% Task 2: Skyline Search (refer to weeks 8 & 9 & 10 lecture notes) % Dataset: There are three datasetscity1, city2, and city3each representing homes in the city. These datasets contain 2D points and are stored in text files formatted as follows: id_i x_1 yi id_? x_?2 y? id_n x_n y_n Each line in the datasets represents a home, with \"x\" indicating the cost of the home (for example, $500,000) and \"y\" representing its size (for example, 200 square meters). The goal is to apply the Skyline Search algorithm to find homes that provide the optimal balance of size and cost. This method helps to filter out properties that are either too costly or too small in comparison to other available options. Program Design: * Select ONE dataset (cityl, city2, or city3). * Implement the following algorithms to assist users in choosing the ideal home based on two key criteria: cost and size. 1. Sequential Scan Based Method: Identify the skyline by sequentially evaluating whether each node is dominated by any other nodes. 2. Branch and Bound Skyline (BBS) Algorithm: Construct an R-tree for the selected dataset. Implement the BBS algorithm with the R-tree to identify the skyline. 3. BBS with Divide-and-Conquer: Firstly, divide the dataset into two subspaces (based on X dimension or Y dimension), then construct an R-tree for each subspace. Implement the BBS algorithm to identify the skyline in each subspace. Finally, obtain the skyline for the entire space through 1D dominance screening method. \f