MODEL QUESTIONS SIMILAR UESTION FOR QUESTION 1 : Ticket Reservation System for Sri Lanka Railways The OLTP tables and their attributes for a typical ticket reservation system with standard notations follow: Stations Routes Trains Tickets Reservations Tables and Attributes: Stations: StationID, Name, Location, Region, RouteID Routes: RouteID, Name, StartStationID, EndStationID, Distance Trains: TrainID, Name, DepartureTime, ArrivalTime, TravelTime, TrainClass, RouteID, StartStationID, EndStationID Tickets: TicketID, SeatNumber, ReservationDate, TravelDate, Status, Amount, Class, TrainID Reservations: ReservationID, TicketID, TravelDate, Status In the above design, Stations, Routes, Trains, Tickets, and Reservations are the OLTP tables, and Primary keys are marked in BOLD, while foreign keys are marked in BoldItalic. 2.1 Stating all LOGICAL assumptions, create a data warehouse design for the above OLTP database. 2.2 Explain why surrogate keys are essential for Type 2 SCDs. 2.3 What method would you propose to analyze ticket reservations for the following scenarios? 1. Analysis of ticket reservations during the bus strike dates. 2. Analysis of ticket reservations on School Vaction in Sri Lanka. SIMILAR QUESTION FOR QUESTION 4 4. A large e-commerce company is leveraging social media data to understand customer sentiments towards their products and services. The dataset includes user reviews, comments, and mentions across various platforms. The company aims to implement text analytics to extract valuable insights, detect spam, and enhance customer satisfaction. 1) Explain why text analytics in the context of social media data can be considered complex. 2) Propose strategies to reduce the complexity of text analytics in social media. How can the implementation of specific techniques, such as normalization, help improve the accuracy of sentiment analysis? 3) Discuss the importance of spam detection in social media analytics. How can a system effectively identify and filter out spam content from user-generated data? 4) Explore how recommender systems can be integrated into the text analytics process to enhance customer satisfaction. Provide an example. 5) Explain the importance of measuring accuracy in text analytics applications in context of social media. How can the Term Document Incident Matrix (TDIM) be utilized to assess the performance of sentiment analysis models? 6) Elaborate on the role of text preprocessing techniques in improving the effectiveness of sentiment analysis. Provide specific examples of tokenization, normalization, and stemming in the context of social media data. 1.