Big Data Process Advancement

2017 ◽  
pp. 379-396
Author(s):  
Roman Jasek ◽  
Said Krayem ◽  
Petr Zacek
Keyword(s):  
Big Data ◽  
Data Process

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals A Review on Big Data Management and Decision-Making in Smart Grid

2019 ◽  
Vol 4 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Amira Mohamed ◽  
Shady S. Refaat ◽  
Haitham Abu-Rub
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Smart Grid ◽  
Energy Security ◽  
Energy Use ◽  
Essential Element ◽  
Existing Problems ◽  
Data Process ◽  
Equipment Failures ◽  
And Control

AbstractSmart grid (SG) is the solution to solve existing problems of energy security from generation to utilization. Examples of such problems are disruptions in the electric grid and disturbances in the transmission. SG is a premium source of Big Data. The data should be processed to reveal hidden patterns and secret correlations to extrapolate the needed values. Such useful information obtained by the so-called data analytics is an essential element for energy management and control decision towards improving energy security, efficiency, and decreasing costs of energy use. For that reason, different techniques have been developed to process Big Data. This paper presents an overview of these techniques and discusses their advantages and challenges. The contribution of this paper is building a recommender system using different techniques to overcome the most obstacles encountering the Big Data processes in SG. The proposed system achieves the goals of the future SG by (i) analyzing data and executing values as accurately as possible, (ii) helping in decision-making to improve the efficiency of the grid, (iii) reducing cost and time, (iv) managing operating parameters, (v) allowing predicting and preventing equipment failures, and (vi) increasing customer satisfaction. Big Data process enables benefits that were never achieved for the SG application.

scholarly journals Detecting Attacks Using Big Data with Process Mining

2017 ◽  
Vol 2 (2) ◽  
pp. 5 ◽  
Author(s):  
Ved Prakash Mishra ◽  
Yogeshwaran Sivasubramanian ◽  
Subheshree Jeevanandham
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Process Mining ◽  
Detection System ◽  
Big Data Analytics ◽  
High Volume ◽  
False Alarms ◽  
Digital World ◽  
Data Process ◽  
System Logs

Abstract- In current digital world, Security has become the major issue for the organization. Every day the amount of data is growing in the world. Processing and analyzing of the data is becoming the new challenge for the analyzers. For this purpose, big data is useful to process the high volume of data in less time. Current security tools like existing firewalls and Intrusion Detection Systems are still not able to detect and prevent the attacks and intrusions in full proof manner and giving many false alarms. Big Data analytics concept could be very useful for analyzing, detection and providing full security to the organization because of the ability of handling the large amount of data. In this paper, we have described the concept and the roll of big data. We have also proposed a model using process mining to generate the alerts in the case of attacks.   Index Terms— Big Data, Process Mining, Intrusion Detection System, Logs.

scholarly journals Big Data Process Engineering under Manifold Coordinate Systems

2021 ◽  
Vol 18 ◽  
pp. 7-11
Author(s):  
Volodymyr Riznyk
Keyword(s):  
Big Data ◽  
High Performance ◽  
Scientific Basis ◽  
Process Engineering ◽  
Optimal Placement ◽  
Coordinate Systems ◽  
Cyclic Groups ◽  
Data Coding ◽  
Data Process ◽  
General Number

This paper involves techniques for improving the quality indices of big data process engineering with respect to high-performance coded design, transmission speed, and reliability under manifold coordinate systems. The system formed with limited number of basis vectors. The set of modular sums of the vectors including themselves form t-dimensional toroidal coordinate grid over the toroid, and the basis is sub-set of general number of grid coordinate set. These design techniques make it possible to configure high performance information technology for big data coding design and vector signal processing. The underlying mathematical principles relate to the optimal placement of structural elements in spatially or temporally distributed systems by the appropriate algebraic constructions based on cyclic groups in extensions of Galois fields, and development of the scientific basis for optimal solutions for wide classes of technological problems in big data process engineering and computer science.

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