Journal of Advanced Database Management & Systems

Journal of Advanced Database Management & Systems Journal of Advanced Database Management & Systems [2393-8730(e)] is a peer-reviewed hybrid open-access journal launched in 2014 and research on all facets of database management, systems analysis and design, and software engineering. The primary origin of this journal is to be instrumental in the improvement and development of theory and practice related to information technology and the management of information resources. The journal is targeted at academic researchers and practicing IT professionals.

• Data models and database design: relational, object-oriented, document, and graph data models, conceptual and logical modelling, schema design and normalisation, schema evolution and versioning, and design methodologies for data-intensive applications.
• Query processing and optimisation: query languages and algebra, cost-based and rule-based optimisation, join and aggregation algorithms, adaptive and approximate query processing, query execution engines, and workload-aware tuning.
• Transaction management and concurrency: concurrency control protocols, isolation levels and consistency models, recovery and logging, distributed transactions and consensus, advanced transaction models, and workflow and long-running transaction systems.
• Storage, indexing, and access methods: physical storage layout, row and column stores, in-memory and persistent memory databases, index structures including B-trees, hash, bitmap, and learned indexes, compression, and buffer and cache management.
• Distributed, parallel, and cloud databases: sharding and replication strategies, distributed query execution, parallel and massively parallel processing architectures, cloud-native and serverless data services, peer-to-peer and grid-based data systems, and elasticity and resource management.
• Spatial, temporal, and high-dimensional data: spatio-temporal indexing and query processing, multidimensional access structures, similarity and nearest-neighbour search, vector databases and embedding retrieval, trajectory and moving object databases, and outlier detection in high-dimensional spaces.
• Stream and real-time data management: continuous query processing, complex event processing, windowing and time-series models, stream ingestion architectures, sensor and IoT data management, and latency-aware data pipelines.
• Data warehousing, OLAP, and analytics: dimensional modelling and warehouse design, ETL and ELT pipelines, data lakes and lakehouse architectures, online analytical processing and cube computation, materialised view selection and maintenance, and decision support system design.
• Data mining and knowledge discovery: association, clustering, and classification methods over databases, scalable mining algorithms, pattern and sequence discovery, in-database machine learning, and integration of analytics with query engines.
• Semi-structured and document data: XML, JSON, and native document stores, schema-flexible data management, path and tree query processing, indexing of semi-structured data, and interoperability between structured and semi-structured sources.
• Semantic technologies and knowledge representation: ontology design and engineering, description logics and reasoning, RDF stores and SPARQL processing, knowledge graph construction and querying, and semantic integration of heterogeneous sources.
• Data quality, integration, and provenance: entity resolution and deduplication, schema and data matching, data cleaning and repair, uncertainty and incompleteness, provenance tracking and lineage, and measurement frameworks for data quality.
• Database security, privacy, and integrity: discretionary, mandatory, and role-based access control, content-dependent authorisation, encryption of data at rest and in transit, privacy-preserving query processing, differential privacy in data systems, auditing and compliance, and integrity constraints and enforcement.
• Databases for emerging workloads: scientific and array databases, large-scale matrix and tensor operations, bioinformatics and genomic data management, multimedia and unstructured content stores, blockchain and immutable ledgers, and data infrastructure for machine learning pipelines.
• Systems analysis, design, and software engineering for data-intensive systems: requirements engineering for data applications, agile and iterative development practice, object-oriented and model-driven design methods, software quality and defect prediction, testing of data-intensive software, and empirical studies of database system development.
• Web-based and information system applications: web-based information systems, information flow control in data-driven applications, digital libraries and scholarly information systems, content and citation retrieval, and enterprise and e-commerce data architectures.