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.

Optimized Spatial Query for Data Integration Based on Semantics

2012 ◽  
Vol 229-231 ◽  
pp. 1895-1899
Author(s):  
Shen Yi Qian ◽  
Hao Dong Zhu
Keyword(s):  
Data Integration ◽  
Query Optimization ◽  
Spatial Data ◽  
Hybrid Approach ◽  
Detailed Examination ◽  
Spatial Knowledge ◽  
Spatial Query ◽  
Spatial Queries ◽  
Spatial Data Integration ◽  
Spatial Semantics

Data integration of geospatial data in distributed, heterogeneous environment involves the use of semantic ontologies. In this kind of integration system, semantic technologies play an important role in improving performance and effectiveness of spatial queries. This paper focuses on methods of query optimization based on spatial semantics at the top level of semantic layer in central data integration systems. After analyzing the hybrid approach for spatial data integration, two categories of query optimization strategies are proposed based on detailed examination of special characteristics of spatial data. With spatial knowledge explicitly specified in ontologies and associated rules, spatial queries can be optimized intelligently.

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.

Design and Realization of Digital Urban Planning Spatial Database

2013 ◽  
Vol 756-759 ◽  
pp. 1824-1827 ◽  
Author(s):  
Ze Yun Yang ◽  
Jin Ling Yang ◽  
Xian Ge Cao ◽  
Xiu Hai Li ◽  
Xin Liang ◽  
...  
Keyword(s):  
Urban Planning ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Extraction ◽  
Spatial Database ◽  
Spatial Query ◽  
Huge Amount ◽  
Gis Application ◽  
Attribute Data ◽  
Digital Urban Planning

Based on studying Digital Urban Planning spatial database, this subject uses spatial data engine ArcSDE as interface between GIS application server and database server, and takes the ArcSDE as the core to realize spatial query and spatial analysis of digital urban planning spatial information, and then unified managed the spatial data and attribute data of digital urban planning, finally to support efficient, the huge amount of data extraction.

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