International Journal of Data Structure Studies

International Journal of Data Structure Studies is a peer-reviewed hybrid open-access journal launched in 2023 is an international peer-reviewed journal that is concerned to deliver good quality knowledge to readers. Journal accepts original research and review papers and welcomes experimental and theoretical papers of exceptional quality.

• Fundamental structures and abstract data types: arrays, lists, stacks, and queues, trees and forests, sets, maps, and multisets, specification and implementation of abstract data types, and trade-off analysis between representations.
• Search trees and balanced structures: binary search trees and rotation-based balancing, red–black, AVL, and weight-balanced trees, B-trees, B+ trees, and multidimensional variants, splay trees and self-adjusting structures, finger search and dynamic optimality, and bulk operations on ordered structures.
• Hashing and dictionary structures: open addressing and chaining schemes, cuckoo, hopscotch, and robin hood hashing, perfect and minimal perfect hashing, universal and tabulation hashing families, resizing and load factor behaviour, and dictionary maintenance under insertions and deletions.
• Priority queues and mergeable structures: binary, binomial, and Fibonacci heaps, pairing and rank-pairing heaps, double-ended priority queues, meldable structures, and heap performance in graph algorithm contexts.
• Succinct, compact, and compressed structures: rank and select structures over bit vectors, succinct trees and graphs, wavelet trees, compressed suffix arrays and FM-indexes, entropy-bounded space analysis, and space–time trade-offs in practice.
• Text and sequence indexing: suffix trees, suffix arrays, and affix trees, construction algorithms and their complexity, self-indexing and compressed text databases, pattern matching and repeat detection structures, and indexing for genomic and large text corpora.
• Geometric and spatial structures: range trees and priority search trees, quadtrees, k-d trees, and R-trees, halfedge and polyhedral surface representations, spatial indexing for multidimensional queries, and kinetic data structures for moving objects.
• Graph representations and structures: adjacency and incidence representations, dynamic connectivity structures, compressed and streaming graph representations, structures supporting shortest path and flow computation, and representations for very large graphs.
• Persistent and functional data structures: partial, full, and confluent persistence, purely functional implementations and amortisation under persistence, versioned and time-travel queries, and persistence in database and file system contexts.
• Concurrent and parallel data structures: lock-based and lock-free designs, linearisability and correctness conditions, memory reclamation and hazard pointers, transactional memory support, contention and scalability under high core counts, and concurrent dictionaries and queues.
• External memory and cache-aware structures: input–output model analysis, cache-oblivious layouts and algorithms, buffer trees and write-optimised structures such as log-structured merge trees, structures for solid state and non-volatile memory, and paging and replacement behaviour.
• Probabilistic and approximate structures: Bloom filters, quotient filters, and variants, count-min and count sketch summaries, HyperLogLog and cardinality estimation, sampling structures for streams, and error bound analysis.
• Analysis of data structures: worst-case, average-case, and amortised analysis, potential function methods, lower bound techniques in the cell probe model, competitive analysis of dynamic structures, and experimental validation of theoretical bounds.
• Implementation and engineering: memory layout and locality optimisation, generic and library design, benchmarking methodology for structures, portability across hardware including GPU and manycore, and reusable data structure library development.