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Visual Analytics of Anomalous User Behaviors: A Survey

An Improved Privacy-Preserving Framework for Location-Based Services Based on Double Cloaking Regions with Supplementary Information Constraints

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