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.