The Today’s digital world computations are tremendously difficult and always demands for essential requirements to significantly process and store enormous size of datasets for wide variety of applications. Since the volume of digital world data is enormous, this is mostly generated unstructured data with more velocity at beyond the limits and double day by day. In last decade, many organizations have been facing major problems to handling and process massive chunks of data, which could not be processed efficiently due to lack of enhancements on existing and conventional technologies. In this paper address, how to overcome these problems as efficiently by using the most recent and world primary powerful data processing tool, which is hadoop clean open source and one of the core component called Map Reduce, but which has few performance issues. This paper main goal is address and overcome the limitations and weaknesses of Map Reduce with Apache Spark.

Although lot Streaming scheduling is an active research field, lot Streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot Streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreover, a novel artificial bee colony optimization is developed. This algorithm utilizes five effective mechanisms to solve the problem. To evaluate the algorithm, it is compared with adaptation of four available algorithms. The statistical analyses showed that the proposed algorithm significantly outperformed the other tested algorithms.

The nanofluid/air interface is practically used in enhancing heat transfer efficiency in thermal management systems, such as in cooling electronics and improving the performance of solar collectors. Additionally, it finds applications in advanced manufacturing processes and biomedical devices, where precise temperature control is crucial. The study investigates the instability of the interface between a Newtonian nanofluid and air in a rectangular setup. This instability arises when the two fluids flow at different velocities, leading to Kelvin-Helmholtz instability at the interface. The air is treated as a viscous, incompressible fluid due to its low Mach number, positioned above the nanofluid. The stability of the interface is determined based on the relative velocity of the fluid layers. The study reveals that various flow parameters, including viscosity ratio, density ratio, volume fraction, and nanoparticle diameter, influence the stability or instability of the interface. Four types of nanofluids are considered, and a comparative analysis is conducted. Interestingly, the nanofluid/air system is found to be more stable compared to the viscous liquid/air system. Sensitivity analysis is performed to examine the impact of different physical variables and their interactions on the critical relative velocity. It is observed that the critical velocity consistently exhibits positive sensitivity to the density ratio. Moreover, the magnitude of critical velocity sensitivities for the density ratio remains constant across all cases. The critical velocity demonstrates the highest positive sensitivity with respect to the parameter of air thickness, with this maximum sensitivity occurring when the air thickness equals 1 and the densities of both fluids are identical.

The classification of fuzzy data is considered as the most challenging areas of data analysis and the complexity of the procedures has been obstacle to the development of new methods for fuzzy data analysis. However, there are significant advances in modeling systems in which fuzzy data are available in the field of mathematical programming. In order to exploit the results of the researches on fuzzy mathematical programming, in this study, a new fuzzy data classification method based on data envelopment analysis (DEA) is provided when fuzzy data are imported as a stream. The proposed method can classify data that changes are created in their behavioral pattern over time using updating the criteria of predicting fuzzy data class. To reduce computational time, fuzzy self-organizing map (SOM) is used to compress incoming data. The new method was tested by simulated data and the results indicated the feasibility of this technique in the face of uncertain and variable conditions.