Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

August 4, 2013

Server-side clustering of geo-points…

Server-side clustering of geo-points on a map using Elasticsearch by Gianluca Ortelli.

From the post:

Plotting markers on a map is easy using the tooling that is readily available. However, what if you want to add a large number of markers to a map when building a search interface? The problem is that things start to clutter and it’s hard to view the results. The solution is to group results together into one marker. You can do that on the client using client-side scripting, but as the number of results grows, this might not be the best option from a performance perspective.

This blog post describes how to do server-side clustering of those markers, combining them into one marker (preferably with a counter indicating the number of grouped results). It provides a solution to the “too many markers” problem with an Elasticsearch facet.

The Problem

The image below renders quite well the problem we were facing in a project:

clustering

The mass of markers is so dense that it replicates the shape of the Netherlands! These items represent monuments and other things of general interest in the Netherlands; for an application we developed for a customer we need to manage about 200,000 of them and they are especially concentrated in the cities, as you can see in this case in Amsterdam: The “draw everything” strategy doesn’t help much here.

Server-side clustering of geo-points will be useful for representing dense geo-points.

Such as an Interactive Surveillance Map.

Or if you were building a map of police and security force sightings over multiple days to build up a pattern database.

July 25, 2013

Sense

Filed under: ElasticSearch — Patrick Durusau @ 1:28 pm

Sense

From the webpage:

A JSON aware developer console to ElasticSearch.

A JSON aware interface to ElasticSearch. Comes with handy machinery such as syntax highlighting, autocomplete, formatting and code folding.

Once installed, you can click on the ElasticSearch icon next to your url bar to open Sense in a new tab.

Works with Chrome.

Has good reviews.

Do you know of a similar tool for Solr?

July 24, 2013

Exploring ElasticSearch

Filed under: ElasticSearch,Search Engines,Searching — Patrick Durusau @ 3:39 pm

Exploring ElasticSearch: A human-friendly tutorial for ElasticSearch. by Andrew Cholakian.

An incomplete tutorial on ElasticSearch.

However, unlike printed (dead tree) and pdf (dead electrons), you can suggest additional topics and I suspect that useful comments would be appreciated as well.

A “live” tutorial on popular software like ElasticSearch, that follows the software as it develops, could prove to be almost as popular as the software itself.

July 14, 2013

Solr vs ElasticSearch

Filed under: ElasticSearch,Lucene,Solr — Patrick Durusau @ 7:14 pm

Solr vs ElasticSearch by Ryan Tabora.

Ryan evaluates Solr and ElasticSearch (both based on Lucene) in these categories:

  1. Foundations
  2. Coordination
  3. Shard Splitting
  4. Automatic Shard Rebalancing
  5. Schema
  6. Schema Creation
  7. Nested Typing
  8. Queries
  9. Distributed Group By
  10. Percolation Queries
  11. Community
  12. Vendor Support

As Ryan points out, making a choice between Solr and ElasticSearch requires detailed knowledge of your requirements.

If you are a developer, I would suggest following Lucene, as well as Solr and ElasticSearch.

No one tool is going to be the right tool for every job.

July 13, 2013

Transforming Log Events into Information

Filed under: ElasticSearch,Functional Programming,Log Analysis — Patrick Durusau @ 4:08 pm

Transforming Log Events into Information by Matthias Nehlsen.

From the post:

Last week I was dealing with an odd behavior of the chat application demo I was running for this article. The issue was timing-related and there were no actual exceptions that would have helped in identifying the problem. How are you going to even notice spikes and pauses in potentially thousands of lines in a logfile? I was upset, mostly with myself for not finding the issue earlier, and I promised myself to find a better tool. I needed a way to transform the raw logging data into useful information so I could first understand and then tackle the problem. In this article I will show what I have put together over the weekend. Part I describes the general approach and applies to any application out there, no matter what language or framework you are using. Part II describes one possible implementation of this approach using Play Framework.

Starting point for transforming selected log events into subjects represented by topics?

Not sure I would want to generate IRIs to identify the events as subjects, particularly since they already have identifiers in the log.

