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Collisions in Multiple Dimensions: Introduction01:05

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Related Experiment Video

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a

Fuyuan Xiao1, Masayoshi Aritsugi2, Qing Wang1

  • 1School of Computer and Information Science, Southwest University, No. 2 Tiansheng Road, BeiBei District, Chongqing 400715, PR China.

Artificial Intelligence in Medicine
|September 25, 2016
PubMed
Summary

A new triaxial hierarchical model efficiently analyzes complex event patterns in healthcare big data. This approach significantly improves throughput for patient monitoring and decision-making support systems.

Keywords:
Complex event processingDecision-makingHealth information systemsMulti-dimensional event streamNested pattern queryOptimisation

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Area of Science:

  • Computer Science
  • Health Informatics
  • Data Science

Background:

  • Healthcare information systems generate vast amounts of big data with complex event patterns.
  • Efficient analysis of these patterns is crucial for effective decision-making and patient monitoring.
  • Existing methods may struggle with the complexity and scale of healthcare data streams.

Purpose of the Study:

  • To propose a novel triaxial hierarchical model for analyzing complex event patterns in healthcare big data streams.
  • To develop a multi-query optimization strategy for efficient processing of nested event pattern queries.
  • To enhance decision-making support within health information systems.

Main Methods:

  • Developed a triaxial hierarchical model focusing on hierarchies within nested event pattern queries and event concept hierarchies.
  • Devised a cost-based heuristic for optimizing query execution plans, considering operator and communication costs.
  • Integrated optimized plans with result reuse schemes for a multi-query optimization strategy.

Main Results:

  • Empirical studies demonstrated significant performance improvements under various stream input rates and workloads.
  • Workload 1 showed 4x and 2x throughput improvement; Workload 2 showed 3x and 2x improvement; Workload 3 showed 6x improvement compared to relative works.
  • The strategy effectively supports patient condition monitoring and adapts to changing patient loads.

Conclusions:

  • The proposed triaxial hierarchical model and multi-query optimization strategy enable efficient processing of complex queries.
  • This approach significantly enhances the capabilities of health information systems for data analysis and decision support.
  • The model is effective in handling varying data stream rates and complex analytical workloads.