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 Experiment Video

Updated: May 8, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
17:01

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

PRESEE: an MDL/MML algorithm to time-series stream segmenting.

Kaikuo Xu1, Yexi Jiang, Mingjie Tang

  • 1College of Computer Science & Technology, Chengdu University of Information Technology, Chengdu 610225, China.

Thescientificworldjournal
|August 20, 2013
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Medial wall/arthroscopy/posterior facet (MAP) strategy for displaced intra-articular calcaneal fractures: evaluation of a novel minimally invasive protocol in a retrospective comparative cohort.

Journal of orthopaedic surgery and research·2026
Same author

Global, regional, and national burden of osteoarthritis in elderly from 1990 to 2021: Insights from the global burden of disease study 2021.

Medicine·2026
Same author

Burden of head and neck cancers in five East Asian countries from 1990 to 2023: Observation, comparison, and forecast from the global burden of disease study 2023.

PloS one·2026
Same author

Electronic-Structure-Regulated Hydrogen Atom Transfer for the Construction of High-Performance Nitroimino-Functionalized Fused Heterocyclic Energetic Materials.

Organic letters·2026
Same author

[<i>Tongdu Jieyu</i> acupuncture combined with local surrounding needling for 34 cases of melasma with liver <i>qi</i> stagnation].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2026
Same author

Integrating <i>N</i>,<i>N</i>'-Azo and <i>N</i>-Nitramino Motifs within a Triazolone Framework toward a High-Energy-Density Energetic Material.

The Journal of organic chemistry·2026
Same journal

Solvent Extraction of Metals in the Circular Economy: Enhancing Resource Efficiency and Sustainability.

TheScientificWorldJournal·2026
Same journal

Agronomic Performance and Nutritive Value Evaluation of Desho Grass Varieties Under Supplementary Irrigation in Western Oromia, Ethiopia.

TheScientificWorldJournal·2026
Same journal

Physicians' and Hospital Administrators' Perspectives of Diagnosis-Related Groups (DRGs) in High-Income Countries: A Systematic Review.

TheScientificWorldJournal·2026
Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026
Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026
Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026
See all related articles

PRESEE is a novel algorithm for time-series stream segmentation, significantly improving efficiency and enabling automatic data segmentation. This parameter-free approach accelerates real-time stream mining by nearly ten times.

Area of Science:

  • Data Mining
  • Time-Series Analysis
  • Machine Learning

Background:

  • Time-series streams are crucial in finance, ecology, and healthcare.
  • Efficient segmentation accelerates stream mining but existing methods lack speed and user-friendliness.
  • Parameter tuning in current algorithms poses challenges for practical application.

Purpose of the Study:

  • Introduce PRESEE, a parameter-free, real-time, and scalable algorithm for time-series stream segmentation.
  • Enhance the efficiency of time-series stream processing.
  • Provide an automated segmentation method that is easy to use.

Main Methods:

  • Developed PRESEE based on Minimum Description Length (MDL) and Minimum Message Length (MML) principles.
  • Designed for automatic, parameter-free segmentation of time-series data streams.

Related Experiment Videos

Last Updated: May 8, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
17:01

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

  • Implemented and evaluated PRESEE on diverse real-time stream datasets.

    • Main Results:

    • PRESEE demonstrates significant efficiency improvements, achieving nearly tenfold speed increase in segmentation.
    • The algorithm successfully segments real-time data streams automatically.
    • Experimental results validate PRESEE's effectiveness compared to state-of-the-art methods.

    Conclusions:

    • PRESEE offers a highly efficient and scalable solution for time-series stream segmentation.
    • Its parameter-free nature simplifies application in real-time data mining scenarios.
    • Successful application on ChinaFLUX sensor network data highlights its practical utility.