Distributed and Parallel Databases

Papers
(The TQCC of Distributed and Parallel Databases is 3. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-06-01 to 2025-06-01.)
ArticleCitations
Deep learning-based computer aided diagnosis model for skin cancer detection and classification53
S3QLRDF: distributed SPARQL query processing using Apache Spark—a comparative performance study13
Bio-SODA UX: enabling natural language question answering over knowledge graphs with user disambiguation12
Novel insights on atomic synchronization for sort-based group-by on GPUs11
Four node graphlet and triad enumeration on distributed platforms10
RETRACTED ARTICLE: Dynamic Multilevel Scheduling Strategy (MSS) mechanism for commercial multi-cloud surroundings9
Optimization method of machining parameters based on intelligent algorithm9
zk-Oracle: trusted off-chain compute and storage for decentralized applications9
Introduction to the special issue on self‑managing and hardware‑optimized database systems 20228
Flexible fingerprint cuckoo filter for information retrieval optimization in distributed network7
Parallel continuous skyline query over high-dimensional data stream windows6
Balanced parallel triangle enumeration with an adaptive algorithm6
ReSKY: Efficient Subarray Skyline Computation in Array Databases5
DOE: database offloading engine for accelerating SQL processing5
Cob: a leaderless protocol for parallel Byzantine agreement in incomplete networks4
An SGX-based execution framework for smart contracts upon permissioned blockchain4
Range constrained group query on attribute social graph4
Research on network abnormal data flow mining based on improved cluster analysis4
Indexing temporal relations for range-duration queries3
Accelerating machine learning queries with linear algebra query processing3
Super-resolution reconstruction algorithm for aerial image data management based on deep learning3
Scalable probabilistic truss decomposition using central limit theorem and H-index3
Recursive SQL and GPU-support for in-database machine learning3
0.04234504699707