Subspace Aggregation Query and Index Generation for Multidimensional Resource Space Mode

2025-05-04

AI Summary

To address the inefficiency of subspace aggregate queries and slow non-empty point localization in multidimensional categorical resource spaces, this paper proposes an efficient query framework that synergistically integrates a partial-order coordinate tree with a graph index. Methodologically: (1) a coordinate tree models the partial-order range of subspaces, enabling path-level aggregation, metric computation, and ordering; (2) a graph index construction algorithm is designed, incorporating intersection linking, probability-driven optimization, cross-dimensional load balancing, and sibling short-linking mechanisms. The key contribution lies in the first deep coupling of partial-order structures with graph indexing, which significantly reduces index size. Experimental results demonstrate a 10×–100× speedup in non-empty point localization and substantial reduction in aggregate query latency, effectively enabling real-time analytics over large-scale multidimensional categorical data.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Organizing resources in a multidimensional classification space is an approach to efficiently managing and querying large-scale resources. This paper defines an aggregation query on subspace defined by a range on the partial order on coordinate tree at each dimension, where each point contains resources aggregated along the paths of partial order relations on the points so that aggregated resources at each point within the subspace can be measured, ranked and selected. To efficiently locate non-empty points in a large subspace, an approach to generating graph index is proposed to build inclusion links with partial order relations on coordinates of dimensions to enable a subspace query to reach non-empty points by following indexing links and aggregate resources along indexing paths back to their super points. Generating such an index is costly as the number of children of an index node can be very large so that the total number of indexing nodes is unbounded. The proposed approach adopts the following strategies to reduce the cost: (1) adding intersection links between two indexing nodes, which can better reduce query processing costs while controlling the number of nodes of the graph index; (2) intersection links are added between two nodes according to the probabilistic distribution calculated for estimating the costs of adding intersection between two nodes; (3) coordinates at one dimension having more resources are split by coordinates at another dimension to balance the number of resources hold by indexing nodes; and, (4) short-cut links are added between sibling coordinates of coordinate trees to make an efficient query on linear order coordinates. Analysis and experiments verified the effectiveness of the generated index in supporting subspace aggregation query. This work makes significant contributions to the development of data model based on multi-dimensional classification.

Problem

• Efficiently query aggregated resources in multidimensional subspace
• Generate graph index for locating non-empty points quickly
• Reduce index generation cost with optimized linking strategies

Innovation

• Subspace aggregation query using partial order ranges
• Graph index with inclusion and intersection links
• Resource balancing via coordinate splitting