A broader processing model for the TAO should allow for the use of user defined identifiers.

What is the Latin for: User Beware? 😉

July 12, 2013

Aggregation Module – Phase 1 – Functional Design (ElasticSearch #3300)

Filed under: Aggregation,ElasticSearch,Merging,Search Engines,Topic Maps — Patrick Durusau @ 2:47 pm

Aggregation Module – Phase 1 – Functional Design (ElasticSearch Issue #3300)

From the post:

The new aggregations module is due to elasticsearch 1.0 release, and aims to serve as the next generation replacement for the functionality we currently refer to as “faceting”. Facets, currently provide a great way to aggregate data within a document set context. This context is defined by the executed query in combination with the different levels of filters that are defined (filtered queries, top level filters, and facet level filters). Although powerful as is, the current facets implementation was not designed from ground up to support complex aggregations and thus limited. The main problem with the current implementation stem in the fact that they are hard coded to work on one level and that the different types of facets (which account for the different types of aggregations we support) cannot be mixed and matched dynamically at query time. It is not possible to compose facets out of other facet and the user is effectively bound to the top level aggregations that we defined and nothing more than that.

The goal with the new aggregations module is to break the barriers the current facet implementation put in place. The new name (“Aggregations”) also indicate the intention here – a generic yet extremely powerful framework for defining aggregations – any type of aggregation. The idea here is to have each aggregation defined as a “standalone” aggregation that can perform its task within any context (as a top level aggregation or embedded within other aggregations that can potentially narrow its computation scope). We would like to take all the knowledge and experience we’ve gained over the years working with facets and apply it when building the new framework.

(…)

If you have been following the discussion about “what would we do differently with topic maps” in the XTM group at LinkedIn, this will be of interest.

What is an aggregation if it is not a selection of items matching some criteria, which you can then “merge” together for presentation to a user?

Or “merge” together for further querying?

That is inconsistent with the imperative programming model of the TMDM, but it has the potential to open up distributed and parallel processing of topic maps.

Same paradigm but with greater capabilities.

July 8, 2013

Querying ElasticSearch – A Tutorial and Guide

Filed under: ElasticSearch,Lucene,Search Engines — Patrick Durusau @ 6:59 pm

Querying ElasticSearch – A Tutorial and Guide by Rufus Pollock.

From the post:

ElasticSearch is a great open-source search tool that’s built on Lucene (like SOLR) but is natively JSON + RESTful. Its been used quite a bit at the Open Knowledge Foundation over the last few years. Plus, as its easy to setup locally its an attractive option for digging into data on your local machine.

While its general interface is pretty natural, I must confess I’ve sometimes struggled to find my way around ElasticSearch’s powerful, but also quite complex, query system and the associated JSON-based “query DSL” (domain specific language).

This post therefore provides a simple introduction and guide to querying ElasticSearch that provides a short overview of how it all works together with a good set of examples of some of the most standard queries.

(…)

This is a very nice introduction to ElasticSearch.

Read, bookmark and pass it along!

June 30, 2013

Elasticsearch and Joining

Filed under: ElasticSearch,Indexing,Joins — Patrick Durusau @ 1:51 pm

Elasticsearch and Joining by Felix Hürlimann.

From the post:

With the success of elasticsearch, people, including us, start to explore the possibilities and mightiness of the system. Including border cases for which the underlying core, Lucene, never was originally intended or optimized for. One of the many requests that come up pretty quickly is the whish for joining data across types or indexes, similar to an SQL join clause that combines records from two or more tables in a database. Unfortunately full join support is not (yet?) available out of the box. But there are some possibilities and some attempts to solve parts of issue. This post is about summarizing some of the ideas in this field.

To illustrate the different ideas, let’s work with the following example: we would like to index documents and comments with a one to many relationship between them. Each comment has an author and we would like to answer the question: Give me all documents that match a certain query and a specific author has commented on it.

A variety of options are explored, including some new features of Elasticsearch.

Would you model documents with comments as an association?

