iDMS: An Index-Based Framework for Tracking Distributed Multidimensional Data Streams

Histograms are a compact and effective way to summarize large datasets, representing data distribution by partitioning it into blocks or buckets for visualization and analysis. They find widespread use in diverse fields, from image processing to statistical analysis and database optimization. This p...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) S. 1381 - 1388
Hauptverfasser: Razavi, Zhinoos, Sellis, Timos, Liao, Kewen, Razavi, Shahab
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 24.07.2023
Schlagworte:
Computational modeling
Data models
Data Summarization
Distributed computing
Distributed databases
Generative adversarial networks
Generative models
Histograms
Index data structures
Merging
Statistical analysis
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Histograms are a compact and effective way to summarize large datasets, representing data distribution by partitioning it into blocks or buckets for visualization and analysis. They find widespread use in diverse fields, from image processing to statistical analysis and database optimization. This paper focuses on merging and maintaining a centralized histogram for distributed summaries in multidimensional spaces. The proposed framework employs a dynamic index data structure to efficiently approximate data distribution from continuous distributed data streams. It offers a simple and implementable solution, gener-ating an up-to-date histogram for aggregated multidimensional data summaries while supporting both centralized and local histograms. Experimental results demonstrate its performance in parallel and distributed settings, considering communication cost, error rate, and practicality.
DOI:10.1109/CSCE60160.2023.00231