SIGMOD Record ACM Journal Impact Factor

SIGMOD Record - ACM | 2024 Cite Score:2.5 | Q2

Cite Score and Journal Rank of SIGMOD Record

About: SIGMOD Record is a peer-reviewed journal published by the ACM Special Interest Group on Management of Data (SIGMOD). It focuses on research and advancements in the field of data management, including database systems, data analytics, and information retrieval. The journal serves as a platform for disseminating significant developments, research findings, and practical insights related to data management technologies and methodologies.
Objective:
The primary objective of SIGMOD Record is to advance the field of data management by publishing high-quality research and innovative practices. The journal seeks to provide a comprehensive resource for researchers, practitioners, and educators to share their work on database systems, data management techniques, and related technologies. SIGMOD Record aims to support the development and dissemination of new ideas and solutions that enhance the efficiency, scalability, and effectiveness of data management systems.
Interdisciplinary Approach:
SIGMOD Record embraces an interdisciplinary approach, welcoming contributions from a range of fields such as computer science, information systems, engineering, and applied mathematics. This approach ensures a broad exploration of data management topics, integrating various perspectives and methodologies. By promoting interdisciplinary research, the journal addresses complex challenges in data management and fosters the development of integrated solutions that advance the state of knowledge in the field.
Impact:
The journal has a significant impact on both academic research and practical applications in data management. It is widely cited by researchers, practitioners, and industry professionals interested in the latest advancements in database systems and data management technologies. The research published in SIGMOD Record contributes to the development of new techniques, tools, and methodologies that improve the efficiency and effectiveness of data management systems. The journal serves as a valuable resource for professionals involved in the design, implementation, and optimization of data management solutions.
Significance:
SIGMOD Record plays a crucial role in advancing the study and practice of data management by providing a platform for high-quality research and practical insights. Its contributions support the development of innovative technologies and methodologies that address current and future challenges in data management. The journals commitment to excellence and interdisciplinary focus make it an essential resource for anyone involved in research, development, and application in the field of data management. Through its rigorous scholarship and broad coverage, SIGMOD Record helps shape the future of data management systems, driving progress in the effective handling and utilization of data.

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Scope: The SIGMOD Record is the official publication of the ACM Special Interest Group on Management of Data (SIGMOD). It focuses on research and advancements in data management and related areas. Its scope includes, but is not limited to:
Database Management Systems (DBMS): Research on the design, implementation, and optimization of database management systems, including both relational and non-relational databases.
Data Models and Query Languages: Studies on data models (e.g., relational, object-oriented, NoSQL) and query languages (e.g., SQL, SPARQL), including their design, extension, and application.
Data Storage and Indexing: Exploration of techniques for data storage, indexing, and retrieval, including data structures like B-trees, hash indexes, and spatial indexes.
Database Architecture and Performance: Research on the architecture of database systems, including distributed databases, parallel databases, and performance optimization techniques.
Data Integration and Interoperability: Studies on methods for integrating data from diverse sources, including data warehousing, ETL (extract, transform, load) processes, and schema matching.
Database Security and Privacy: Research on securing databases and protecting data privacy, including access control, encryption, and data anonymization.
Data Provenance and Quality: Exploration of techniques for tracking data provenance, ensuring data quality, and managing data lineage.
Data Analytics and Mining: Studies on techniques for analyzing and mining data, including data mining algorithms, statistical analysis, and machine learning approaches.
Big Data and Cloud Computing: Research on managing and processing large-scale data in cloud environments, including big data frameworks (e.g., Hadoop, Spark) and cloud-based data services.
Database Applications: Practical implementations and case studies of database systems in various domains, including healthcare, finance, e-commerce, and social networks.