DATA & KNOWLEDGE ENGINEERING

Scope & Guideline

Innovating Knowledge Engineering for Tomorrow's Challenges

Introduction

Delve into the academic richness of DATA & KNOWLEDGE ENGINEERING with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN0169-023x
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Converge1985, from 1987 to 2024
AbbreviationDATA KNOWL ENG / Data Knowl. Eng.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal 'DATA & KNOWLEDGE ENGINEERING' focuses on the intersection of data management, knowledge engineering, and intelligent systems. It aims to advance methodologies, frameworks, and applications that enhance the understanding, processing, and utilization of data in various domains.
  1. Data Engineering and Management:
    Research in this area includes the development of techniques for efficient data storage, retrieval, and processing, particularly in the context of big data and NoSQL systems.
  2. Knowledge Representation and Ontologies:
    This scope covers the creation and application of ontologies for knowledge management, semantic retrieval, and data integration, facilitating better understanding and interoperability among systems.
  3. Artificial Intelligence and Machine Learning:
    Studies focus on integrating AI techniques with data engineering, including the use of machine learning algorithms for data analysis, predictive modeling, and decision support systems.
  4. Process Modeling and Analysis:
    Research in this area emphasizes the design and optimization of business processes, often utilizing data-driven approaches to enhance operational efficiency.
  5. Data Quality and Governance:
    This scope addresses methodologies for ensuring data quality, as well as frameworks for data governance, emphasizing the importance of trustworthy data in decision-making.
  6. Big Data Analytics and Applications:
    This area explores algorithms and frameworks for analyzing large datasets across various applications, aiming to extract valuable insights and knowledge.
The journal has shown a dynamic evolution in its research themes, reflecting emerging trends in technology and methodologies. The following areas have gained increased attention in recent publications, indicating their rising significance.
  1. Explainable AI and Interpretability:
    With a growing emphasis on transparency in AI models, research focusing on explainable AI is becoming increasingly important, particularly in sensitive domains like healthcare.
  2. Data Ecosystems and Governance:
    The exploration of frameworks for managing complex data ecosystems and ensuring data governance has gained momentum, reflecting the need for robust data management practices.
  3. Integration of AI with IoT:
    As the Internet of Things (IoT) continues to expand, the integration of AI with IoT for smarter data collection and analysis is emerging as a key theme, driving innovation in various applications.
  4. Advanced Machine Learning Techniques:
    There is a notable rise in research focusing on sophisticated machine learning techniques, including deep learning and hybrid models, which are being applied to complex data challenges.
  5. Semantic Data Retrieval:
    Research on improving semantic search and retrieval mechanisms is trending, highlighting the need for more effective ways to access and utilize large datasets.

Declining or Waning

While the journal maintains a broad focus on data and knowledge engineering, certain themes are becoming less prominent in recent publications. These waning scopes may indicate shifts in research interests or saturation in specific areas.
  1. Traditional Database Management Systems:
    There is a noticeable decline in research focused on conventional relational database management systems, possibly due to the rise of NoSQL and big data technologies that offer more flexibility and scalability.
  2. Basic Data Mining Techniques:
    As more advanced methodologies and machine learning approaches gain traction, traditional data mining techniques are being overshadowed, leading to a reduced emphasis on simpler algorithms.
  3. Generic Applications of Data Analytics:
    Research that applies data analytics in a one-size-fits-all manner is decreasing, as there is a growing preference for context-specific and domain-driven applications.
  4. Static Knowledge Bases:
    The focus on static knowledge representation is waning in favor of dynamic, evolving knowledge systems that can adapt to new information and contexts.
  5. Descriptive Analytics:
    There is a shift away from purely descriptive analytics toward more predictive and prescriptive analytics, indicating a demand for deeper insights and actionable recommendations.

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