arXiv subjects

Yoshiharu Ishikawa

Publications and source records attributed to Yoshiharu Ishikawa.

10 recordsLinked to original sources

arXiv2026

Approximate Nearest Neighbor Search for Modern AI: A Projection-Augmented Graph Approach

Approximate Nearest Neighbor Search (ANNS) is fundamental to modern AI applications. Most existing solutions optimize query efficiency but fail to align with the practical requirements of modern workloads. In this paper, we outline six critical demands of modern AI applications: high query efficiency, fast indexing, low memory footprint, scalability to high dimensionality, robustness across varying retrieval sizes, and support for online insertions. To satisfy all these demands, we introduce Projection-Augmented Graph (PAG), a new ANNS framework that integrates projection techniques into a graph index. PAG reduces unnecessary exact distance computations through asymmetric comparisons between exact and approximate distances as guided by projection-based statistical tests. Three key components are designed and integrated into the graph index to optimize indexing and searching. Experiments on six modern datasets demonstrate that PAG consistently achieves superior queries per second (QPS)-recall performance -- up to 5x faster than HNSW -- while offering fast indexing speed and moderate memory footprint. PAG remains robust as dimensionality and retrieval size increase and naturally supports online insertions.

cs.IR↗

arXiv2026

Efficient and Robust Lock-Free Multi-Word Compare-and-Swap via Contention-Aware Helping

Efficient concurrent access to shared memory remains a central focus for researchers seeking to enhance data structure performance. Lock-based synchronization often limits scalability and introduces liveness issues such as deadlocks. In contrast, implementing non-blocking structures with single-word compare-and-swap (CAS) instructions increases algorithmic complexity because of unavoidable intermediate states. Multi-word compare-and-swap (MCAS) operations offer a practical primitive for atomically updating multiple discrete memory locations, thereby addressing these challenges. However, under high contention, helping mechanisms designed to guarantee lock-freedom may cause excessive cache invalidations and significant performance degradation. Furthermore, existing approaches are vulnerable to the ABA problem. Current lock-free MCAS algorithms may duplicate the execution of the same operation, leading to inconsistent states in certain edge cases. To address these challenges, this paper introduces a new lock-free MCAS algorithm that achieves both efficiency and consistency. First, we propose a contention-aware helping mechanism that dynamically regulates the number of concurrent helpers through exponential backoff and embedded entry counters. These counters also enable a fast garbage-collection path, significantly reducing memory management overhead. Second, we introduce a version embedding approach to suppress the ABA problem during MCAS operations. Although version embedding requires several bits per target memory region to store version information, embedded versions allow helpers to avoid duplicated MCAS executions. Experimental results show that the proposed method achieves up to three times the throughput of the state-of-the-art lock-free MCAS algorithm. Moreover, the results indicate that version embedding is sufficient to prevent the ABA problem in practical scenarios.

cs.DB↗

arXiv2026

Probabilistic Kernel Function for Fast Angle Testing

In this paper, we study the angle testing problem in the context of similarity search in high-dimensional Euclidean spaces and propose two projection-based probabilistic kernel functions, one designed for angle comparison and the other for angle thresholding. Unlike existing approaches that rely on random projection vectors drawn from Gaussian distributions, our approach leverages reference angles and adopts a deterministic structure for the projection vectors. Notably, our kernel functions do not require asymptotic assumptions, such as the number of projection vectors tending to infinity, and can be theoretically and experimentally shown to outperform Gaussian-distribution-based kernel functions. We apply the proposed kernel function to Approximate Nearest Neighbor Search (ANNS) and demonstrate that our approach achieves a 2.5x--3x higher query-per-second (QPS) throughput compared to the widely-used graph-based search algorithm HNSW.

cs.LG↗

arXiv2024

Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search

Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal challenge in the field of machine learning. In recent years, graph-based methods have emerged as the superior approach to ANNS, establishing a new state of the art. Although various optimizations for graph-based ANNS have been introduced, they predominantly rely on heuristic methods that lack formal theoretical backing. This paper aims to enhance routing within graph-based ANNS by introducing a method that offers a probabilistic guarantee when exploring a node's neighbors in the graph. We formulate the problem as probabilistic routing and develop two baseline strategies by incorporating locality-sensitive techniques. Subsequently, we introduce PEOs, a novel approach that efficiently identifies which neighbors in the graph should be considered for exact distance calculation, thus significantly improving efficiency in practice. Our experiments demonstrate that equipping PEOs can increase throughput on commonly utilized graph indexes (HNSW and NSSG) by a factor of 1.6 to 2.5, and its efficiency consistently outperforms the leading-edge routing technique by 1.1 to 1.4 times.

cs.LG↗

arXiv2024

Practical Persistent Multi-Word Compare-and-Swap Algorithms for Many-Core CPUs

In the last decade, academic and industrial researchers have focused on persistent memory because of the development of the first practical product, Intel Optane. One of the main challenges of persistent memory programming is to guarantee consistent durability over separate memory addresses, and Wang et al. proposed a persistent multi-word compare-and-swap (PMwCAS) algorithm to solve this problem. However, their algorithm contains redundant compare-and-swap (CAS) and cache flush instructions and does not achieve sufficient performance on many-core CPUs. This paper proposes a new algorithm to improve performance on many-core CPUs by removing useless CAS/flush instructions from PMwCAS operations. We also exclude dirty flags, which help ensure consistent durability in the original algorithm, from our algorithm using PMwCAS descriptors as write-ahead logs. Experimental results show that the proposed method is up to ten times faster than the original algorithm and suggests several productive uses of PMwCAS operations.

