H

1. Paper Presentation
on
Challenges of Big Data to Big Data Mining
with their Processing Framework
Kamlesh Kumar Pandey
Dept. of Computer Science & Applications
Dr. Hari Singh Gour Vishwavidyalaya, Sagar, M.P
E-mail: kamleshamk@gmail.com
International Conference on Communication Systems and Network Technologies 2018

2. Content
• Big Data
• Big Data Mining
• Data challenges
• Process challenges
• Management Challenges
• Big Data Mining Processing Framework

3. BIG DATA
• Diebold et Al. (2000) is a first writer who discussed the word Big Data
in his research paper. All of these authors define Big Data there means
if the data set is large then gigabyte then these type of data set is
known as Big Data.
• Doug Laney et al (2001) was the first person who gave a proper
definition for Big Data. He gave three characteristics Volume, Variety,
and Velocity of Big Data and these characteristics known as 3 V’s of
Big Data Management. Basically, these 3 V’s describe the framework
of Big Data.
• Gartner (2012), “Big data is high-volume, high-velocity and high-
variety information assets that demand cost-effective, innovative
forms of information processing for enhanced insight and decision
making”

4. BIG DATA V’s
• In present time seven V’s used for Big Data where the first three V’s Volume,
Variety, and Velocity are the main characteristics of big data. In addition to
Variability, Value, Veracity, and Visualization are depending on the
organization.

5. BIG DATA MINING
• Big Data Mining fetching on the requested information, uncovering
hidden relationship or patterns or extracting for the needed
information or knowledge from a dataset these datasets have to meet
three V’s of Big Data with higher complexity.

6. CHALLENGES OF BIG DATA MINING
• Data challenges,
• Process challenges
• Management challenges
• Data challenges are based on the basic characteristics such as volume,
variety, velocity, veracity etc. of the Big Data. These type of challenges differ
from traditional data characteristics.
• Process challenges are based on the technique for data mining, data
processing or analysis in which algorithms are used to mining or analysis,
integration, transform, preprocessing on data etc.
• Management challenges are cover to data management related challenges like
privacy, security, governance, and other aspects.

7. DATA CHALLENGES
• Roberto V. Zicari et al. (2014) and Uthayasankar Sivarajah et al.
(2017) are categorizing data challenges in seven categories.
• Volume
• Variety
• Velocity
• Variability
• Value
• Veracity
• Visualization

8. PROCESS CHALLENGES
• Kaisler et al. (2013) and Uthayasankar Sivarajah et al. (2017) identify
data processing related challenges that can be classify into five steps for
data mining.
• Data acquisition and warehousing
• Data cleaning
• Data analysis and Mining
• Data integration and aggregation
• Data querying and indexing

9. MANAGEMENT CHALLENGES
• Uthayasankar Sivarajah et al (2017) has discussed various
Management challenges which are ensuring data are used correctly,
data access where used by only authorized person, without any
permission data are not accessible, which maintains privacy, given
higher security from external and internal attack, the proper way of
transformed and derived data etc.
• Privacy
• Security
• Data and information sharing
• Cost/operational expenditures
• Data ownership

10. BIG DATA MINING PROCESSING FRAMEWORK
• Wu Xindong et al. (2014) presents a HACE theorem and big data
processing model for big data mining process and challenges
perspective. This big data mining processing model cover to data and
management driven challenges.

11. References
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