Big Data and Cognitive Computing

Scope & Guideline

Pioneering Research in Cognitive Computing and Beyond.

Introduction

Immerse yourself in the scholarly insights of Big Data and Cognitive Computing with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN-
PublisherMDPI
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationBIG DATA COGN COMPUT / Big Data Cogn. Comput.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND

Aims and Scopes

The journal 'Big Data and Cognitive Computing' focuses on the intersection of advanced computing technologies and big data analytics, emphasizing innovative methodologies and applications across various domains.
  1. Big Data Analytics:
    The journal extensively covers methodologies for processing and analyzing large datasets, emphasizing techniques that enhance data extraction, management, and interpretation.
  2. Artificial Intelligence Applications:
    AI methodologies, including machine learning and deep learning, are a central theme, particularly their applications in diverse fields such as healthcare, finance, and social media.
  3. Cognitive Computing:
    Research that explores the use of cognitive computing systems to mimic human thought processes in analyzing and interpreting data is a key focus area.
  4. Interdisciplinary Approaches:
    The journal encourages interdisciplinary research that integrates big data with fields such as healthcare, environmental science, and urban planning, showcasing innovative solutions to complex problems.
  5. Security and Privacy:
    There is a consistent emphasis on the security and privacy challenges posed by big data, particularly in the context of autonomous systems and personal data handling.
  6. Methodological Innovations:
    The journal highlights novel algorithms, frameworks, and models that advance the state of the art in big data processing and analysis.
The journal has identified several trending and emerging themes that reflect current technological advancements and societal needs, highlighting the pivotal role of big data and cognitive computing in addressing contemporary challenges.
  1. Federated Learning and Privacy-Preserving Techniques:
    Recent publications emphasize federated learning approaches that enhance data privacy while allowing collaborative learning across distributed datasets.
  2. Explainable AI (XAI):
    The growing need for transparency in AI systems has led to increased research on explainable AI, focusing on methodologies that make AI decisions understandable to users.
  3. Healthcare Innovations:
    There is a surge in research applying big data and AI techniques to healthcare, particularly in predictive analytics for disease detection and management.
  4. Environmental Monitoring and Sustainability:
    Emerging studies focus on leveraging big data for environmental monitoring and sustainability efforts, addressing climate change and resource management challenges.
  5. Real-Time Data Processing and IoT Integration:
    The integration of IoT with big data analytics for real-time data processing and decision-making is gaining traction, reflecting the increasing importance of smart technologies.
  6. Natural Language Processing (NLP):
    NLP applications are rapidly emerging, particularly in sentiment analysis and social media monitoring, as researchers explore the implications of language data in various contexts.

Declining or Waning

While the journal covers a broad spectrum of topics, certain themes have shown a decline in prominence over recent years, reflecting shifts in research priorities and emerging technologies.
  1. Traditional Data Management Techniques:
    There has been a noticeable decrease in publications focusing on conventional data management strategies, as newer methodologies and technologies gain traction.
  2. General Surveys and Reviews:
    The number of general survey articles has declined, indicating a shift towards more targeted, application-specific research rather than broad overviews.
  3. Basic Statistical Methods:
    Basic statistical analysis papers are becoming less frequent, as more advanced computational methods dominate the research landscape.
  4. Static Data Analysis:
    Research focusing on static data analysis techniques is waning, with a growing preference for dynamic and real-time analytics that address current challenges.
  5. Single-Domain Applications:
    There is a decline in studies that focus solely on single-domain applications, as interdisciplinary research that incorporates multiple fields is increasingly favored.

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