Temporal database management: Temporal registration

Kvet, Marek; Kvet, Michal

doi:10.1109/dt.2017.8024301

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Besides, various lock strategies are discussed, pointing to the lost updates [9], pessimistic [9] [15] and optimistic [9] [15] locking using version column [12] or checksum [16] [19].…”

Section: Lockingmentioning

confidence: 99%

Concept of Select Unlocking Optimization

Kvet¹

2023

IPSI TIR

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Ensuring data integrity stored in a database requires change encapsulation in a transaction. Transaction maintains consistency by shifting the database from one consistent state to another, protected by atomicity, isolation, and durability. This paper focuses on the isolation and parallelism allowing distributed access to the data tuple across multiple transactions. Access to the data tuple is marked by the locks applied on the row level. Locks can be shared expressing data retrieval process or exclusive intended to change the values of the columns. The general locking rule takes the lock before the operation and releases it just after the operation or at the end of the transaction. Oracle database, however, does not release the lock physically, whereas the data block access would be necessary to be reloaded during the transaction approval. Therefore, this paper aims in optimizing the process of unlocking data before the data retrieval process using multiple strategies.

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“…Besides, various lock strategies are discussed, pointing to the lost updates [9], pessimistic [9] [15] and optimistic [9] [15] locking using version column [12] or checksum [16] [19].…”

Section: Lockingmentioning

confidence: 99%

Concept of Select Unlocking Optimization

Kvet¹

2023

IPSI TIR

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“…In paper [12], researchers defined their own proposed temporal architecture concerning element registration in the system. Whereas the data models depicted attributes and temporality definition, evolve, it is necessary to create a complex environment and possibilities for dealing with these changes and reflections to the temporal management layer.…”

Section: Related Workmentioning

confidence: 99%

Various Approaches Proposed for Eliminating Duplicate Data in a System

Čerešňák¹,

Dudáš²

2021

Komunikácie

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The growth of big data processing market led to an increase in the overload of computation data centers, change of methods used in storing the data, communication between the computing units and computational time needed to process or edit the data. Methods of distributed or parallel data processing brought new problems related to computations with data which need to be examined. Unlike the conventional cloud services, a tight connection between the data and the computations is one of the main characteristics of the big data services. The computational tasks can be done only if relevant data are available. Three factors, which influence the speed and efficiency of data processing are - data duplicity, data integrity and data security. We are motivated to study the problems related to the growing time needed for data processing by optimizing these three factors in geographically distributed data centers.

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