Rahul Kumar Chawda

Bio: Rahul Kumar Chawda is an academic researcher from Maulana Azad National Institute of Technology. The author has contributed to research in topics: Transfer of learning & Artificial intelligence. The author has an hindex of 1, co-authored 2 publications receiving 21 citations.

Big data and advanced analytics tools

Abstract: Nowadays a big data analytics is a very broad area for both academia and industry. Big data analytics has attracted intense interest from all academia and industry recently for its attempt to extract knowledge, information and wisdom form big data. Big data and cloud computing, two of the most important trends that are defining the new emerging analytical tools. Big data analytical capabilities using cloud delivery models could ease adoption for many industry, and most important thinking to cost saving, it could simplify useful insights that could providing them with different kinds of competitive advantage. Many companies to provide online Big Data analytical tools some of the top most companies like Amazon Big data Analytics Platform, HIVE web based Interface, SAP Big data Analytics, IBM InfoSphere BigInsights, TERADATA Big Data Analytics, 1010data Big Data Platform, Cloudera Big Data Solution etc. Those companies analyze huge amount of data with help of different type of tools and also provide easy or simple user interface for analyzing data. This paper deals with the study of big data 5V's definition, Analysis requirements, tools, frame works and different type of cloud based big data analytics tools provide by different companies and functioning of Hadoop or MapReduce Process.

Analysing Big Data in VANET via HADOOP Framework

Abstract: Methods/Statistical analysis: The technology of big data is continuously growing and with its rapid increase it is gaining the attention of the researchers. The data is analyzed and outputted in a form that it helps in making quick responses and action in real time environment like Vehicular Ad hoc network. Big data helps in gaining the insight view of the stored, operational and altered data, to improve the traffic conditions. When the Vehicular Ad Hoc Network and the big data are combined, it helps in maintaining the large amount of traffic triggers very easily as the data mining process in big data helps to make quick decisions on the basis of statistics or graph, which are the result of analysis of data.

Learning with Big Data Technology: The Future of Education

Abstract: The use of Big Data systems in the field of education allows to envisage new approaches and new learning contexts. Indeed, the rapid emergence of the new e-learning platforms have been presented in many interests. However, the quality of the teaching service rendered depends on the capacity of the learning approaches to be provided to learners, content and learning path tailored to their needs. In this paper, we will present how Big Data helps to solve education issues through reaching the objective of learning. Then, we will introduce some opportunities of Big Data analytics to develop the efficiency and effectiveness of students learning and maximize their knowledge retention. Finally, our research method show that students can generate personalized activities and offer academic advising. Big Data can expose the capabilities of learners, predict their future performances and offer assistance for educational organizations to make strategic decisions.

Big data analytics in smart mobility: Modeling and analysis of the Aarhus smart city dataset

Abstract: A smart city is a modern and visionary approach for a city to provide intelligent and smart urban services by using information and communication technologies (ICT). The Internet of Things (IoT), emerging through intelligent networking and sensing technologies, is seen as the data-driven enabler for smart cities with current and future infrastructures. The Open Data Aarhus datasets have been created from sensor data in the city Aarhus in Denmark. This paper presents how big data technologies in the context of smart cities are used to implement a framework with a prototype R Shiny application to analyze road traffic and pollution data to make a step towards smart mobility. The main objective of the approach is the calculation and visualization of the least polluted route from a chosen start to an end point by applying an algorithm utilizing the MapReduce framework running on a Hadoop cluster.

Opinion Mining of Twitter Data using Hadoop and Apache Pig

Abstract: Twitter, one of the largest and famous social media site receives millions of tweets every day on variety of important topic. This large amount of raw data can be used for industrial , Social, Economic, Government policies or business purpose by organizing according to our need and processing. Hadoop is one of the best tool options for twitter data analysis and hadoop works for distributed Big data , Streaming data , Time Stamped data , text data etc. This paper discuss how to use FLUME for extracting twitter data and store it into HDFS for opinion mining because twitter contains variety of opinions on various topics so we have to analyse these opinions using hadoop and its ecosystems to check every tweets polarity either tweets contains positive ,negative or neutral opinions on particular topic. This paper provides an efficient mechanism to perform opinion mining by coming up with a finish to finish pipeline with the assistance of Apache Flume ,Apache HDFS, and Apache Pig. Here we have used dictionary based approach for analysis for which we have implemented pig statements through which we can analysis these complex twitter data to check polarity of the tweets based on the polarity dictionary through which we can say that which tweets have negative opinion or positive opinion.

A Review On Relationship Between Iot– Cloud Computing – Fog Computing (Applications And Challenges)

Abstract: Many vital domains such as smart building, smart cities, healthcare, agriculture, and environment monitoring, take benefits from IoT (Internet of Things) technology emergence. Recently, the amount of IoT generated data becomes huge, creating many research challenges in data management topic, in another side, transmission of these amounts of data requires large bandwidth, and generate significant delay. From this vision, we prepare this paper to help new searchers interested in IoT data management topic, to find out a good starting point. For this reason, we shed light on IoT technology, and show its relationship with big data evolution scene, then show the importance of the new paradigm known as fog computing to overcome most of these problems. At first, we introduce the five elements of an IoT object and common communication models used by smart objects, then we discuss comparison of hardware IoT platforms, and recommendations to take into consideration while choosing a hardware IoT platform, in the next pages we introduce cloud IoT infrastructures, followed by a presentation of fog computing advantages and challenges.