Coconut: sortable summarizations for scalable indexes over static and streaming data series
Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos, Themis Palpanas

TL;DR
Coconut introduces a novel sortable summarization technique for data series indexing, enabling scalable, efficient similarity search over massive static and streaming datasets by leveraging sorted, z-order based organization.
Contribution
It presents the first sortable summarization for data series, enabling scalable indexing and querying of streaming data with improved performance and storage efficiency.
Findings
Coconut outperforms existing indexes in construction speed.
Coconut achieves faster query response times.
Coconut reduces storage costs significantly.
Abstract
Many modern applications produce massive streams of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data series indexes that are used for this purpose do not scale well for massive datasets in terms of performance, or storage costs. We pinpoint the problem to the fact that existing summarizations of data series used for indexing cannot be sorted while keeping similar data series close to each other in the sorted order. To address this problem, we present Coconut, the first data series index based on sortable summarizations and the first efficient solution for indexing and querying streaming series. The first innovation in Coconut is an inverted, sortable data series summarization that organizes data series based on a z-order curve, keeping similar series close to each other in the sorted order. As a result, Coconut is…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Data Management and Algorithms · Music and Audio Processing
