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

Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

31
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
31
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.0K
Correlation of Experimental Data01:23

Correlation of Experimental Data

336
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
336
Aggregates Classification01:29

Aggregates Classification

413
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
413
Contingency Table01:29

Contingency Table

2.7K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.7K
Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

24
The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...
24

You might also read

Related Articles

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

Sort by
Same author

Comment on "Association of Symptoms of neuropsychological long COVID with imaging and plasma biomarkers".

Journal of the neurological sciences·2026
Same author

Comment on "Fennel essential oil and its nanoemulsion modulate macrophage-mediated inflammatory responses and promote pressure ulcer healing".

International immunopharmacology·2026
Same author

A Brief Review on Transdermal Patches.

Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology·2025
Same author

A novel phosphodiesterase target as a therapeutic approach: inhibiting DEN-induced hepatocellular carcinoma progression.

EXCLI journal·2025
Same author

The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review.

PeerJ. Computer science·2025
Same author

FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy.

PeerJ. Computer science·2025

Related Experiment Video

Updated: Oct 12, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Big data integration enhancement based on attributes conditional dependency and similarity index method.

Vishnu Vandana Kolisetty1, Dharmendra Singh Rajput2

  • 1SCOPE, Vellore Institute of Technology, Vellore 632014, India.

Mathematical Biosciences and Engineering : MBE
|November 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the Attribute Conditional Dependency and Similarity Index (ACD-SI) method for big data integration. ACD-SI enhances data analysis by improving data purity and relevance compared to existing techniques.

Keywords:
big dataintegration attributes dependencysimilarity index

More Related Videos

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.9K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Related Experiment Videos

Last Updated: Oct 12, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.9K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Area of Science:

  • Computer Science
  • Data Science
  • Information Science

Background:

  • The exponential growth of big data necessitates efficient integration methods across diverse digital sources.
  • Existing data integration techniques often suffer from processing complexity, time constraints, and limitations in handling multiple data sources.
  • Difficulty in determining relationships and studying data structures hinders effective data access and retrieval for analysis.

Purpose of the Study:

  • To propose a novel big data integration mechanism, termed ACD-SI (Attribute Conditional Dependency and Similarity Index).
  • To address the limitations of existing integration methods by improving data processing efficiency and accuracy.
  • To enhance the ability to determine relationships between data from various sources for better analysis.

Main Methods:

  • Developed the ACD-SI mechanism incorporating an improved Bayesian approach to analyze attribute dependencies.
  • Utilized attribute conversion and selection for data mapping and grouping, facilitating integration.
  • Employed Latent Semantic Analysis (LSA) for content analysis of data attributes to ensure relevance and accuracy.

Main Results:

  • Experimental evaluation on a large bibliographic dataset demonstrated improved data purity and normalization.
  • The Normalized Mutual Information (NMI) ratio confirmed the relevancy of clustered data.
  • Precision, recall, and accuracy rates indicated significant improvements over existing data integration approaches.

Conclusions:

  • The proposed ACD-SI method offers a more effective approach to big data integration.
  • ACD-SI successfully addresses challenges related to data complexity and multi-source integration.
  • The method provides a foundation for more accurate and efficient big data analysis.