Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering

Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential speedups when applied to many of the toughest Array (Tensor) DBMS challenges stipulated by classical computing and real-world Earth data use-cases. We also propose new types of charts: Quantum Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how multiple operations occur simultaneously across different data items and what are the input/output data dependencies between these operations. Unlike traditional Gantt charts, which typically track project timelines and resources, QGantt charts integrate specific data items and operations over time. A Quantum Network Diagram combines several QGantt charts to show dependencies between multistage operations, including their inputs/outputs. By using a static format, QGantt charts and Quantum Network Diagrams allow users to explore complex processes at their own pace, which can be beneficial for educational and R&D purposes.

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Publication Details

Journal
Earth
Published
2024-09-14
DOI
https://doi.org/10.3390/earth5030027
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering

Ramon Antonio Rodriges Zalipynis
Earth
Computational Physics and Python Applications
article

Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering

Ramon Antonio Rodriges Zalipynis
article en

Abstract

Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential speedups when applied to many of the toughest Array (Tensor) DBMS challenges stipulated by classical computing and real-world Earth data use-cases. We also propose new types of charts: Quantum Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how multiple operations occur simultaneously across different data items and what are the input/output data dependencies between these operations. Unlike traditional Gantt charts, which typically track project timelines and resources, QGantt charts integrate specific data items and operations over time. A Quantum Network Diagram combines several QGantt charts to show dependencies between multistage operations, including their inputs/outputs. By using a static format, QGantt charts and Quantum Network Diagrams allow users to explore complex processes at their own pace, which can be beneficial for educational and R&D purposes.

EarthVol. 5(3)
National Research University Higher School of Economics (RU)
Openalex Percentile: Top 87%
Computational Physics and Python Applications
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Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering — Ramon Antonio Rodriges Zalipynis · Earth (2024) | TGRS Research Map | TGRS