Recent Posts by Neil Alex

Neil is a Data Engineer closely working with open source search engines like Lucene and Elasticsearch since 2011. After graduating from Vinayaka Missions in BE Computer Science, Neil worked with Algotree Research Pvt Ltd where he ran into the world of Search. He was closely associated with the active development of Project NewsMills which is a real time news analytics platform built on top of Elasticsearch. He has hands on experience with NLP tools like GATE as he lead the data management team associated with the project. Now he is a freelance search consultant, ardently looking forward to explore new realms of free text and semantic search.

Before setting up elasticsearch to fulfill entity extraction, it is worth checking out how it became such an easy task. There is a lot of buzz around the new Ingest API shipped with elasticsearch 5.x.

The Ingest API allows data manipulation and enrichment by defining a pipeline through which every document is subject to pass. This pipeline is created with a set of processors - each of which do specific tasks that enrich our data. A typical example of the processor is a grok processor, which allows you to modify and structure your unstructured log using pattern matching. Elasticsearch 5 ships many built-in processors about which you can read here.

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Natural Langue Processing, or NLP, is one of the most active areas of research in Data Analytics due to the large volume of data available across the web and the need to analyze and gain insights from this data that constitute to development and growth from a business perspective. There are a number of areas like Entity Extraction, Event Classification Sentiment Analysis, and more that NLP can be thought of like a super set to. We considered how elasticsearch can be used as a source to visualize the end product of all these tasks. This series introduces basic level prototypes of the functional areas of NLP to help you get started.

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Although Elasticsearch offers an efficient scoring algorithm, it may often be inadequate in e-commerce contexts. Most users tend to care only about the topmost number of results. which means that it’s very important to have a flexible scoring mechanism. If you can present the topmost results according to user preference, then your conversion rate is likely to increase significantly.

In this article, we’ll look at the default scoring configuration in Elasticsearch, and we'll also walk through several customizations to the scoring. This knowledge can help you achieve a user-customizable list of results.

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The previous posts in this series focused on aggregations with which we did some faceting and analytics. Now let us put aside aggregations for a while and check out some other exciting features of Elasticsearch that could be put to good use in our e-commerce context. This post introduces Percolation as a useful tool from the merchant's perspective.

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The ever growing competition in the field of e-commerce analytics is proof of the increasing importance of business intelligence, and the increasing popularity of Elasticsearch isn’t a coincidence. But did you know that Elasticsearch can also help you manage your business intelligence requirements?

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Elasticsearch began as a full-text search solution, but its evolution has brought it to the point where it is a top contender for building e-commerce product search.

Continue reading below to find out why.

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