Big Data

Poor Man’s Training Data: Graph-Based Semi-Supervised Learning

By Michael S. Malak and Robin East

Semi-supervised learning combines the best of both worlds of supervised learning and unsupervised learning. In this article, excerpted from Spark GraphX in Action, we talk about semi-supervised learning.

What Is a Graph and Why Is It Useful?

By Corey L. Lanum

In this article, excerpted from Visualizing Graph Data, we’ll introduce the concept of a graph and its history and uses.

What is a Recommender System?

By Kim Falk

In this article, excerpted from Practical Recommender Systems, I talk about the steps that compose a recommender system.

How (I think) Netflix gathers evidence while you browse

By Kim Falk
In this article, excerpted from my book Practical Recommender Systems, I explain the concept of “evidence” using Netflix.

How (I think) Netflix gathers evidence while you browse (PDF)

Solving an applied relevance problem

By Doug Turnbull, author of Relevant Search
How do you solve an applied relevance problem? What process can you define that incorporates both some of the narrower, domain-specific data points that influence your relevance along with Information Retrieval techniques? In this article, we discuss the applied relevance problem.

Solving an applied relevance problem (PDF)

First steps with GraphX using the Spark Shell

By Michael S. Malak, author of Spark GraphX in Action
In this article, we will download some sample graph data, and using the Spark Shell, quickly determine which out of a series of papers has been cited the most frequently.

First steps with GraphX using the Spark Shell (PDF)

Interaction Patterns: Request/Response

Andrew G. Psaltis, author of Streaming Data, explains the concept of the Request/Response pattern and how you can collect data using it.

Interaction Patterns: Request/Response (PDF)

The pros and cons of SPAs

By Simon Holmes, author of Getting MEAN with Mongo, Express, Angular, and Node
Coding in SPAs (Single Page Applications) is most likely a vast improvement on what you’ve been doing before, but it may not always be the best solution. Here’s a brief look at some things to bear in mind about SPAs when designing a solution, and how to decide whether a full SPA is right for your project.

The pros and cons of SPAs (PDF)

Composition Techniques with JCascalog

The power of these abstractions is in how they promote reuse and composability. In this article based on chapter 5 taken from Big Data, author Nathan Marz discusses various composition techniques possible with JCascalog.

Composition Techniques with JCascalog (PDF)

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