InnoMetrics Dashboard: The Design, and Implementation of the Adaptable Dashboard for Energy-Efficient Applications Using Open Source Tools

Abstract

Increasing amount of data the organizations worldwide have at their disposal lead to the need to structure, organize and present the information obtained from it. That is because, in today's rapid-changing business environment, managers and executives need to be able to gain crucial insights about the ongoing project in as little time as possible. Recently, energy efficiency has become a greater field of research, and companies started concentrating on monitoring energy-related metrics. In addition, many of them have built their own internal tools (dashboards) to do just this. However, one of the major drawbacks of building specialized tools is the lack of adaptability. That is, they are often tailored to only one person (e.g. CEO), or a small group of them (e.g. board of directors, managers). Furthermore, the combination of metrics that is displayed to them does not change over time. This is a problem because most likely there exists a better metric combination that would allow users to get the crucial insights faster. To fill this gap, our ongoing research focuses on making the dashboards adaptable to multiple roles within the organization while optimizing for a certain goal. In some scenarios the dashboard's goal may be to detect defects, in others it may be to generate the most profit. As our primary research interest is to amplify energy efficiency, we have chosen that to be our dashboard's goal. Our previous work suggests that in order to handle compound metrics at scale it is needed to represent the dashboard as a complex system. This paper presents the design and the architecture of our proposed solution synergizing the notions from complexity theory, software architecture and user experience (UX) design.

Design of an adaptable dashboard for smart cities

“…What elements can be adapted within a smart city dashboard? Based on the research questions and considering other terms obtained from previous research [16][17][18]9], we identified the following key words that are consistent with what is proposed to be found: "Smart city", "Adaptable dashboard", "Information visuali*ation", and "Key performance indicator". Once the terms had been defined, they were used to create the search string (S1): S1.…”

“…Decision-makers can contrast the data through visual components (either static or real-time KPIs) to make comparisons and inferences to improve city operations [21] [22]. One of the biggest challenges of dashboards for smart cities is to satisfy the different information visualization needs of users to ensure that they are useful in their decision making [9]. In the current development of dashboards for smart cities, the aim is to provide the user with visual representations of data that are part of several screens [23].…”

“…In the current development of dashboards for smart cities there is a tendency to focus on a single user or a group with specific characteristics [9], which affects the deployment in cases where a user who is outside the specific context seeks to use the dashboard for their benefit. Some authors [10][11][12] have explored the different information needs of dashboard users and tried to approach the problem from a user-centric perspective, which has led to the development of different dashboards for different users.…”

Analysis of Energy Consumption of Software Development Process Entities

“…The advantages of micro-services can be characterized as the ease of maintainability, scalability, and cross-functional team maintenance. In order to provide a prototype that will help us to start the data collection process and validate our data model, a system with a monolithic API has been developed that contains all the required functionalities such as handling the requests of the data collection components and providing the information needed in the dashboard [16] to be used for data analysis.…”

Uncertainty Visualization for Renewable Energy Potential

“…First, interactive visualization can aid in model development by providing a functionality for modelers to explore their data and models in ways which are unconventional [6,29]. Such interactions and data exploration allow for the discovery of model features and behaviors which would be difficult to assess using traditional quantitative methods alone and might lead to model refinements, e.g., refined spatial resolution, inclusion/exclusion of assumptions, or adjustments in model parameters or input data [4,7,11]. And second, visual representations of uncertainty which are interactive, in some sense help bring data to life and provide for a more engaging way to convey scientific findings than traditional methods, e.g., bar charts, static maps, or histograms [16,38].…”