A Data Model for Energy Decision Support Systems for Smart Cities - The Case of BESOS Common Information Model

2015 ◽  
Author(s):  
G. Anadiotis ◽  
E. Hatzoplaki ◽  
K. Tsatsakis
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Model ◽  
Support Systems ◽  
Smart Cities ◽  
Information Model ◽  
Common Information ◽  
Common Information Model

Development of Decision Support Systems for Smart Cities

2019 ◽  
pp. 52-63 ◽  
Author(s):  
Fedor Georgievich Maitakov ◽  
Alexander Alekseevich Merkulov ◽  
Evgeny Vladimirovich Petrenko ◽  
Abdurashid Yarullaevich Yafasov
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Smart Cities

scholarly journals FIViz: Forensics Investigation through Visualization for Malware in Internet of Things

2020 ◽  
Vol 12 (18) ◽  
pp. 7262
Author(s):  
Israr Ahmad ◽  
Munam Ali Shah ◽  
Hasan Ali Khattak ◽  
Zoobia Ameer ◽  
Murad Khan ◽  
...  
Keyword(s):  
Decision Support ◽  
Internet Of Things ◽  
Decision Support Systems ◽  
Support Systems ◽  
Smart Cities ◽  
Security And Privacy ◽  
The Internet ◽  
Data Set ◽  
Raw Data ◽  
Internet Of Medical Things

Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim’s device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.

scholarly journals Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities

2021 ◽  
Author(s):  
Nuno Alpalhão ◽  
Miguel de Castro Neto ◽  
Marcel Motta
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Smart Cities ◽  
Temporal Trends ◽  
Mobility Patterns ◽  
Dynamic Framework ◽  
Visualization Techniques ◽  
Spatial And Temporal Trends ◽  
The Impact

Being mobility one of the biggest challenge’s cities face today, the COVID-19 pandemic reinforced this challenge and caused a deep structural change in the mobility of the multilayered dynamic framework of Smart Cities. The need to supply decision support systems to city authorities is higher than ever. Planning and managing mobility in Smart Cities has become more challenging, as the amount of information available and the pressure to enforce sustainable and secure policies increases, stakeholders require faster and more targeted actions. Dashboards are powerful tools that can be used in this context to provide, in an understandable manner, multidimensional information otherwise unavailable in classically static visualizations, as these tools offer a reliable foundation for decision support systems. This chapter goes through the required visualization techniques used to produce meaningful dashboards, to both showcase spatial and temporal trends in the context of mobility in Smart Cities following the COVID-19 pandemic. A general framework for analyzing mobility patterns is suggested by gathering methods and techniques recently developed in the literature.

Competitive Smart Cities through Healthy Decision-Making

2015 ◽  
pp. 37-55 ◽  
Author(s):  
Ori Gudes ◽  
Sarah Jane Edwards ◽  
Tan Yigitcanlar ◽  
Virendra Pathak
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Science ◽  
Support Systems ◽  
Smart Cities ◽  
Health Planning ◽  
Online Information ◽  
Healthy Cities ◽  
Challenges And Opportunities

This chapter examines the challenges and opportunities associated with planning for competitive, smart and healthy cities. The chapter is based on the assumptions that a healthy city is an important prerequisite for a competitive city and a fundamental outcome of smart cities. One of the major decision support systems to support healthy cities is e-health. This chapter focuses on the role of e-health planning, by utilising web-based geographic decision support systems. The chapter proposes the implementation of a novel decision system which would provide a powerful and effective platform for stakeholders to support access online information. This would also provide for better decision-making as well as empower community participation. The chapter highlights the need for a comprehensive conceptual framework to guide the decision process of planning for cities in association with opportunities and limitations. This chapter provides critical insights into using information science-based frameworks.

Competitive Smart Cities through Healthy Decision-Making

2016 ◽  
pp. 1438-1457
Author(s):  
Ori Gudes ◽  
Sarah Jane Edwards ◽  
Tan Yigitcanlar ◽  
Virendra Pathak
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Science ◽  
Support Systems ◽  
Smart Cities ◽  
Health Planning ◽  
Online Information ◽  
Healthy Cities ◽  
Challenges And Opportunities

This chapter examines the challenges and opportunities associated with planning for competitive, smart and healthy cities. The chapter is based on the assumptions that a healthy city is an important prerequisite for a competitive city and a fundamental outcome of smart cities. One of the major decision support systems to support healthy cities is e-health. This chapter focuses on the role of e-health planning, by utilising web-based geographic decision support systems. The chapter proposes the implementation of a novel decision system which would provide a powerful and effective platform for stakeholders to support access online information. This would also provide for better decision-making as well as empower community participation. The chapter highlights the need for a comprehensive conceptual framework to guide the decision process of planning for cities in association with opportunities and limitations. This chapter provides critical insights into using information science-based frameworks.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

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