Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Review and Preview01:13

Review and Preview

8.9K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
8.9K
Bar Graph01:07

Bar Graph

16.1K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
16.1K
5-Number Summary01:04

5-Number Summary

4.2K
In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
4.2K
Time-Series Graph00:54

Time-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
Manipulation and Analysis01:21

Manipulation and Analysis

23
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
23
Statgraphics01:10

Statgraphics

121
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
121

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Quantum-enhanced spiking intelligence framework for real-time anomaly detection in industrial internet of things.

Scientific reports·2026
Same author

Multimodal healthcare system for human activity recognition using multiple features and advanced ensemble classifier.

Digital health·2026
Same author

Toward intelligent rehabilitation: Multimodal human pose modeling with parametric meshes and graph-based temporal reasoning.

Digital health·2026
Same author

A Novel Network-Level Fused Self-Attention Deep Neural Network for Cervical Cancer Classification from Cervicography Images.

Technology in cancer research & treatment·2026
Same author

Deep locomotion prediction learning over biosensors, ambient sensors, and computer vision.

PloS one·2026
Same author

Prediction of <i>β</i>-thalassemia carrier using federated learning and explainable AI.

Frontiers in medicine·2026

Related Experiment Video

Updated: Jun 18, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.4K

Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization.

Syed Jalaluddin Hashmi1, Bayan Alabdullah2, Naif Al Mudawi3

  • 1School of Computing, National University of Computer and Emerging Science, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary

This study introduces IPGS, a faster algorithm for personalized graph summarization. It efficiently analyzes large graph datasets, like those in biosensors, by focusing on targeted nodes for better data mining and visualization.

Keywords:
Bio–Mouse–Genebig datacorrection setsdata miningdata visualizationgraph summarizationsensors datasetsweighted LSH

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.3K

Related Experiment Videos

Last Updated: Jun 18, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.4K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.3K

Area of Science:

  • Data Science
  • Bioinformatics
  • Network Analysis

Background:

  • Large-scale sensory data, particularly in healthcare and biosensors, is growing rapidly.
  • Graph representations are crucial for analyzing complex relationships within this data.
  • Existing graph summarization methods struggle with large datasets and lack personalization.

Purpose of the Study:

  • To develop a faster algorithm for personalized graph summarization (PGS).
  • To improve data mining and visualization of large graph datasets, especially in biosensor applications.
  • To achieve comparable compression ratios to state-of-the-art PGS methods with enhanced efficiency.

Main Methods:

  • Introduced IPGS, an improved algorithm for personalized graph summarization.
  • Utilized weighted, locality-sensitive hashing to accelerate the summarization process.
  • Conducted experiments on eight large, publicly available datasets, including the Bio-Mouse-Gene dataset.

Main Results:

  • IPGS demonstrates significant improvements in execution time compared to existing PGS algorithms.
  • The algorithm achieves compression ratios comparable to the state-of-the-art.
  • Effectiveness and scalability were validated across diverse large-scale datasets.

Conclusions:

  • IPGS offers an efficient and scalable solution for personalized graph summarization.
  • The method enhances data mining and visualization capabilities for complex sensory datasets.
  • This research contributes to the analysis of biosensor data through advanced graph summarization techniques.