Seasonal influenza, commonly referred to as "Flu", affects 1530% of the world's population every year, causing over 200,000 millions of people to be hospitalized from associated complications. Influenza is a contagious respiratory illness, which can range in severity from mild cases with cold-like symptoms to death. The hospitals reports include the patient's data, when they arrived to the hospital and when they leave it. Flu epidemics are fast-moving and spread rapidly due to rapid viral reproduction and short generation times (time from when an infected person infects another), which makes them very difficult to control. Additionally, there are several different strains of the influenza virus and new viruses constantly evolving. Consequently, to design and implement a predictive system in order to predict when, where and at what level of severity the flu will strike during the flu season is considered to be a significantly challenging analytic problem. a) State if the case study is a big data problem or not? If yes, mention the characteristics of big data exist in the above problem? b) Define your problem and give a detailed description of your data set (types, size, duration, ...etc.) c) Determine clearly the aim and objectives of the system d) Specify which type of analysis you will adopt? Justify your answer? e) Explain the tools and techniques, which you suggest to use? Justify your answer? f) Draw a detailed block diagram showing the different phases of the proposed system. Relate these phases to the data analytic lifecycle