DISTRIBUTED AND PARALLEL DATABASES

Scope & Guideline

Transforming Data Management through Collaborative Research

Introduction

Explore the comprehensive scope of DISTRIBUTED AND PARALLEL DATABASES through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore DISTRIBUTED AND PARALLEL DATABASES in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN0926-8782
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1993 to 2024
AbbreviationDISTRIB PARALLEL DAT / Distrib. Parallel Databases
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS

Aims and Scopes

The journal 'DISTRIBUTED AND PARALLEL DATABASES' focuses on advancing the field of database systems, particularly in distributed and parallel architectures. It aims to publish high-quality research that addresses the challenges and innovations in managing and processing large-scale data across different environments.
  1. Distributed Database Systems:
    Research on the design, implementation, and optimization of databases that are spread across multiple locations, focusing on data distribution, replication, and consistency.
  2. Parallel Processing Techniques:
    Innovative methodologies and algorithms for parallel query processing and data management, leveraging multi-core and distributed computing environments.
  3. Big Data Management:
    Strategies and technologies for handling large volumes of data, including storage, retrieval, and processing techniques tailored for big data applications.
  4. Blockchain and Decentralized Systems:
    Exploration of blockchain technologies and their applications in databases, including smart contracts and secure distributed ledgers.
  5. Machine Learning and AI Integration:
    Research on integrating machine learning algorithms with database systems for enhanced data analytics and decision-making capabilities.
  6. Internet of Things (IoT) Data Management:
    Techniques for managing and processing data generated by IoT devices, emphasizing scalability and efficiency.
  7. Graph Databases and Query Processing:
    Study of graph-based data management techniques, including novel algorithms for querying and analyzing graph data structures.
  8. Security and Privacy in Databases:
    Research focused on ensuring data security and privacy in distributed and parallel database systems, addressing challenges related to data breaches and unauthorized access.
The journal has shown a dynamic evolution in its focus areas, reflecting the latest trends and advancements in technology and data management. Researchers are increasingly drawn to innovative solutions that address contemporary challenges.
  1. Decentralized Applications and Blockchain Technologies:
    A significant uptick in research related to decentralized applications and blockchain technologies, highlighting their importance in modern data management and security.
  2. Federated Learning and Computation:
    Emerging interest in federated learning, which enables collaborative data processing and analysis while preserving privacy, indicating a shift towards more decentralized data strategies.
  3. Graph Processing and Analysis:
    Growing emphasis on graph databases and algorithms for processing complex networks, reflecting the increasing relevance of graph data in various applications.
  4. Energy-Efficient Data Processing:
    Research focused on energy-efficient techniques for data processing in cloud and distributed environments is gaining momentum, driven by the need for sustainable computing practices.
  5. AI and Machine Learning Integration in Databases:
    A marked increase in studies exploring the integration of AI and machine learning within database systems, aimed at enhancing data analytics and decision-making capabilities.

Declining or Waning

In contrast to the emerging themes, certain areas within the journal's scope appear to be losing traction or are being addressed less frequently in recent publications. This decline could reflect shifts in research priorities or the maturation of existing methodologies.
  1. Traditional Relational Database Optimization:
    There has been a noticeable decrease in research focused solely on traditional relational database optimization techniques, as newer paradigms like NoSQL and distributed systems gain prominence.
  2. Static Data Models:
    Research on static data models and schemas is becoming less prevalent, as dynamic and flexible data structures are increasingly favored in modern applications.
  3. Basic Query Processing Algorithms:
    The focus on fundamental query processing algorithms, such as simple join operations without parallelization, is waning as more complex and efficient approaches are developed.
  4. Centralized Database Architectures:
    The interest in centralized database architectures is declining, as the shift towards distributed and cloud-based systems continues to dominate the research landscape.
  5. Legacy Systems Integration:
    Research related to integrating legacy systems with modern databases is becoming less common, possibly due to the rapid advancement in new technologies that obviate the need for such integration.

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