Related Experiment Video
Updated: Jun 24, 2025
07:31
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
7.1K
VisCARS: Knowledge Graph-Based Context-Aware Recommender System for Time-Series Data Visualization and Monitoring
IEEE Transactions on Visualization and Computer Graphics
|June 13, 2024
Summary
This study introduces VisCARS, a context-aware visualization recommender system. It personalizes dashboards for monitoring applications by using knowledge graphs and user preferences, enhancing data visualization efficiency.
Area of Science:
- Data Science
- Human-Computer Interaction
- Software Engineering
Background:
- Creating effective data visualizations requires significant expertise in data models and dashboard applications.
- Manual dashboard creation is time-consuming and complex for users.
- Monitoring applications necessitate efficient and accurate data presentation.
Purpose of the Study:
- To develop a context-aware visualization recommender system (VisCARS) that automates personalized dashboard creation.
- To reduce the user's burden of requiring expert knowledge for visualization and dashboard design.
- To improve the efficiency and effectiveness of data visualization in monitoring applications.
Main Methods:
- Utilized a knowledge graph-based approach to incorporate expert knowledge as contextual features.
- Developed a dashboard ontology to semantically annotate the knowledge graph.
- Employed knowledge graph embedding, comparison techniques, and context-aware collaborative filtering.
Main Results:
- Implemented and integrated VisCARS into a dynamic dashboard solution.
- Evaluated the system on a smart healthcare use-case, demonstrating strong performance and scalability.
- Showcased superior results compared to state-of-the-art methods.
Conclusions:
- VisCARS effectively recommends personalized dashboards by considering system context and user preferences.
- The knowledge graph approach enhances the accuracy and relevance of visualization recommendations.
- The system shows significant potential for time-critical monitoring applications, improving user experience and data interpretation.
Related Concept Videos
00:54Time-Series Graph
4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
01:34Velocity and Position by Graphical Method
7.4K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.4K
01:12Average and Instantaneous Velocity Vectors
6.2K
To calculate other physical quantities in kinematics, the time variable must be introduced. The time variable not only allows us to state where an object is (its position) during its motion, but also how fast it’s moving. The speed at which an object is moving is given by the rate at which the position changes with time. For each position, a particular time is assigned. If the details of the motion at each instant are not important, the rate is usually expressed as the average velocity v.
6.2K
01:15Scatter Plot
6.8K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
6.8K
01:26Classification of Systems-I
179
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
179
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K