Wallace Jackson, “Android Apps for Absolute Beginners”, Apress, 2011, ISBN 978 1430234463.

 Illustrate the role of mapreduce programming in various scenarios Unit I -Introduction: Types of Digital Data-Classification of Digital Data- Introduction to Big dataCharacteristics of Data- Evolution of Big data- Definition of Big data- Challenges with big data- What is big data- Why big data- data warehouse environment – hadoop environment. Unit II -Big data Analytics and The Big Data Technology Landscape: What is Big data AnalyticsClassification of Analytics- Top Challenges facing big data-Why is big data analytics important – Data Science- Terminologies used in big data environment - BASE- NoSQL- Hadoop. Unit III -Hadoop: Introducing Hadoop- Why Hadoop- Why not RDBMS- RDBMS versus HadoopDistributed Computing Challenges- History of Hadoop – Hadoop Overview – Use case of HadoopHadoop Distributors – HDFS- Processing Data with Hadoop- Mapping Resources and Applications with Hadoop YARN – Interaction with Hadoop Ecosystem. Unit IV - MongoDB and Mapreduce Programming: What is MongoDB – Why MongoDB –Terms used in RDBMS and MongoDB – Data Types in MongoDB – MongoDB Query Language. Mapreduce Introduction –Mapper – Reducer – Combiner –Partitioner – Searching – Sorting – Compression. Unit V - Introduction to Hive and Machine Language: What is Hive- Hive Architecture – Hive Data Types- Hive File Format – Hive Querey Language – RC File Implementation – SerDe- User Defined Function – Introduction to Machine Learning – Machine Learning Algorithms.

Text Book:

1. Seema Acharya, Subhashini Chellappan, “Big data and Analytics”, First Edition, Wiley, 2016, ISBN: 978-8126554782.

Reference Books:

1. Anil Maheshwari, “Big Data Made Accessible”, McGrawHill Education Publication, 2017, ISBN: 978-9352604548. 2. Nathan Marz, James Warren, “Big data: Principles and Best Practices of Scalable Realtime Data Systems”, Manning Publications, 2015, ISBN: 978-1617290343. 3. Sandeep Karanth, “Mastering Hadoop”, First Edition, Packt Publishing, 2014, ISBN: 9781783983643.

18CA2002 INTERNET OF THINGS

Credits: 3:0:0 Course Objective:

 To understand the fundamental principles of Internet of Things  To understand about wireless sensor networks  To develop IoT based applications

Course Outcome:

Students will be able to  Understand the fundamental concepts of Internet of Things  Understand how to interconnect software and hardware  Analyze protocols in wireless sensor networks  Design IoT applications in various domains  Evaluate performance of IoT applications  Develop sensor based applications through embedded platform Unit I - Introduction to IoT: Defining IoT, Characteristics of IoT, Physical Design of IoT, Logical design of IoT, Functional blocks of IoT, Communication models & APIs. IoT& M2M:Machine to Machine, Difference between IoT and M2M, Software define Network. Unit II - Network & Communication aspects: Wireless medium access issues, MAC protocol survey, Survey routing protocols, Sensor deployment & Node discovery, Data aggregation & Dissemination. Unit III - Challenges in IoT: Design challenges, Development challenges, Security challenges, Other challenges. Unit IV - Domain specific applications of IoT: Home automation, Industry applications, Surveillance applications, Other IoT applications.