arXiv subjects

Dimitrios Tsitsigkos

Publications and source records attributed to Dimitrios Tsitsigkos.

6 recordsLinked to original sources

arXiv2026

In-memory Multidimensional Indexing Using the skd-tree

In this paper, we revisit the problem of indexing multi-dimensional data in memory for the efficient support of multi-dimensional range queries and nearest neighbor queries. This is a classic problem in main-memory databases, where there is a need for indexing multiple columns simultaneously. Established data structures include the R-tree, kd-tree, quad-tree, and grid-based partitioning. More recently, multi-dimensional learned indexes have also been proposed to address this problem. We propose slicing kd-tree (skd-tree), a variant of the kd-tree, where each node partitions the space of its subtree into multiple slices across a single splitting dimension. By compressing the splitters of the partitions and with the help of data-parallelism, we (i) radically reduce the number of levels of the tree and (ii) limit the number of computations required for multi-dimensional range and proximity queries. The nodes of the skd-tree resemble the nodes of a main-memory B+-tree, however, a different dimension is used at each level. Our novel range and kNN algorithms on the skd-tree apply only a small constant number of SIMD instructions at each node during tree traversal. Our contributions also include a novel top-down construction algorithm, different types of inner and leaf nodes that warrant tree balancing, and a novel update algorithm. Our skd-tree achieves strong performance compared to existing methods, according to our experimental evaluation on real and synthetic datasets.

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arXiv2026

CRISP: Correlation-Resilient Indexing via Subspace Partitioning

As the dimensionality of modern learned representations increases to thousands of dimensions, the state-of-the-art Approximate Nearest Neighbor (ANN) indices exhibit severe limitations. Graph-based methods (e.g., HNSW) suffer from prohibitive memory consumption and routing degradation, while recent randomized quantization and learned rotation approaches (e.g., RaBitQ, OPQ) impose significant preprocessing overheads. We introduce CRISP, a novel framework designed for ANN search in very-high-dimensional spaces. Unlike rigid pipelines that apply expensive orthogonal rotations indiscriminately, CRISP employs a lightweight, correlation- aware adaptive strategy that redistributes variance only when necessary, effectively reducing the preprocessing complexity. We couple this adaptive mechanism with a cache-coherent Compressed Sparse Row (CSR) index structure. Furthermore, CRISP incorporates a multi-stage dual-mode query engine: a Guaranteed Mode that preserves rigorous theoretical lower bounds on recall, and an Optimized Mode that leverages rank-based weighted scoring and early termination to reduce query latency. Extensive evaluation on datasets of very high dimensionality (up to 4096) demonstrates that CRISP achieves state-of-the-art query throughput, low construction costs, and peak memory efficiency.

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arXiv2025

BS-tree: A gapped data-parallel B-tree

We propose BS-tree, an in-memory implementation of the B+-tree that adopts the structure of the disk-based index (i.e., a balanced, multiway tree), setting the node size to a memory block that can be processed fast and in parallel using SIMD instructions. A novel feature of the BS-tree is that it enables gaps (unused positions) within nodes by duplicating key values. This allows (i) branchless SIMD search within each node, and (ii) branchless update operations in nodes without key shifting. We implement a frame of reference (FOR) compression mechanism, which allows nodes to have varying capacities, and can greatly decrease the memory footprint of BS-tree. We compare our approach to existing main-memory indices and learned indices under different workloads of queries and updates and demonstrate its robustness and superiority compared to previous work in single- and multi-threaded processing.

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arXiv2023

Two-layer Space-oriented Partitioning for Non-point Data

Non-point spatial objects (e.g., polygons, linestrings, etc.) are ubiquitous. We study the problem of indexing non-point objects in memory for range queries and spatial intersection joins. We propose a secondary partitioning technique for space-oriented partitioning indices (e.g., grids), which improves their performance significantly, by avoiding the generation and elimination of duplicate results. Our approach is easy to implement and can be used by any space-partitioning index to significantly reduce the cost of range queries and intersection joins. In addition, the secondary partitions can be processed independently, which makes our method appropriate for distributed and parallel indexing. Experiments on real datasets confirm the advantage of our approach against alternative duplicate elimination techniques and data-oriented state-of-the-art spatial indices. We also show that our partitioning technique, paired with optimized partition-to-partition join algorithms, typically reduces the cost of spatial joins by around 50%.

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arXiv2021

A Two-level Spatial In-Memory Index

Very large volumes of spatial data increasingly become available and demand effective management. While there has been decades of research on spatial data management, few works consider the current state of commodity hardware, having relatively large memory and the ability of parallel multi-core processing. In this work, we re-consider the design of spatial indexing under this new reality. Specifically, we propose a main-memory indexing approach for objects with spatial extent, which is based on a classic regular space partitioning into disjoint tiles. The novelty of our index is that the contents of each tile are further partitioned into four classes. This second-level partitioning not only reduces the number of comparisons required to compute the results, but also avoids the generation and elimination of duplicate results, which is an inherent problem of spatial indexes based on disjoint space partitioning. The spatial partitions defined by our indexing scheme are totally independent, facilitating effortless parallel evaluation, as no synchronization or communication between the partitions is necessary. We show how our index can be used to efficiently process spatial range queries and drastically reduce the cost of the refinement step of the queries. In addition, we study the efficient processing of numerous range queries in batch and in parallel. Extensive experiments on real datasets confirm the efficiency of our approaches.

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arXiv2019

Parallel In-Memory Evaluation of Spatial Joins

The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. As main memories become bigger and faster and commodity hardware supports parallel processing, there is a need to revamp classic join algorithms which have been designed for I/O-bound processing. In view of this, we study the in-memory and parallel evaluation of spatial joins, by re-designing a classic partitioning-based algorithm to consider alternative approaches for space partitioning. Our study shows that, compared to a straightforward implementation of the algorithm, our tuning can improve performance significantly. We also show how to select appropriate partitioning parameters based on data statistics, in order to tune the algorithm for the given join inputs. Our parallel implementation scales gracefully with the number of threads reducing the cost of the join to at most one second even for join inputs with tens of millions of rectangles.

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