Temporal Flower Index Eliminating Impact of High Water Mark

Kvet, Michal

doi:10.1007/978-3-319-93408-2_7

Cited by 5 publications

(17 citation statements)

References 8 publications

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“…The index managed data are immune to physical fragmentation in data files. Performance analysis regarding processing steps can be found in the work of Kvet and Matiaško …”

Section: Query Processingmentioning

confidence: 99%

“…To get the list of indexed columns and the order of attributes in the index, { user | all | dba }_ dict _ columns system table can be queried. Structure of the dictionary with regards on the index definition can be found in detail in the work of Kvet and Matiaško …”

Section: Indexmentioning

confidence: 99%

“…Although indexes are extensive for a complex temporal table, they are not usually backed up, whereas all data are already stored in the database. Index recovery can be done with various techniques; the most often used is based on the SYS owned package DBMS _ METADATA . Default database index type is B‐tree, respectively, B+tree, which is mostly used because it maintains the efficiency despite frequent changes of records ( Insert , Delete , and Update ).…”

Section: Indexmentioning

confidence: 99%

“…The most often used types are bitmap , hash , reverse key , function‐based , or domains . A special category is a cluster . During the processing, the data optimizer chooses the best relevant access method for the defined query, based on provided statistics.…”

Section: Indexmentioning

confidence: 99%

“…In the past, it was formed by the 8 bytes; nowadays, it is covered by 10 bytes (from Oracle 8i version). It consists of the following structure (elements): the data object number (1 to 32 bits), data file in which the row resides (the first file is 1; file number is relative to tablespace) (33 to 44 bits), data block in the data file in which the row resides (45 to 64 bits), and the position of the row in the data block (the first row is 0) (65 to 80 bits). …”

Section: Indexmentioning

confidence: 99%

See 4 more Smart Citations

Locating and accessing large datasets using Flower Index Approach

Kvet

Kršák

2019

Concurrency and Computation

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Summary 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.

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“…The index managed data are immune to physical fragmentation in data files. Performance analysis regarding processing steps can be found in the work of Kvet and Matiaško …”

Section: Query Processingmentioning

confidence: 99%

Section: Indexmentioning

confidence: 99%

Section: Indexmentioning

confidence: 99%

Section: Indexmentioning

confidence: 99%