Would you query on roles when searching for such a comment by a specific author on such a document?

June 27, 2013

elasticsearch 0.90.2

Filed under: ElasticSearch,Geo-Indexing,Georeferencing — Patrick Durusau @ 12:52 pm

0.90.2 released by Clinton Gormley.

From the post:

The Elasticsearch dev team are pleased to announce the release of elasticsearch 0.90.2, which is based on Lucene 4.3.1. You can download it here.

We recommend upgrading to 0.90.2 from 0.90.1, especially if you are using the terms-lookup filter, as this release includes some enhancements and bug fixes to terms lookup.

Besides the other enhancements and bug-fixes, which you can read about on the issues list, there is one new feature that is particularly worth mentioning: improved support for geohashes on geopoints:

A geohash is a string representing an area on earth – the longer the string the more precise the geohash. A geohash just one character long refers to an area with a very rough precision: +/- 2500 km. A geohash of length 8 would be accurate to within 20m, etc. Because a geohash is just a string, we can index it in Elasticsearch and take advantage of the inverted index to make blazingly fast geo-location queries.

Wikipedia on Geohash. Numerous external links, including http://geohash.org/. Enter geo-coordinates or a geohash, displays map with location displayed.

June 19, 2013

Terms filter lookup [ElasticSearch]

Filed under: ElasticSearch,Search Engines — Patrick Durusau @ 1:17 pm

Terms filter lookup by Zachary Tong.

From the post:

There is a new feature in the 0.90 branch that is pretty awesome: the Terms Filter now supports document lookups.

In a normal Terms Filter, you provide a list of Terms that you want to filter against. This is fine for small lists, but what if you have 100 terms? A thousand terms? That is a lot of data to pass over the wire. If that list of terms is stored in your index somewhere, you also have to retrieve it first…just so you can pass it back to Elasticsearch.

The new lookup feature tells Elasticsearch to use another document as your terms array. Instead of passing 1000 terms, you simply tell the Terms Filter “Hey, all the terms I want are in this document”. Elasticsearch will fetch that document internally, extract the terms and perform your query.

Very cool!

Even has a non-Twitter example. 😉

Nutch/ElasticSearch News!

Filed under: ElasticSearch,Nutch,Search Engines — Patrick Durusau @ 12:51 pm

Apache Nutch-1527

To summarize: Elasticsearch indexer committed to the trunk of Apache Nutch in rev. 1494496.

Enjoy!

May 30, 2013

Getting Started with ElasticSearch: Part 1 – Indexing

Filed under: ElasticSearch,Lucene,Solr — Patrick Durusau @ 2:35 pm

Getting Started with ElasticSearch: Part 1 – Indexing by Florian Hopf.

From the post:

ElasticSearch is gaining a huge momentum with large installations like Github and Stackoverflow switching to it for its search capabilities. Its distributed nature makes it an excellent choice for large datasets with high availability requirements. In this 2 part article I’d like to share what I learned building a small Java application just for search.

The example I am showing here is part of an application I am using for talks to show the capabilities of Lucene, Solr and ElasticSearch. It’s a simple webapp that can search on user group talks. You can find the sources on GitHub.

Some experience with Solr can be helpful when starting with ElasticSearch but there are also times when it’s best to not stick to your old knowledge.

As rapidly as Solr, Lucene and ElasticSearch are developing, old knowledge can be an issue for any of them.

March 30, 2013

ElasticSearch: Text analysis for content enrichment

Filed under: ElasticSearch,Indexing,Search Engines,Searching — Patrick Durusau @ 6:15 pm

ElasticSearch: Text analysis for content enrichment by Jaibeer Malik.

From the post:

Taking an example of a typical eCommerce site, serving the right content in search to the end customer is very important for the business. The text analysis strategy provided by any search solution plays very big role in it. As a search user, I would prefer some of typical search behavior for my query to automatically return,

  • should look for synonyms matching my query text
  • should match singluar and plural words or words sounding similar to enter query text
  • should not allow searching on protected words
  • should allow search for words mixed with numberic or special characters
  • should not allow search on html tags
  • should allow search text based on proximity of the letters and number of matching letters

Enriching the content here would be to add above search capabilities to you content while indexing and searching for the content.

