Technical Implementation Overview

This solution processes CVPR cofnerence papers to generate rseearch trend visualizations and extract PDF resources. The system comprises two components: a Python web crawler and a web-based visualization interface.

Python Data Processing Implementation

import re
import requests
import pymysql
from collections import defaultdict

def fetch_page_content(target_url):
    response = requests.get(target_url)
    return response.text

def create_db_connection():
    connection = pymysql.connect(
        host='localhost',
        user='db_user',
        password='secure_password',
        db='research_db'
    )
    return connection, connection.cursor()

def extract_paper_metadata():
    conf_url = 'http://openaccess.thecvf.com//CVPR2019.py'
    page_content = fetch_page_content(conf_url)
    
    pdf_links = re.findall(
        r"(?<=href=\")[^\"]+?\.pdf(?=\">pdf)|(?<=href=\')[^']+?\.pdf(?=\">pdf)", 
        page_content
    )
    
    paper_titles = re.findall(
        r"(?<=2019_paper.html\">)[^<]+(?=)", 
        page_content
    )
    
    return list(zip(paper_titles, pdf_links))

def compute_word_frequency(paper_data):
    title_text = " ".join(title for title, _ in paper_data)
    words = title_text.split()
    frequency_map = defaultdict(int)
    
    for term in words:
        frequency_map[term] += 1
        
    return sorted(
        frequency_map.items(), 
        key=lambda x: x[1], 
        reverse=True
    )

def persist_data(connection, cursor):
    paper_records = extract_paper_metadata()
    frequency_data = compute_word_frequency(paper_records)
    
    # Store paper metadata
    cursor.executemany(
        "INSERT INTO papers (title, pdf_url) VALUES (%s, %s)",
        paper_records
    )
    
    # Store word frequencies
    cursor.executemany(
        "INSERT INTO term_frequencies (term, count) VALUES (%s, %s)",
        frequency_data
    )
    
    connection.commit()

# Execution
db_conn, db_cursor = create_db_connection()
persist_data(db_conn, db_cursor)
db_cursor.close()
db_conn.close()

Visualization Interface Implemantation

<%@ page contentType="text/html;charset=UTF-8" %>
<%@ taglib prefix="c" uri="http://java.sun.com/jsp/jstl/core" %>

<html>
<head>
  <script src="https://cdn.jsdelivr.net/npm/echarts@4.8.0/dist/echarts.min.js"></script>
  <script src="/js/wordcloud.min.js"></script>
  <style>
    #visualization-container { 
      width: 800px; 
      height: 600px; 
      background: #f8f9fa 
    }
    .paper-link { font-family: 'Segoe UI', sans-serif; font-size: 1.1rem }
  </style>
</head>

<body>
  <div id="visualization-container"></div>
  
  <script>
    const chart = echarts.init(document.getElementById('visualization-container'));
    const frequencyData = [];
    
    fetch('/frequency-data')
      .then(response => response.json())
      .then(records => {
        records.forEach(item => {
          frequencyData.push({ 
            name: item.term, 
            value: item.count 
          });
        });
        
        renderVisualization(frequencyData);
      });

    function renderVisualization(termData) {
      const options = {
        tooltip: {},
        series: [{
          type: 'wordCloud',
          shape: 'circle',
          sizeRange: [20, 80],
          rotationRange: [-45, 45],
          textStyle: {
            color: () => `rgb(${
              [100 + Math.floor(Math.random() * 155),
               100 + Math.floor(Math.random() * 155),
               100 + Math.floor(Math.random() * 155)]
            })`
          },
          data: termData
        }]
      };
      
      chart.setOption(options);
      
      chart.on('click', params => {
        window.location = `/papers?term=${encodeURIComponent(params.name)}`;
      });
    }
  </script>

  <table>
    <thead>
      <tr><th>Research Papers</th></tr>
    </thead>
    <tbody>
      <c:forEach items="${paperList}" var="paper">
        <tr>
          <td>
            <a class="paper-link" href="${paper.url}">
              ${paper.title}
            </a>
          </td>
        </tr>
      </c:forEach>
    </tbody>
  </table>
</body>
</html>

Implementation Notes

• The Python component extracts paper metadata and computes term frequencies
• Data is stored in MySQL database tables: papers (titles, URLs) and term_frequencies
• Web interface visualizes term frequencies using ECharts word cloud
• Clicking visualization terms filters associated research papers
• Common words (pronouns/articles) may dominate frequency results without filtering