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When working with big data, the first and most crucial step is to ensure a reliable data source. Our approach involves using Python to scrape data from various government websites, focusing on the three major financial reports—profit statements, balance sheets, and cash flow statements—from industries such as banking, securities, stocks, and trusts. After collecting the raw data, we perform analysis and store it in a structured database. For the data processing part, we use Java to implement the analysis of these financial reports.
The program design follows the principles of object-oriented programming (OOP) in Java. We leverage features like inheritance, encapsulation, and polymorphism to create a flexible and maintainable codebase. This design allows us to reuse previously written classes, making future updates and modifications more efficient and less error-prone.
The overall architecture is designed to handle multiple types of PDF files. The core component is the `ParentTable` class, which contains three essential methods:
- `matchStartLine()`: This method identifies the starting line of a financial report within a document.
- `matchEndLine()`: This method locates the end of the financial report.
- `anla()`: This method parses the rows of the report once the start and end lines are identified.
When dealing with different PDF formats, we override these methods in child classes to accommodate variations in table structures. This flexibility ensures that our system can handle a wide range of financial documents without requiring significant rework.
To process an entire PDF file, we create a `PDFFile` class. This class calls the `anla()` method from the `ParentTable` class to parse all the tables present in the document. Here's a simplified version of the main parsing logic:
```java
public boolean anla() {
for (ParentTable table : tables) {
int nIndexStart = table.matchStartLine(lines);
int nIndexEnd = table.matchEndLine(lines);
// Remove the table content from the original lines
String[] arrTableLines = new String[nIndexEnd - nIndexStart + 1];
System.arraycopy(lines, nIndexStart, arrTableLines, 0, nIndexEnd - nIndexStart + 1);
if (arrTableLines.length <= 0) continue;
table.setTableLines(arrTableLines);
table.anla();
}
}
```
In the main program, invoking the `anla()` function of the `PDFFile` class will automatically parse all tables in the provided PDF files. This process handles different table structures by leveraging the overridden methods in the child classes.
In the next section, we will explore how to implement multithreading to improve the efficiency of parsing large numbers of PDF files simultaneously. This optimization will significantly reduce processing time and allow the system to scale effectively when handling high volumes of data.
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