I thought the “…look for synonyms matching my query text…” might get your attention. 😉

Not quite a topic map because there isn’t any curation of the search results, saving the next searcher time and effort.

But in order to create and maintain a topic map, you are going to need expansion of your queries by synonyms.

You will take the results of those expanded queries and fashion them into a topic map.

Think of it this way:

Machines can rapidly harvest, even sort content at your direction.

What they can’t do is curate the results of their harvesting.

That requires a secret ingredient.

That would be you.

I first saw this at DZone.

March 21, 2013

elasticsearch 0.90.0.RC1 Released

Filed under: ElasticSearch,Lucene,Searching — Patrick Durusau @ 2:08 pm

elasticsearch 0.90.0.RC1 Released by Shay Banon.

From the post:

elasticsearch version 0.90.0.RC1 is out, the first release candiate for the 0.90 release. You can download it here.

This release includes an upgrade to Lucene 4.2, many improvements to the suggester feature (including its own dedicated API), another round of memory improvements to field data (long type will now automatically “narrow” to the smallest type when loaded to memory) and several bug fixes. Upgrading to it from previous beta releases is highly recommended. (inserted URL to release notes)

Just to keep you on the cutting edge of search technology!

March 12, 2013

Elasticsearch and Joining

Filed under: ElasticSearch,Joins,Lucene — Patrick Durusau @ 2:40 pm

Elasticsearch and Joining by Felix Hürlimann.

From the post:

With the success of elasticsearch, people, including us, start to explore the possibilities and mightiness of the system. Including border cases for which the underlying core, Lucene, never was originally intended or optimized for. One of the many requests that come up pretty quickly is the whish for joining data across types or indexes, similar to an SQL join clause that combines records from two or more tables in a database. Unfortunately full join support is not (yet?) available out of the box. But there are some possibilities and some attempts to solve parts of issue. This post is about summarizing some of the ideas in this field.

To illustrate the different ideas, let’s work with the following example: we would like to index documents and comments with a one to many relationship between them. Each comment has an author and we would like to answer the question: Give me all documents that match a certain query and a specific author has commented on it.

The latest beta release of Elasticsearch is described as:

If you have more complex requirements for join, a new feature introdcued in the latest beta release may can help you. It introduces another feature that allows for a kind of join by looking up filter terms in another index or type. This allows then e.g. for queries like ‘Show me all comments from documents that relate to this document and the author is ‘John Doe’.

The “looking up” in a different index or type sounds quite interesting.

Have you looked at the new beta of Elasticsearch?

March 9, 2013

Elasticsearch OpenNLP Plugin

Filed under: ElasticSearch,Natural Language Processing — Patrick Durusau @ 3:50 pm

Elasticsearch OpenNLP Plugin

From the webpage:

This plugin uses the opennlp project to extract named entities from an indexed field. This means, when a certain field of a document is indexed, you can extract entities like persons, dates and locations from it automatically and store them in additional fields.

Extracting entities into roles perhaps?

February 11, 2013

New Book: ElasticSearch Server!

Filed under: ElasticSearch,Lucene,Solr — Patrick Durusau @ 3:51 pm

New Book: ElasticSearch Server!

In the blog post dedicated to Solr 4.0 Cookbook we give a small hint that cookbook was not the only project that occupies our free time. Today we can officially say that a few month of hard work is slowly coming to an end – we can announce a new book about one of the greatest piece of open-source software – ElasticSearch Server book!

ElasticSearch server book describes the most important and commonly used features of ElasticSearch (at least from our perspective). Example of topics discussed:

  • ElasticSearch installation and configuration
  • Static and dynamic index structure creation
  • Querying ElasticSearch with Query DSL explained
  • Using filters
  • Faceting
  • Routing
  • Indexing data that is not flat

BTW, some wag posted a comment saying a Solr blog should not talk about ElasticSearch.

