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

Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

22.3K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
22.3K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

13.7K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
13.7K
Scalar and Vectors01:22

Scalar and Vectors

1.5K
In mechanics, commonly used terms like force, speed, velocity, and work can be classified as either scalar or vector quantities. A scalar is a physical quantity that can be described by its magnitude alone and does not require any directional components. Examples of scalar quantities are mass, area, and length.
Scalar quantities with the same physical units can be added or subtracted according to the usual algebra rules for numbers. For example, a class ending 10 min earlier than 50 min lasts...
1.5K
Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

2.8K
Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....
2.8K
Cartesian Vector Notation01:28

Cartesian Vector Notation

969
Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
969
Structural Classification of Joints01:20

Structural Classification of Joints

4.2K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Understanding Collaborative CT and MRI Utilization Through Network Analysis: Retrospective Study Using Administrative Claims Data.

JMIR formative research·2026
Same author

Virulence of Marek's disease virus in Japan is linked to polymorphisms in the <i>meq</i> oncogene.

The Journal of general virology·2026
Same author

Two distinct polymorphisms in the basic region of Meq protein of marek's disease virus alter pathological progression and clinical manifestations.

Virology journal·2025
Same author

Breast cancer classification based on the integration of diagnostic algorithms for calcifications and masses using a mixture of experts.

PloS one·2025
Same author

Trends in the healthcare burden of hepatitis C after the introduction of direct-acting antivirals in Japan, 2013-2022: A national claims database study in Japan.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2025
Same author

Diabetes Prediction with Code-Based Mecical Insurance Claims Based on Multimodal Representations and Vision-Language Models.

Studies in health technology and informatics·2025
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: Sep 12, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.4K

Allowing Vector Attributes in Relational Database for Japanese Insurance Claims.

Jumpei Sato1,2, Kazuo Goda1, Hiroyasu Kiba2

  • 1The University of Tokyo, Tokyo, Japan.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces vector attributes in relational databases to efficiently manage Japanese public healthcare insurance claims data. This approach significantly speeds up analytical queries for policy making.

Keywords:
Insurance claimsrelational databasevector attributes

More Related Videos

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.4K
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

8.8K

Related Experiment Videos

Last Updated: Sep 12, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.4K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.4K
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

8.8K

Area of Science:

  • Database Management Systems
  • Health Informatics
  • Public Health Policy

Background:

  • Japanese public healthcare insurance claims represent a valuable population-scale dataset for informing healthcare policy.
  • Existing data models struggle to efficiently process these claims due to their complex nested-tuple structure.
  • The transformation of this data into standard relational models is technically challenging and inefficient.

Purpose of the Study:

  • To explore the technical advantages of incorporating vector attributes into relational databases.
  • To assess the effectiveness of this approach for managing large-scale public healthcare insurance claims data.
  • To improve the efficiency of data analysis for healthcare policy development.

Main Methods:

  • Implementation of a relational database system allowing vector attributes.
  • Experimentation using a commercial database implementation with a Japanese population-scale claims dataset.
  • Performance comparison of analytical queries with composite predicates against traditional methods.

Main Results:

  • The proposed approach demonstrated significantly faster analytical query performance, up to 3.4 times improvement.
  • Data preprocessing and database import times incurred only a minor penalty (approximately 10%).
  • Vector attributes effectively manage the complexity of nested-tuple data structures.

Conclusions:

  • Allowing vector attributes in relational databases offers a highly efficient solution for managing public healthcare insurance claims.
  • This method enhances the speed of data analysis, supporting evidence-based healthcare policy making.
  • The approach provides a practical and performant alternative to traditional data management techniques for complex datasets.