Scalable Computing-Practice and Experience

Scope & Guideline

Transforming Ideas into Scalable Realities

Introduction

Welcome to the Scalable Computing-Practice and Experience information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Scalable Computing-Practice and Experience, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1895-1767
PublisherUNIV VEST TIMISOARA, WEST UNIV TIMISOARA
Support Open AccessNo
CountryRomania
TypeJournal
Convergefrom 2005 to 2024
AbbreviationSCALABLE COMPUT-PRAC / Scalable Comput.-Pract. Exp.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressBLVD VASILE PARVAN 4, TIMISOARA, TIMIS 300223, ROMANIA

Aims and Scopes

Scalable Computing: Practice and Experience focuses on advancing the field of scalable computing through innovative methodologies and applications. The journal emphasizes the integration of cutting-edge technologies, including machine learning, artificial intelligence, and the Internet of Things, to solve complex problems across various domains.
  1. Scalable Computing Techniques:
    The journal explores methodologies for enhancing computational scalability, particularly in distributed systems, cloud computing, and big data analytics.
  2. Integration of AI and Machine Learning:
    A significant focus is on applying artificial intelligence and machine learning algorithms to optimize processes in various fields, including healthcare, agriculture, and logistics.
  3. IoT Applications:
    The research includes applications of the Internet of Things (IoT) in smart cities, environmental monitoring, and industrial automation, highlighting the importance of data-driven decision-making.
  4. Interdisciplinary Approaches:
    The journal encourages interdisciplinary studies that combine technology with domains like education, healthcare, and sports to improve outcomes and efficiencies.
  5. Performance Optimization:
    Research on performance enhancements in computing environments, including algorithm efficiency, resource management, and system architecture improvements.
The journal has identified several emerging themes that reflect current trends in scalable computing. These themes are indicative of the evolving landscape of technology and research priorities.
  1. Deep Learning Applications:
    There is a significant rise in research focusing on deep learning applications across various domains, including healthcare diagnostics, environmental monitoring, and automated systems.
  2. Blockchain Integration:
    The integration of blockchain technology for data security, transparency, and traceability in various applications, particularly in supply chain and healthcare, is gaining traction.
  3. Sustainable Computing Solutions:
    Emerging research on sustainable and green computing practices reflects a growing concern for environmental impact, with a focus on energy-efficient algorithms and smart resource management.
  4. Smart City Technologies:
    Increased focus on smart city technologies, including IoT implementations and data-driven urban planning, highlights the relevance of scalable computing in addressing urban challenges.
  5. Federated Learning and Privacy Preservation:
    The trend towards privacy-preserving machine learning techniques, such as federated learning, is emerging as a critical area of study, especially in sensitive data environments like healthcare.

Declining or Waning

While the journal continues to thrive in many areas, certain themes have shown a decline in publication frequency or relevance. These waning scopes reflect shifts in research priorities and technological advancements.
  1. Traditional Computing Models:
    There has been a noticeable decrease in papers focusing solely on traditional computing models, as the field moves towards more integrated and scalable solutions involving AI and IoT.
  2. Basic Algorithm Studies:
    Research that solely focuses on the theoretical aspects of algorithms without practical application in real-world scenarios is becoming less frequent, as the emphasis shifts to applied research.
  3. Standalone Data Analysis Techniques:
    Papers that present isolated data analysis techniques without integration into broader systems or frameworks are declining, reflecting a trend towards comprehensive, system-based approaches.
  4. Hardware-Specific Innovations:
    Innovations that are limited to specific hardware improvements are waning, with more emphasis being placed on software solutions and cloud-based architectures.
  5. Legacy Systems Integration:
    Research on integrating legacy systems with new technologies has decreased as organizations increasingly prioritize complete system overhauls or cloud migrations.

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