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Amy Ma is a 12th Grade student attending Marjory Stoneman Douglas High School in Florida, USA. Amy joined the HPCC Systems Intern Program to work on a project that supports our Cloud Native platform. Read Amy's 2021 intern blog journal which includes a more in depth look of her work and view her 2021 Poster.
Amy returned to the HPCC Systems Intern Program for the second time in 2022 to complete a project involving the collecting dispersed information about data patterns and combining it into one document. This document will be added to our current suite of manuals which are available here on our website.
As well as the resources included here, read Amy's 2022 intern blog journal which includes a more in depth look of her 2022 HPCC Systems intern project.
Poster Abstract
Data Patterns is a library or ECL Bundle that provides data profiling and research tools for use by ECL programmers. Data Patterns was an existing feature of HPCC Systems, however it was never formally documented. The information about usage was documented in three separate files. The purpose of this project was to gather the information from the various files and consolidate them into a book accessible to users from the Documentation area of the HPCC Systems website The documentation includes the following sections about the usage and functions of Data Patterns:
- Introduction
- Using Data Patterns inside ECL Watch
- Using the Data Patterns bundle
- Using the Std.DataPatterns Module in ECL
Presentation
In this Video Recording, Amy provides a tour and explanation of her poster content.
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| Charvi Dave is a BTech student studying Data Science at NMIMS’ Mukesh Patel School of Technology Management and Engineering. Charvi joined the program to work on a project involving the development of a Resume Analyzer in the programming language, NLP++. The analyzer worked on text files of Resumes. It extracted the main headers and sections of the Resumes with efficiency and accuracy. The intent was to create an automated solution to the recruitment process using NLP++. NLP++ is a “smart” language and can learn things like humans do. It stores information in Knowledge Bases. After the Analyzer extracts the information, it stores it in the Knowledge Base which makes it easier to view and analyze the Resume. |
Poster Abstract
A Resume Analyzer is the implementation of an approach to apply various techniques for analyzing the resumes a company receives and retrieving the main sections.
Our Analyzer works regardless of the format of the resume. Word/PDF/Image Resumes need to be converted into Text files since NLP++ can only take text files as an input. NLP++ consists of built-in English dictionaries and tokenizers. It automatically breaks down text into tokens, making it easier to work with Natural Language. We use the Text file of a Resume as an input in NLP++. According to the formatting and words, we can write Rules and use Knowledge in NLP++. We create an Analyzer which consists of multiple passes, where each pass of code performs a particular task or extracts a different piece of information from the resume. When the Analyzer is run, all the passes of code run on the file that is selected. The Analyzer which identifies, classifies and extracts the headers and sections of the Resume i.e. Skills, Work Experience, Email ID, Education, etc.
This information is then stored in text files and a Knowledge Base (KB). It is easier to view and analyze the KB than it is to go through the actual Resume. We have created dictionaries consisting of a list of all possible Headers, Skills, Technologies, Languages, etc, which helps the Analyzer identify a piece of information and store it in the KB accordingly.
We are attempting to reduce time and efforts on the company’s side. The companies can adopt the system as a part of their recruitment process. We are attempting to simplify the recruitment process and help companies identify the right, relevant talent.
Presentation
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Resume analyzer in NLP++
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