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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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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.
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Updated: Jun 8, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
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ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments.

Kristalys Ruiz-Rohena1, Manuel Rodriguez-Martínez2

  • 1Department of Electrical and Computer Engineering, University of Puerto Rico, Mayagüez.

Proceedings. IEEE International Conference on Cloud Computing
|November 4, 2024
PubMed
Summary
This summary is machine-generated.

ArcaDB is a new data analytics engine using container technology to optimize query performance. It intelligently places data processing tasks on suitable hardware, achieving up to 3.5x faster results than traditional systems.

Keywords:
Cloud computingDBaaScloud acceleratorsdata analyticsquery engine

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Area of Science:

  • Computer Science
  • Data Engineering
  • High-Performance Computing

Background:

  • Modern enterprises require robust data management for large datasets.
  • Hardware accelerators like GPUs and TPUs are essential for scaling data processing.
  • Existing data analytics engines need architectural evolution to leverage new hardware trends.

Purpose of the Study:

  • To introduce ArcaDB, a disaggregated query engine designed for modern data analytics.
  • To demonstrate how ArcaDB utilizes container technology for efficient operator placement.
  • To evaluate the performance improvements offered by ArcaDB.

Main Methods:

  • Developed ArcaDB, a disaggregated query engine using Java, Python, and Docker containers.
  • Implemented a system where query plans are dispatched to worker nodes with diverse computing characteristics.
  • Annotated operators with preferred compute node types for optimized execution.

Main Results:

  • ArcaDB successfully places operators on compute nodes matching their performance profiles.
  • A prototype implementation was created and tested using image and scientific data.
  • Preliminary studies show ArcaDB speeds up query performance by up to 3.5x compared to shared-nothing architectures.

Conclusions:

  • ArcaDB's disaggregated architecture and container-based operator placement enhance data analytics efficiency.
  • The engine effectively leverages heterogeneous compute nodes for improved query performance.
  • ArcaDB represents a significant advancement in data analytics engine design for modern hardware.