Category Archives: Data Analytics

esProc Helps Process Structured Text in Java–Conditional Filtering

Following problems will arise if you perform conditional filtering on text files in Java alone: 1. The text file is not a database,soit cannot be accessed by SQL. The code needs to be modified if filtering conditions are changed. Besides, … Continue reading

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esProc Helps Process Structured Texts in Java –Expression Computing

As Java doesn’t directly support dynamically parsing expressions in the text files, the computation can only be realized by splitting strings manually and then writing a recursive program. The whole process requires writing a great amount of code, is complicated … Continue reading

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esPro Helps Process Structured Texts in Java – Data Import

Java provides only the most basic data import functions like specifying separators. But other common functions, such as importing specified columns by column names, specifying the order of columns and data types and that no separatorshould be used, need to … Continue reading

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esProc Helps Process Structured Text in Java – Import data into the database

While importing the structured text files into the database using Java alone, we need to combine the SQL statements together manually, and to deal with various troublesome situations as well, like if the data in a table has been existed, … Continue reading

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esProc’s Participation in Java Structured Text Processing– Grouping and Summarizing

Following problems will arise if you perform conditional filtering on text files in Java alone: The text file is not a database, so it cannot be accessed by SQL. The code needs to be modified if the expression of grouping … Continue reading

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Constants in esProc

In esProc computing, we may frequently use constants or store the data in the cell directly. In this article, let’s learn the usage of constants in the esProc. 1.Directly using constant in the expression Constants can be directly used in … Continue reading

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Programming Languages for Desktop Data Analysis

For a programming language for desktop data analysis, the most important is that it must be user-friendly and have great computing power. We can judge whether a language is suitable for analyzing data on desktop from six aspects: application environment, … Continue reading

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