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Spatiotemporal Data Indexing using hB π - tree

This paper presents the hBπ-tree data structure, designed for efficient indexing of spatiotemporal data that encompasses both spatial and temporal attributes. Developed by Evangelos Kanoulas and Georgios Evangelidis from the Department of Applied Informatics at the University of Macedonia, this framework addresses the challenges posed by transaction time databases, enabling support for past, current, and future data. The hBπ-tree offers innovative techniques for managing time-variant and time-invariant attributes, along with a novel D/fp algorithm for effective data node and index node manipulation.

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Spatiotemporal Data Indexing using hB π - tree

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  1. Spatiotemporal Data Indexing using hBπ-tree Evangelos Kanoulas, Georgios Evangelidis Department of Applied Informatics, University of Macedonia, Hellas

  2. Spatialand Temporal Data • Linear data • Spatial data • Temporal data Applications, which require to support past, current and future data. Kanoulas - Evangelidis

  3. Transaction Time Databases • transaction time • records (tuples) • time-invariant key • time – variant attributes • time interval • valid records Kanoulas - Evangelidis

  4. TSB-tree 1 - structure Kanoulas - Evangelidis

  5. TSB-tree 3 – splitting Kanoulas - Evangelidis

  6. hBπ– tree (Data nodes) Kanoulas - Evangelidis

  7. hBπ– tree (Index nodes) Kanoulas - Evangelidis

  8. TShB-tree – transaction time Data Records – an example Kanoulas - Evangelidis

  9. TShB-tree - splitting • Index nodes • Using the D/fp algorithm • Data nodes • Time split • Key split Kanoulas - Evangelidis

  10. D/fp – Τ/Κ (1) Kanoulas - Evangelidis

  11. D/fp – Τ/Κ (2) Kanoulas - Evangelidis

  12. D/fp – Τ/ΤΚ (1) Kanoulas - Evangelidis

  13. D/fp – Τ/ΤΚ (2) Kanoulas - Evangelidis

  14. Kanoulas - Evangelidis

  15. Kanoulas - Evangelidis

  16. Kanoulas - Evangelidis

  17. Kanoulas - Evangelidis

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