big data

1. “Cross-Platform Aviation Analytics Using Big-Data
Methods”
Pro. Ranjit R. Banshpal

2. Contents
What Is Big-Data?
Why Big-Data?
Big-Data Application Domain
What Is Aviation?
What Is The Problem In Aviation
Big-Data Analytics
Conclusions
References

3. What Is Big-Data?
No single standard definition.
Big Data is basically a vast amount of data.
Requires new architecture, techniques, algorithms and
analytics to manage and extract value and hidden
knowledge

4. What Is Big-Data? Contd…..
 Big-Data is usually defined by 3Vs:

5. What Is Big-Data? Contd…
Sometimes one more parameter is considered

6. Big-Data Is All About…Big-Data Is All About…
Understand and navigate
federated big data sources
Manage & store huge
volume of any data
Structure and control data
Manage streaming data
Analyze unstructured data
Integrate and govern all
data sources
Federated Discovery and Navigation
Hadoop File System
MapReduce
Data Warehousing
Stream Computing
Text Analytics Engine
Integration, Data Quality, Security,
Lifecycle Management, MDM

7. Why Big-Data
Since the amount of data collected, and analyzed in
enterprises has increased several-folds
 volume, variety, and velocity of generation and
consumption,
Organizations have started struggling with architectural
limitations of traditional RDBMS architectures.
Hence arises the need to focus on
Big Data

8. Big-Data Application Domains
 Big Data can be applied to solve problems in various
domains
Financial Industry
 Retail Industry
 Mobility
 Health Care
 Insurance
 Aviation

9. What Is Aviation ?
Aviation is defined as the design , development, production,
operation and use of aircraft.
The aviation industry highly depends on data for operational
planning and execution.
For analyzing airspace performance, operational efficiency
and aviation safety a big and heterogeneous data set is
required.

10. What Is The Problem In Aviation?
 In Aviation the data sets are published by diverse sources and
do not have the standardization, uniformity or defect
controls required for simple integration and analysis.
 Hence the traditional data mining techniques are effective
only on uniform data sets.
 Integrating heterogeneous data sets introduces complexity in
 Data standardization, Data normalization and scalability.

11. Big-Data Analytics
Analytics is the process of examining diverse, large-scale
data sets to uncover patterns, unknown correlations and
other useful information .
Organizations have different levels of
(1)database management expertise and
(2) knowledge to process and analyze big data sets
Focuses on unstructured data sources

12. Big-Data Analytics Contd…
Employ the software tools commonly used as part of
advanced analytics disciplines such as data mining and
predictive analytics.
Mining data, trends or analysis of these multi-terabyte data
sets requires parallel software running to keep pace with user
demands and processing expectations

13. Traditional Data Warehouse Analytics
Vs Big Data Analytics
Analyzes on the data that is well
understood
Targets at unstructured data outside of
traditional means of capturing the data.
Traditional Analytics is built on
top of the relational data model.
Most of the big data analytics
database are based out Columnar
databases
Traditional analytics is batch
oriented.
Big Data Analytics is aimed at near real
time analysis of the data using the
support of the software meant for it
Parallelism in a traditional
analytics system is achieved
through costly hardware like
MPP
(Massively Parallel Processing)
systems and / or SMP systems
While there are appliances in the market
for the Big Data Analytics, this can also
be achieved through commodity
hardware and new
generation of analytical software like
Hadoop or other Analytical databases

14. Big-Data Analytics- A Solution
The unstructured data sources used for big-data analytics,
do not fit into desktop or small-scale database structures .
Hence can be hosted using cloud computing at lower cost,
and mined more efficiently.
A cloud based Big data Analytics approach is used to
provide efficient solution

15. Big-Data Analytics- A Solution Contd…
 The goal of cloud computing is
 To allow users to benefit from all of these technologies
 Without the need for deep knowledge about or
expertise with each one of them.
 A new class of big-data technology has emerged to
address user demands for horizontal scaling and
availability of underlying data.

16. Big-Data Analytics- A Solution Contd…
Examples include
NoSQL databases,
Hadoop,
and MapReduce.
Through big-data analytics and technologies,
 massive data sets can be integrated and
 unified results can be presented from across the data sets.

17. Big-Data Analytics- A Solution Contd…
To see how Big data analytics methods are applied on
aviation problem, let us consider the working of masFlight.
masFlight is a Global Aviation Data Warehouse and Big-
Data Analytics Platform .
 masFlight’s methods vertically integrated big-data solutions
for global airlines, airports and industry vendors.

18. Big-Data Analytics- A Solution Contd…Big-Data Analytics- A Solution Contd…
 masFlight’s methods combine
 conditioned data,
 physical and cloud based data warehousing,
 flexible interfaces and
 data mining tools to provide a complete, turnkey
solution for operations planning and research worldwide.
masFlight developed proprietary cloud based data collection
and integration systems that merge large scale operational
data sets in real-time.

19. ConclusionsConclusions
 Big Data can be very helpful with real time data.
Big-Data analytics methods are very efficient.
Big-Data analysis fundamentally transforms operational,
financial and commercial problems in aviation
Hence aviation data sets issue can be addressed by considering
Big-Data Analytics Methods, Data warehousing and
software solutions for fast response data mining

20. References
1. Dr. Tulinda Larsen, masFlight, Bethesda, MD, “Cross-platform aviation analytics using
big-data methods”, IEEE Integrated Communications Navigation and Surveillance (ICNS)
Conference, 2013.
2. Samet Ayhan, Boeing Research & Technology, Chantilly, Virginia Johnathan Pesce,
Embry-Riddle Aeronautical University, Daytona Beach, Florida “Predictive analytics with
aviation big data” IEEE Integrated Communications Navigation and Surveillance (ICNS)
Conference,2013.
3. Zheng, Zibin ; Zhu, Jieming ; Lyu, Michael R. “Service-Generated Big Data and Big
Data-as-a-Service: An Overview” Big Data (BigData Congress), IEEE International
Congress, 2013.
4. Sagiroglu, S. ; Dept. of Comput. Eng., Gazi Univ., Ankara, Turkey ; Sinanc, D. “Big data: A
review” Collaboration Technologies and Systems (CTS), 2013 International Conference

21. References Contd..
4. Dong, X.L. ; AT&T Labs.-Res., Florham Park, NJ, USA ; Srivastava, D. “Big data
integration” Data Engineering (ICDE), 2013 IEEE 29th International Conference
5. Wigan, M.R. ; Oxford Systematics, Melbourne, VIC, Australia ; Clarke, R. “Big Data's
Big Unintended Consequences” Computer 2013 IEEE JOURNALS & MAGAZINES
6. Big Data for Development: Challenges & Opportunities May2012 by global pulse
7. http://tdwi.org/portals/big-data-analytics.aspx
8. http://strata.oreilly.com/tag/big-data
9. http://www.eng.auburn.edu/users/fmm0002/ISQC2013Paper.pdf
10. www.thoughtworks.com/big-data-analytics
11. http://www.teradata.com/business-needs/Big-Data-Analytics/

22. Thank You !