I bet they don’t see the sunshine very often from that position either. 😉

January 24, 2013

Solr vs. ElasticSearch: Part 6 – User & Dev Communities

Filed under: ElasticSearch,Searching,Solr — Patrick Durusau @ 8:08 pm

Solr vs. ElasticSearch: Part 6 – User & Dev Communities by Rafał Kuć.

From the post:

One of the questions after my talk during the recent ApacheCon EU was what I thought about the communities of the two search engines I was comparing. Not surprisingly, this is also a question we often address in our consulting engagements. As a part of our Apache Solr vs ElasticSearch post series we decided to step away from the technical aspects of SolrCloud vs. ElasticSearch and look at the communities gathered around thesee two projects. If you haven’t read the previous posts about Apache Solr vs. ElasticSearch here are pointers to all of them:

Rafał compares user activity (discussion lists), resources available, search trends, code statistics.

My take away is that both projects have very vibrant and responsive user and development communities.

You?

January 12, 2013

JUnit Rule for ElasticSearch

Filed under: ElasticSearch,Solr — Patrick Durusau @ 7:02 pm

JUnit Rule for ElasticSearch by Florian Hopf.

From the post:

While I am using Solr a lot in my current engagement I recently started a pet project with ElasticSearch to learn more about it. Some of its functionality is rather different from Solr so there is quite some experimentation involved. I like to start small and implement tests if I like to find out how things work (see this post on how to write tests for Solr).

ElasticSearch internally uses TestNG and the test classes are not available in the distributed jar files. Fortunately it is really easy to start an ElasticSearch instance from within a test so it’s no problem to do something similar in JUnit. Felix Müller posted some useful code snippets on how to do this, obviously targeted at a Maven build. The ElasticSearch instance is started in a setUp method and stopped in a tearDown method:

Useful information about tests for Solr and ElasticSearch is too useful to pass up.

Besides, it reminded me of the need to have testable merging instances, both for TMDM merging as well as more complex merging scenarios.

January 11, 2013

Solr vs ElasticSearch: Part 5 – Management API Capabilities

Filed under: ElasticSearch,Search Engines,Searching,Solr — Patrick Durusau @ 7:35 pm

Solr vs ElasticSearch: Part 5 – Management API Capabilities by Rafał Kuć.

From the post:

In previous posts, all listed below, we’ve discussed general architecture, full text search capabilities and facet aggregations possibilities. However, till now we have not discussed any of the administration and management options and things you can do on a live cluster without any restart. So let’s get into it and see what Apache Solr and ElasticSearch have to offer.

Rafał continues this excellent series on Solr and ElasticSearch and promises there is more to come!

This series sets a high standard for posts comparing search capabilities!

December 21, 2012

Sense

Filed under: ElasticSearch — Patrick Durusau @ 6:09 am

Sense by Boaz Leskes.

From the webpage:

A JSON aware, web based interface to ElasticSearch. Comes with handy machinary such as syntax highlighting, autocomplete, formatting and code folding.

If you are using ElasticSearch, certainly worth a look!

November 25, 2012

Complexificaton: Is ElasticSearch Making a Case for a Google Search Solution?

Filed under: ElasticSearch,Interface Research/Design,Search Interface,Searching — Patrick Durusau @ 10:15 am

Complexificaton: Is ElasticSearch Making a Case for a Google Search Solution? by Stephen Arnold.

From the post:

I don’t have any dealings with Google, the GOOG, or Googzilla (a word I coined in the years before the installation of the predator skeleton on the wizard zone campus). In the briefings I once endured about the GSA (Google speak for the Google Search Appliance), I recall three business principles imparted to me; to wit:

  1. Search is far too complicated. The Google business proposition was and is that the GSA and other Googley things are easy to install, maintain, use, and love.
  2. Information technology people in organizations can often be like a stuck brake on a sports car. The institutionalized approach to enterprise software drags down the performance of the organization information technology is supposed to serve.
  3. The enterprise search vendors are behind the curve.

