Book Image

Hands-On Big Data Modeling

By : James Lee, Tao Wei, Suresh Kumar Mukhiya
Book Image

Hands-On Big Data Modeling

By: James Lee, Tao Wei, Suresh Kumar Mukhiya

Overview of this book

Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently.
Table of Contents (17 chapters)

VSM with Lucene

The VSM, or term vector model, is an algebraic model for representing text documents as vectors of identifiers such as index terms. It is used in information filtering, information retrieval, indexing, and relevancy rankings.

In VSM, weights associated with the terms are calculated based on the following two numbers:

  • Term frequency (TF): How many times a particular term appears in the document
  • Inverse document frequency (IDF): How important a word is to a document in a collection

VSM is implemented in a lot of open source software, including Apache Lucene, Elasticsearch, Genism, Numpy, Weka, word2vec, and Konstanz Information Miner (KNIME).

Lucene

In this section, we are going to explore the VSM using an...