Project

Scholarly Search Engine (SSE)

The name of the project is Scholarly Search Engine

It is a software that takes in input a file in a particular format (CSV) and allows one to run particular search operations

The input CSV file describes some metadata of existing scholarly articles published in a few academic journals

You need a group

The project must be implemented by a group of people

You need to

• form groups of at least 3 and at most 4 people

• choose a name for the group (yes: a name) - it will be used to publish the ranks of the best-performing projects (more info later)

• communicate the name of the group and its members (including their emails) to me by sending an email at silvio.peroni@unibo.it

All groups must be ready by next Monday (11 December)

Project stub

from <group_name> import *

class ScholarlySearchEngine():
  def __init__(self, source_csv_file_path):
    self.data = process_data(source_csv_file_path)

  def search(self, query, col, is_number, partial_res=None):
    result_data = do_search(self.data, query, col, is_number, partial_res)
    return result_data
	
  def pretty_print(self, result_data):
    list_of_strings = do_pretty_print(self.data, result_data)
    return list_of_strings
		
  def publication_tree(self, author):
    pub_tree = do_publication_tree(self.data, author)
    return pub_tree
	
  def top_ten_authors(self):
    list_of_tuples = do_top_ten_authors(self.data)
    return list_of_tuples

  def coauthor_network(self, author):
    coauth_graph = do_coauthor_network(self.data, author)
    return coauth_graph

Your output

You have to develop a Python file named as your group, where spaces are substituted by underscores and the whole file is lowercase

E.g.: group Best group ever, file best_group_ever.py

Each group has to implement the six functions that have been highlighted in red in the previous slide

An example of input will be provided for testing the software

process_data

def process_data(source_csv_file_path)

It takes in input a comma separated value (CSV) file, and return a data structure containing all the data included in the CSV is some form

The data can be preprocessed, changed according to some empirical rule, ordered in a certain way, etc.

These data will be automatically provided in input to the other functions

Example of CSV data

authors (list of strings): Given Name 1, Family Name 1; Given Name 2, Family Name 2; ...

categories (list of strings): category 1; category 2; ...

keywords (list of strings): keyword 1; keyword 2; ...

do_search

def do_search(data, query, col, is_number, partial_res=None)

Return a selection of the data processed before which answer to the query

Kinds of query

The query is a string that actually can contains a quite complex set of operations, according to the following ones:

• boolean operator: and, or, not
  <tokens 1> <operator> <tokens 2>

• comparison operator: <, >, <=, >=
  <operator> <tokens>

Wild cards and rules

Multiple wildcards * can be used, e.g. World*Web looks for all the string that matches with the word World followed by zero or more characters, followed by the word Web (examples: World Wide Web, World Spider Web, etc.)

All searches are case insensitive – e.g. searching for World will match also strings that contain world

It must be possible to execute another do_search on the partial data returned by a previous execution of the same function

do_pretty_print

do_pretty_print(data, result_data)

do_publication_tree

do_publication_tree(data, author)

Return all the publications written by such author described by the following tree:

• root node: author name

• children of root node: as many node as many years of author's publication

• children of an year node: the result of the do_pretty_print function applied to the particular article written by the author and published in that year

do_top_ten_authors

do_top_ten_authors(data)

Return a list of tuples with two items each, where the first item is a string representing the author full name and the second item is the number of papers it has published

The tuple in the list are sorted in descending order, starting from the author with most publication

do_coauthor_network

do_coauthor_network(data, author)

Return a graph having a central node for the input author and a series of additional connected nodes for each other person who co-authored a paper with the input author, where the weight of the arc connecting the input author with his co-author is the number of paper they have co-authored together

Submission

The project, i.e. the implementation of the six functions including any additional ancillary function developed, must be included in a single file named as your group, e.g. best_group_ever.py

The file must be sent by email to me at silvio.peroni@unibo.it

Submission: 2 days before the exam session (the whole group must attend the session) – e.g. send the project the 21st of January for discussing it on the session of the 23rd of January

Your project will be compared with the others in terms of efficiency (i.e. time needed for addressing specific tasks)

Evaluation

Maximum score = c-score + e-score + o-score = 16

All projects will run on a large CSV file

Correctness of the result: c-score <= 4

Efficiency of the software: e-score <= 4; projects ranked according to the time spent for addressing various tasks

1. e-score = 4

2. e-score = 3

3. e-score = 2

Oral colloquium: -8 <= o-score <= 8; it is a personal score, each member of the group has its own

At least 2 for passing the exam, otherwise an additional effort is required (new function)