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Electrodeposition01:08

Electrodeposition

Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
Electrodeposition can...

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Parallel Materialization of Large ABoxes.

Sivaramakrishnan Narayanan1, Umit Catalyurek, Tahsin Kurc

  • 1Dept. of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a parallel framework for efficient knowledge base materialization, speeding up computations on large datasets. The approach effectively utilizes shared-nothing architectures but is constrained by TBox complexity.

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Area of Science:

  • Knowledge representation and reasoning
  • Database systems
  • Parallel computing

Background:

  • Efficient materialization is crucial for large knowledge bases.
  • Existing systems face scalability challenges with large ABoxes.
  • Parallel processing offers a potential solution for computational bottlenecks.

Purpose of the Study:

  • To develop and evaluate a framework for efficient knowledge base materialization on shared-nothing parallel machines.
  • To address the computational demands of large ABoxes.
  • To leverage TBox axiom parallelism for performance gains.

Main Methods:

  • A framework for parallel materialization on shared-nothing systems.
  • Partitioning of TBox and ABox axioms using a min-min strategy.
  • Integration with existing inference systems (e.g., SwiftOWLIM) for local computations.
  • Coordination of information exchange between processors.

Main Results:

  • The framework demonstrates speedup in a cluster environment by exploiting TBox axiom parallelism.
  • Experimental evaluation using Lehigh University Benchmark (LUBM) datasets.
  • The approach's performance is influenced by the complexity of the TBox.

Conclusions:

  • The proposed framework offers an efficient approach to knowledge base materialization in parallel environments.
  • Parallelism in TBox axioms can be effectively exploited for performance improvements.
  • TBox complexity remains a limiting factor for this parallel materialization strategy.