Ir directamente a la navegación principal Ir directamente a la búsqueda Ir directamente al contenido principal

Real-Time Analytics with Big Data and Streaming Computation

  • Ashvin B. Amale
  • , K. K. Bajaj
  • , Mohamed Dawood Shamout
  • , Liliana Carmen Campos Ramírez
  • , María Luisa Medina Vásquez
  • , Jose Ricardo Yataco Torrealva
  • Yeshwantrao Chavan College of Engineering
  • RNB Global University
  • University of Sharjah
  • Universidad Nacional de San Luis

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

3 Citas (Scopus)

Resumen

Real-time analytics with significant facts and streaming computation is a more and more essential up-to-the-minuteolset for companies up-to-the-minute generate insights from massive, complicated facts units. This generation lets groups up-to-the-minute fast examine, interpret, and act on facts, supporting them to live ahead of the competition and provide better services to up-to-the-minute clients. With real-time analytics, data can be continuously accumulated and analyzed from various resources, with computerized alerting of pertinent facts. Circulate processing allows up-to-the-minute, up-to-the-moment detection and reply to up-to-the-minute incoming records while not having up-to-the-minute re-run queries every time. This generation permits organizations up-to-the-minute advantage and valuable insights from client behavior, ensuring compliance and protection, saving you fraudulent transactions, and optimizing IT overall performance. Real-time analytics and movement processing are critical components for any agency to date make up the maximum of their facts. Actual-time analytics with huge statistics and streaming computation is a powerful approach for reading massive statistics streams in real-time. This methodology combines the usage of significant data technologies along with NoSQL, dispensed computing, device date, streaming architectures, up-to-date hasty technique, and visualizes information in up-to-the-minuteup moment actual-time. Data can be accrued from a spread of sources, such as sensors, internet APIs, and user-generated content, and then streamed thru complicated algorithms up-to-the-minute up to the moment and visualized insights. Those insights can then be used for date decisions at a granular degree, with the consequences reaping benefits from an extensive range of areas from supply chain optimization to predictive upkeep. This technique helps corporations up to the moment boom their operational efficiencies while minimizing the fee and time related to the moment analytics on massive datasets.

Idioma originalInglés
Título de la publicación alojada2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1668-1673
Número de páginas6
ISBN (versión digital)9798350357769
DOI
EstadoPublicada - 2023
Publicado de forma externaSí
Evento2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023 - Greater Noida, India
Duración: 19 dic. 2023 → 23 dic. 2023

Serie de la publicación

Nombre2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

Conferencia

Conferencia2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023
País/TerritorioIndia
CiudadGreater Noida
Período19/12/23 → 23/12/23

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

  1. ODS 7: Energía asequible y no contaminante
    ODS 7: Energía asequible y no contaminante

Huella

Profundice en los temas de investigación de 'Real-Time Analytics with Big Data and Streaming Computation'. En conjunto forman una huella única.

Citar esto