GMTDS Algorithm for Dynamic Management of Transaction under Different Workload Condition: A Novel Approach | Chapter 8 | Research Updates in Mathematics and Computer Science Vol. 1

 This paper highlights a Novel GMTDS Algorithm for Dynamic Management of Transaction under Different Workload Condition. In today’s scenario large enterprise world spread across different locations, continents or having a diverse presence over the globe, where data is enormous and handling such large data over distributed computing becomes critical in real-time database system. Transaction processing ensures that related data is added to or deleted from the database simultaneously, thus preserving data integrity in your application. In transaction processing, data is not written to the database until a commit command is issued. Ensuring that the sequence of updates in the stable warehouses at various locations is safely confirmed or canceled as a single full item of work is a critical task for the distributed environment's transaction management system. Working with Real-Time Database System (RTDBS) and that to on a distributed computing system is a tough task. When we work with distributed environment over larger database, we need to take care of the transaction time period as well as number of transactions that are actually executed (committed) and number of transactions fail. The application on dynamic RTDBS becomes more complex when certain deadlines need to be completed. In this paper, we had carried out the test of CRUD (Create, Read, Update, and Delete) operation on transactional Real-time databases in real time dynamic distributed environment by using the existing EDF, GEDF algorithm and we had compared these algorithms with our proposed GMTDS (Generic Multi-dimensional Transaction Management under Dynamic Settings) algorithm in standalone and distributed environment with a dynamic self adaptive approach for management of transactions.

 

To validate the efficacy of the GMTDS algorithm, comprehensive simulations were conducted under various workload scenarios. Comparative analysis against existing transaction management algorithms showcase the advantages and improvements offered by GMTDS in terms of response times, throughput, and adaptability.

Author(s) Details:

Mohammad Sharfoddin Khatib,
Computer Science and Engineering Department, Anjuman, College of Engineering and Technology, Sadar, Nagpur-440001, India.

Mohammad Atique,
P.G Department of Computer Science and Engineering, Director, UGC –MMTC (Formerly HRDC) S. G. B. Amravati University, Amravati, India.

Sayyed Qudsiya Naaz,
Computer Science and Engineering Department, Anjuman, College of Engineering and Technology, Sadar, Nagpur-440001, India.

Please see the link here: https://stm.bookpi.org/RUMCS-V1/article/view/13675

Determination of Trees Predictive Models for Surface Roughness in High-Speed Machining (HSP): A Study in Steel and Aluminum Metalworking Industry

 The present study climaxes a surface roughness (Ra) guess model that considers a subset of elements complicated in the milling process namely related to the build piece, the tool, and traits of the machine tool. Due to the excellent results it produces in agreements of surface finish and financial benefits, high-speed produce (HSP) continues to be a method of great interest in the result of metal parts. The manufacturing has a propensity to use data administration and analysis arrangements to generate dossier that can be used to raise the results of machining for Ra. In this work, we use real preparation data and we have more obtained a graphical likeness of knowledge using classic resolution trees to complement the results got by GBT, in this way the joint result supports greater graphic eloquence regarding dependent influences and the values of the prophet variables on the class labels than for example Bayesian networks. The results are differred with prior happenings that use the same exploratory design but with various soft-computing methods and they are also compared with the results of analogous previous works.

Author(s) Details:

Victor Flores,
Department of Computing & Systems Engineering, Universidad Católica del Norte, Angamos Av. 0610, Antofagasta, Chile.

Please see the link here: https://stm.bookpi.org/RHMCS-V4/article/view/9158

Determination of Trees Predictive Models for Surface Roughness in High-Speed Machining (HSP): A Study in Steel and Aluminum Metalworking Industry| Chapter 4 | Research Highlights in Mathematics and Computer Science Vol. 4

 The present study climaxes a surface roughness (Ra) indicator model that considers a subset of elements complicated in the milling process namely related to the build piece, the tool, and traits of the machine tool. Due to the excellent results it produces in conditions of surface finish and financial benefits, high-speed produce (HSP) continues to be a method of great interest in the result of metal parts. The manufacturing has a propensity to use data administration and analysis means to generate dossier that can be used to upgrade the results of machining for Ra. In this work, we use real preparation data and we have more obtained a graphical likeness of knowledge using classic resolution trees to complement the results acquired by GBT, in this way the joint result supplies greater graphic eloquence regarding dependent influences and the values of the prophet variables on the class labels than for example Bayesian networks. The results are compared with prior happenings that use the same exploratory design but with various soft-computing methods and they are also differred with the results of related previous works.

Author(s) Details:

Victor Flores,
Department of Computing & Systems Engineering, Universidad Católica del Norte, Angamos Av. 0610, Antofagasta, Chile.

Please see the link here: https://stm.bookpi.org/RHMCS-V4/article/view/9158