1. Privacy Preserving Data Mining 2. Private Information Retrieval 3. Overview of spatial databases and investigation into spatial databases used in commercial Geographic Information Systems 4....

Big Data        20
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Table of contents:
Introduction------------------------------------------------------------------------------ 2
Big Data History and Current Scenario-------------------------------------------- 4
Big Data for Business------------------------------------------------------------------- 6
Big Data Goals--------------------------------------------------------------------------- 9
Big Data Frameworks----------------------------------------------------------------- 10
Big Data Applications----------------------------------------------------------------- 11
Big Data Challenges------------------------------------------------------------------- 12
Big Data Facts and Figures-----------------------------------------------------------14
Big Data Clustering ------------------------------------------------------------------- 15
Conclusion-------------------------------------------------------------------------------16
References------------------------------------------------------------------------------- 17
Introduction:
BIG data started when the data on the internet exploded! We have generated tons and tons of data bytes since the inception of the digital age. This transformation started taking place with the usage of computers on wide scale. The internet came and added fuel to the fire of data and it exploded. The rise of computers, the usage of internet, betterment of technology and innovation at regular intervals have led to the growth of data into big data. Data now in itself is an innovation and helps create insights to the businesses. Going back before the digital age, papers, files, notepads were all data and was maintained in an ethical and clean manner. Computers and spreadsheets gave us a responsible and a compact storage to organise data into large scale and in an easily understandable and accessible way. The information was available at the click of a mouse! Today’s digital world has expanded more than ever and in 2 days we create as much data as we created in the year 2000. It is believed that by the year 2020, the total worldwide data would exceed 5zettabytes today to 50zettabytes! i.e. 10 times more! (Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014)
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s as of 2012, every day 2.5 Exabyte’s (2.5×1018) of data are generated. Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020.[13] By 2025, IDC predicts there will be 163 zettabytes of data.[14] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[15] Every action we take has a digital footprint and creates a trail path that leads to the user of the data. We generate data when we click the mobile data ON, we generate data when we Switch on the GPS, we generate data when we text, call, view websites and every action. The amount of data generated by the machines is growing too. In smart homes, the devices collect data and share it among the other devices to make it smarter. The data is collected in the home servers and in industry too, the machinery plants and factories are fitted with sensors that collect data and transmit it at a very fast bitrate. The following report is the analysis of the usage of Big Data and its analysis tools for the organisations
Big Data History and Current Scenario
The concept of big data started growing around the year 2000. It was the massive amounts of data that was being generated that lead to the initialisation of the term Big Data. The industry analyst Doug Lanley was the one to articulate the mainstream definition of the Big Data which includes the 3 V’s
Volume: Multiple sources are used to collect data in an organisation which includes the social media, the business transactions and information from sensors that collect the machine data. Storage of such huge amounts of data would have been a problem in the past, but in current world due to lesser costing storage devices, Cloud storages and technologies such as Hadoop have eased out data storage.
Velocity: The velocity of the arrival of the data bits have gone up multiple times. The data is structured as well as unstructured. The data must be stored in a timely manner and in a clean format. Sensors and smart metering devices to unstructured data, text documents, emails videos and texts all are different kinds of data which arrive at speed! (Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014)
Variety: Data types are various in today’s world. Ranging from text, audio, video to multiple formats which include the structured and the unstructured data, documents, financial documents and ticker data.
When we consider SAS, two dimensions of data are considered:
1. Variability: Sometimes data arrival at the servers can be low and at sometimes extremes. Data variability measures the increasing velocity and the variety of data. There are peaks and troughs in the data that is being flown. If something is trending on the social media, the rate of flow of data is terrific and if there is world cup going on, the data flow is exorbitant. While on the other hand if it is a normal day and no ups and downs have taken place, stable data flows.
2. Complexity: In todays world, we generate data from multiple sources and from multiple formats. It is this data which has to be mixed and matched, cleansed and also transformed in order to gather insights. It is necessary to connect the data and correlate the relationship among the sources and the data to find out inner understandings. If the correlation is not done, it would be just random data and would spiral out of control if no insights are generated.
Big Data for Business
Organisations and businesses are eager to understand big data and its uses in order to try and get benefits form the applications of big data. A survey was conducted which showed that only 12% of the organisations implement big data and its applications in their business to drive growth and sustainability. Approximately 71% of companies are in the planning stage for the implementation of the Big data. This proves that organisations are still weak in understanding big data and its applications which can be used to create value to the customers and to increase business. The organisations need different knowledge of the data and also the skillset which the big data applications require to build the tools and conduct analysis which help them to compete in a stronger way to deduce insights and be a step ahead of the competition. Organisations can use big data to create smarter decisions and also to improve efficiency of their business. The types of decisions that...