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

Time-Series Graph00:54

Time-Series Graph

5.2K
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...
5.2K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

329
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
329
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

274
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
274
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

535
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
535
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

557
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
557
Ogive Graph01:07

Ogive Graph

6.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Challenges of identification and anonymity in time-continuous data from medical environments.

Frontiers in digital health·2025
Same author

A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults.

Scientific data·2025
Same author

A kinematic dataset of locomotion with gait and sit-to-stand movements of young adults.

Scientific data·2024
Same author

Covid notions: Towards formal definitions - and documented understanding - of privacy goals and claimed protection in proximity-tracing services.

Online social networks and media·2021
Same author

StreAM-[Formula: see text]: algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs.

Algorithms for molecular biology : AMB·2017

Related Experiment Video

Updated: Feb 1, 2026

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

14.5K

Compile- and run-time approaches for the selection of efficient data structures for dynamic graph analysis.

Benjamin Schiller1, Clemens Deusser1, Jeronimo Castrillon2

  • 11Privacy and Data Security, Department of Computer Science, TU Dresden, Nöthnitzer Straße 46, Dresden, 01187 Germany.

Applied Network Science
|December 12, 2018
PubMed
Summary

Optimizing dynamic graph analysis is crucial for performance. This study introduces automated data structure selection, achieving significant speedups for both constant and non-constant workloads in graph processing.

Keywords:
Compile-time optimizationData structuresDynamic graph analysisMeasurement studyPerformance

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K

Related Experiment Videos

Last Updated: Feb 1, 2026

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

14.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K

Area of Science:

  • Computer Science
  • Data Science
  • Algorithm Analysis

Background:

  • Dynamic graphs are prevalent in various fields like social network analysis and big data processing.
  • Analyzing high-frequency dynamic graphs demands high performance, which is sensitive to data structure choices.
  • Selecting optimal data structures for dynamic graph analysis is challenging, even for expert programmers.

Purpose of the Study:

  • To automatically select the most efficient data structures for dynamic graph analysis.
  • To extend previous compile-time approaches with a measurement study and cost estimation functions.
  • To develop a run-time approach for dynamic graph analysis with non-constant workloads.

Main Methods:

  • Conducted a measurement study of seven data structures for dynamic graph analysis.
  • Developed and evaluated a compile-time approach for selecting data structures based on estimated costs.
  • Implemented and analyzed a run-time approach with live profiling and automatic data structure exchange.

Main Results:

  • The compile-time approach achieved speedups of up to 5.4× for real-world dynamic graphs with constant workloads.
  • The run-time approach achieved speedups of up to 7.3× for artificial non-constant workloads.
  • Both approaches demonstrated significant performance improvements over basic data structure configurations.

Conclusions:

  • Automated data structure selection significantly enhances dynamic graph analysis performance.
  • The run-time approach effectively addresses the limitations of static, compile-time methods for non-constant workloads.
  • This work provides practical solutions for optimizing performance in dynamic graph processing.