Continuous Query for Top-$K$ Maximal Sum Intervals over Streaming Data

2026-07-12

AI Summary

This work addresses the inefficiency of existing approaches for continuously querying the top-k maximum-sum subarrays within a sliding window over data streams, which often suffer from high enumeration costs or reliance on frequent index reconstruction, hindering their applicability in streaming scenarios. To overcome these limitations, the paper proposes a novel partition-based strategy that guarantees each maximum-sum subarray is entirely contained within a single partition, thereby enabling safe pruning and parallel processing. The approach integrates partition construction, incremental maintenance, and an efficient intra-partition top-k search algorithm to support real-time, incremental updates on streaming data. Experimental results on both real-world and synthetic datasets demonstrate that the proposed method significantly outperforms state-of-the-art algorithms, making it well-suited for real-time applications such as IoT stream processing.

Abstract

The continuous identification of top-$k$ maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small $k$ values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, enabling independent and parallel processing. This design provides two key advantages: it enables safe pruning of partitions that cannot contribute to top-$k$ results, drastically narrowing the search space, and it enables efficient, incremental maintenance of the maximal sum intervals in each partition. We develop algorithms for partition construction, incremental partition updates, and partition-based top-$k$ maximal sum interval search. Extensive experiments on real and synthetic datasets demonstrate that our approach significantly improves efficiency.

Problem

• streaming data
• top-k
• maximal sum intervals
• sliding window
• continuous query

Innovation

• partition-based strategy
• maximal sum interval
• streaming data
• top-k query
• incremental maintenance