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Locating and accessing large datasets using Flower Index Approach

机译:使用花卉索引方法定位和访问大型数据集

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Information system core part is just the data stored in the database. Over the decades, the number and structure of the data have been changed. Nowadays, data must reflect not only current valid data but also historical and future images as well. Each data tuple is therefore delimited by the validity timeframe forming a temporal paradigm. Several temporal models have been developed with an emphasis on the data structure, the frequency of changes, and synchronization processes. Although the system stores time delimited data during the object lifecycle, it is not efficient, even useful to store data in the main system indefinitely. Reliability is another significant aspect of the processing covered by the purging processes. Query processing is based on the accessing data in the memory buffer cache of the database instance preceded by the loading process from the physical database. This paper proposes a Flower Index Approach as the main contribution. It removes the impact of the High Water Mark, removes useless block loading with no relevant data, and provides effective data access stream using a specific index. Full Table Scan is then not used and data are accessed directly using index ROWID locators.
机译:信息系统核心部分只是存储在数据库中的数据。在几十年中,数据的数量和结构已更改。如今,数据必须不仅反映当前的有效数据,还反映历史和未来的图像。因此,每个数据元组由形成时间范式的有效时间帧限定。已经开发了几种时间模型,并强调数据结构,变化频率和同步过程。虽然系统在对象生命周期期间存储时间分隔数据,但它不高效,甚至是无限期地存储主系统中的数据。可靠性是吹扫过程所涵盖的处理的另一个重要方面。查询处理基于来自物理数据库的加载进程之前的数据库实例的内存缓冲区中的访问数据。本文提出了一种作为主要贡献的花卉指数方法。它消除了高水位标记的影响,除去无用的块加载,没有相关数据,并使用特定索引提供有效的数据访问流。然后,不使用完整表扫描,并使用索引RowID定位器直接访问数据。

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