using big-data methods analyse the Cross platform aviation

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  • 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 !