A neural-based concurrency control algorithm for database systems

Sheikhan, Mansour; Rohani, Mohsen; Ahmadluei, Saeed

doi:10.1007/s00521-011-0691-6

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…But executing transactions concurrently without control will cause problems like deadlock and inconsistency. A number of concurrency control algorithms (CC for short) [1][2][3][4][5] have been proposed to solve this kind of problem, which can guarantee the consistency and integrity of database while executing transactions.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Decision Concurrency Control Algorithm

Chen

Zhuang

2014

AMR

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For the lack of adaptability about the existing concurrency control algorithms, adaptive decision concurrency control algorithm is proposed. ADCC algorithm divides concurrency control process into two phases in: execution authorizing phase and strategy selecting phase. In execution authorizing phase, algorithm compares statistics and effectiveness of transactions to determine the execution order of conflict transactions. In strategy selecting phase, according to transactions’ read/write status and current conflict rate, algorithm selects optimistic/pessimistic conflict resolution strategy adaptively. Such selection mechanism makes ADCC algorithm have high efficiency no matter database system is busy or idle. Simulation experiment proves that ADCC algorithm this paper proposed is superior to classical strict two phases locking algorithm and hybrid concurrency control. So ADCC algorithm performs well in the period of concurrency control.

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Section: Introductionmentioning

confidence: 99%

“…Mansour Sheikhan, Mohsen Rohani et al proposed an algorithm based on neural network: NCC algorithm [3]. The algorithm classified and simulated transactions through ART-2 neural network model.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Decision Concurrency Control Algorithm

Chen

Zhuang

2014

AMR

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Concurrency control for real‐time and mobile transactions: Historical view, challenges, and evolution of practices

Chaudhry

Yousaf

2021

Concurrency and Computation

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Recently, mobile computing has changed the way that spatial data and GIS are processed. Unlike wired and stand-alone GIS, now the trend has been switched from offline to real-time data processing using location aware services, such as GPS technology. The increased usage of location aware services in multiuser real-time environment has made transaction management incredibly significant. If the simultaneous query operations on the same data item are not handled intelligently then this results in data inconsistency issue. Concurrency control protocol is one of the primary aspects that helps in overcoming this issue a in multiuser environment. To the best of our knowledge, the impact of technological advancements on concurrency control has not been thoroughly studied in the literature. In this article, we explored the literature on concurrency control algorithms in depth with respect to real-time applications and the applications with moving objects. We defined a taxonomy of concurrency control solutions and assessed the maturity of these solutions in the light of characteristics of real-time and mobile environment. We compared the most recent developments made in the literature and presented meaningful insights. Challenges are also identified and discussed, which can assist in doing research in this domain in future. K E Y W O R D S concurrency control, mobile, real time 1 INTRODUCTION IoT (Internet of Things) emerged as a promising technology that has revolutionized the way of living. GPS-based devices and sensors are commonly used in majority of the applications. The convergence of multiple domains such as real-time GIS, mobile computing, and IoT has made it necessary to solve the computing problem considering the requirements of each domain. Mobile GIS domain integrates several technologies: GPS, mobile computing, and GIS. In recent times, the data processing environment deals with varying kinds of sensors. The real-time data collected by sensors need to be processed in timely manner to ensure efficient data management. The sensors can be categorized into two types depending on their mobility: fixed and agile/mobile sensors. Fixed sensors are spatially fixed, whereas mobile sensors change their location with respect to time. Mobile sensors collect data on geographical grounds and this data can be referred to as spatiotemporal data. This has given rise to a new research domain of real-time mobile data processing and management. Thus, real-time processing domain emerged with respect to time depending on the nature of sensor technology and evolution of IoT. The future processing systems are expected to meet the requirements of spatiotemporal and real-time data, leading toward a new domain of real-time GIS. Real-time data can be used for both static and dynamic processing. For example, in the first aspect, mobile sensors can process and analyze the real-time spatiotemporal data for dynamic processing. On the other hand, real-time data can be collected and saved in a log for future offline analysis (static processing).

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An intelligent hybrid optimistic/pessimistic concurrency control algorithm for centralized database systems using modified GSA-optimized ART neural model

Sheikhan

Ahmadluei

2012

Neural Comput & Applic

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A neural-based concurrency control algorithm for database systems

Cited by 3 publications

References 67 publications

An Adaptive Decision Concurrency Control Algorithm

An Adaptive Decision Concurrency Control Algorithm

Concurrency control for real‐time and mobile transactions: Historical view, challenges, and evolution of practices

An intelligent hybrid optimistic/pessimistic concurrency control algorithm for centralized database systems using modified GSA-optimized ART neural model

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