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GLOBALSOFT TECHNOLOGIES 
IEEE PROJECTS & SOFTWARE DEVELOPMENTS 
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE 
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS 
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com 
Data mining with big data 
Abstract 
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With 
the fast development of networking, data storage, and the data collection capacity, Big Data are now 
rapidly expanding in all science and engineering domains, including physical, biological and biomedical 
sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, 
and proposes a Big Data processing model, from the data mining perspective. This data-driven model 
involves demand-driven aggregation of information sources, mining and analysis, user interest 
modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven 
model and also in the Big Data revolution. 
Existing system 
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With 
the fast development of networking, data storage, and the data collection capacity, Big Data are now 
rapidly expanding in all science and engineering domains, including physical, biological and biomedical 
sciences. 
Proposed system 
This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and 
proposes a Big Data processing model, from the data mining perspective. This data-driven model 
involves demand-driven aggregation of information sources, mining and analysis, user interest 
modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven 
model and also in the Big Data revolution.
SYSTEM CONFIGURATION:- 
HARDWARE CONFIGURATION:- 
 Processor - Pentium –IV 
 Speed - 1.1 Ghz 
 RAM - 256 MB(min) 
 Hard Disk - 20 GB 
 Key Board - Standard Windows Keyboard 
 Mouse - Two or Three Button Mouse 
 Monitor - SVGA 
SOFTWARE CONFIGURATION:- 
 Operating System : Windows XP 
 Programming Language : JAVA 
 Java Version : JDK 1.6 & above.

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2014 IEEE JAVA DATA MINING PROJECT Data mining with big data

  • 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com Data mining with big data Abstract Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution. Existing system Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. Proposed system This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
  • 2. SYSTEM CONFIGURATION:- HARDWARE CONFIGURATION:-  Processor - Pentium –IV  Speed - 1.1 Ghz  RAM - 256 MB(min)  Hard Disk - 20 GB  Key Board - Standard Windows Keyboard  Mouse - Two or Three Button Mouse  Monitor - SVGA SOFTWARE CONFIGURATION:-  Operating System : Windows XP  Programming Language : JAVA  Java Version : JDK 1.6 & above.