cs.DB↗

arXiv2023

Z-ordered Range Refinement for Multi-dimensional Range Queries

The z-order curve is a space-filling curve and is now attracting the interest of developers because of its simple and useful features. In the case of key-value stores, because the z-order curve achieves multi-dimensional range queries in one-dimensional z-ordered space, its use has been proposed for both academic and industrial purposes. However, z-ordered range queries suffer from wasteful query regions due to the properties of the z-order curve. Although previous studies have proposed refining z-ordered ranges, doing so is computationally expensive. In this paper, we propose z-ordered range refinement based on jump-in/out algorithms, and then we approximate z-ordered query regions to achieve efficient range refinement. Because the proposed method is lightweight and pluggable, it can be applied to various databases. We implemented our approach using PL/pgSQL in PostgreSQL and evaluated the performance of range refinement and multi-dimensional range queries. The experimental results demonstrate the effectiveness and efficiency of the proposed method.

cs.DB↗

arXiv2021

Consistent and Flexible Selectivity Estimation for High-Dimensional Data

Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion. Answering this problem accurately and efficiently is essential to many applications, such as density estimation, outlier detection, query optimization, and data integration. The estimation problem is especially challenging for large-scale high-dimensional data due to the curse of dimensionality, the large variance of selectivity across different queries, and the need to make the estimator consistent (i.e., the selectivity is non-decreasing in the threshold). We propose a new deep learning-based model that learns a query-dependent piecewise linear function as selectivity estimator, which is flexible to fit the selectivity curve of any distance function and query object, while guaranteeing that the output is non-decreasing in the threshold. To improve the accuracy for large datasets, we propose to partition the dataset into multiple disjoint subsets and build a local model on each of them. We perform experiments on real datasets and show that the proposed model consistently outperforms state-of-the-art models in accuracy in an efficient way and is useful for real applications.

cs.DB↗

arXiv2020

Sequenced Route Query with Semantic Hierarchy

The trip planning query searches for preferred routes starting from a given point through multiple Point-of-Interests (PoI) that match user requirements. Although previous studies have investigated trip planning queries, they lack flexibility for finding routes because all of them output routes that strictly match user requirements. We study trip planning queries that output multiple routes in a flexible manner. We propose a new type of query called skyline sequenced route (SkySR) query, which searches for all preferred sequenced routes to users by extending the shortest route search with the semantic similarity of PoIs in the route. Flexibility is achieved by the {\it semantic hierarchy} of the PoI category. We propose an efficient algorithm for the SkySR query, bulk SkySR algorithm that simultaneously searches for sequenced routes and prunes unnecessary routes effectively. Experimental evaluations show that the proposed approach significantly outperforms the existing approaches in terms of response time (up to four orders of magnitude). Moreover, we develop a prototype service that uses the SkySR query, and conduct a user test to evaluate its usefulness.

cs.DB↗

arXiv2020

Fast Subtrajectory Similarity Search in Road Networks under Weighted Edit Distance Constraints

In this paper, we address a similarity search problem for spatial trajectories in road networks. In particular, we focus on the subtrajectory similarity search problem, which involves finding in a database the subtrajectories similar to a query trajectory. A key feature of our approach is that we do not focus on a specific similarity function; instead, we consider weighted edit distance (WED), a class of similarity functions which allows user-defined cost functions and hence includes several important similarity functions such as EDR and ERP. We model trajectories as strings, and propose a generic solution which is able to deal with any similarity function belonging to the class of WED. By employing the filter-and-verify strategy, we introduce subsequence filtering to efficiently prunes trajectories and find candidates. In order to choose a proper subsequence to optimize the candidate number, we model the choice as a discrete optimization problem (NP-hard) and compute it using a 2-approximation algorithm. To verify candidates, we design bidirectional tries, with which the verification starts from promising positions and leverage the shared segments of trajectories and the sparsity of road networks for speed-up. Experiments are conducted on large datasets to demonstrate the effectiveness of WED and the efficiency of our method for various similarity functions under WED.

cs.DB↗

arXiv2017

CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling

In this paper, we present a compressed data structure for moving object trajectories in a road network, which are represented as sequences of road edges. Unlike existing compression methods for trajectories in a network, our method supports pattern matching and decompression from an arbitrary position while retaining a high compressibility with theoretical guarantees. Specifically, our method is based on FM-index, a fast and compact data structure for pattern matching. To enhance the compression, we incorporate the sparsity of road networks into the data structure. In particular, we present the novel concepts of relative movement labeling and PseudoRank, each contributing to significant reductions in data size and query processing time. Our theoretical analysis and experimental studies reveal the advantages of our proposed method as compared to existing trajectory compression methods and FM-index variants.

cs.DS↗