Now the assertions from the 2004 salad days of Google are only partially correct today. As everyone with a colleague under 25 years of age knows, Google is the go to solution for information. A number of large companies have embraced Google’s all-knowing, paternalistic approach to digital information. However, others—many others, in fact—have not.

I won’t repeat Stephen’s barbs at ElasticSearch but his point applies to search interfaces and approaches in general.

Is your search application driving business towards simpler solutions? (If the simpler solution isn’t yours, isn’t that the wrong direction?)

November 10, 2012

Battle of the Giants: Apache Solr 4.0 vs. ElasticSearch 0.20

Filed under: ElasticSearch,Search Engines,Searching,Solr,SolrCloud — Patrick Durusau @ 8:42 am

Battle of the Giants: Apache Solr 4.0 vs. ElasticSearch 0.20 by Rafał Kuć.

A very nice summary (slides) of Rafał’s comparison of the latest releases of Solr and ElasticSearch.

They differ and those differences fit some use cases better than others.

And the winner is: … (well, I won’t spoil the surprise.)

Read the slides.

Unless you are Rafał, you will learn something you didn’t know before.

November 7, 2012

ElasticSearch in Production

Filed under: ElasticSearch,Searching — Patrick Durusau @ 5:06 pm

ElasticSearch in Production by Anne Veling.

From the description:

With Proquest Udini, we have created the worlds largest online article store, and aim to be the center for researchers all over the world. We connect to a 700M solr cluster for search, but have recently also implemented a search component with ElasticSearch. We will discuss how we did this, and how we want to use the 30M index for scientific citation recognition. We will highlight lessons learned in integrating ElasticSearch in our virtualized EC2 environments, and challenges aligning with our continuous deployment processes.

Includes an amusing evolution diagram that shows the current pinnacle of search techology. (Your guess here.)

Just once I would like to see an evolution diagram where the speaker argues, “we got off here because X requirement was better met here.” 😉

Even with that puffing, I wish I had seen the presentation.

Delivering even a subset of 150M publications is no mean feat. I would liked to hear even more details.

October 30, 2012

Solr vs ElasticSearch: Part 4 – Faceting

Filed under: ElasticSearch,Faceted Search,Facets,Solr,SolrCloud — Patrick Durusau @ 2:16 pm

Solr vs ElasticSearch: Part 4 – Faceting by Rafał Kuć.

From the post:

Solr 4 (aka SolrCloud) has just been released, so it’s the perfect time to continue our ElasticSearch vs. Solr series. In the last three parts of the ElasticSearch vs. Solr series we gave a general overview of the two search engines, about data handling, and about their full text search capabilities. In this part we look at how these two engines handle faceting.

Rafał continues his excellent comparison of Solr and ElasticSearch.

Understanding your software options is almost as important as understanding your data.

October 5, 2012

What’s so cool about elasticsearch?

Filed under: ElasticSearch,Search Engines — Patrick Durusau @ 6:04 am

What’s so cool about elasticsearch? by Luca Cavanna.

From the post:

Whenever there’s a new product out there and you start using it, suggest it to customers or colleagues, you need to be prepared to answer this question: “Why should I use it?”. Well, the answer could be as simple as “Because it’s cool!”, which of course is the case with elasticsearch, but then at some point you may need to explain why. I recently had to answer the question, “So what’s so cool about elasticsearch?”, that’s why I thought it might be worthwhile sharing my own answer in this blog.

Its not a staid comparison piece but a partisan, this is cool piece.

You will find it both entertaining and informative. Good weekend reading.

Will give you something to have a strong opinion (one way or the other) next Monday!

October 2, 2012

Solr vs ElasticSearch: Part 3 – Searching

Filed under: ElasticSearch,Lucene,Solr — Patrick Durusau @ 9:21 am

Solr vs ElasticSearch: Part 3 – Searching by Rafał Kuć.

From the post:

In the last two parts of the series we looked at the general architecture and how data can be handled in both Apache Solr 4 (aka SolrCloud) and ElasticSearch and what the language handling capabilities of both enterprise search engines are like. In today’s post we will discuss one of the key parts of any search engine – the ability to match queries to documents and retrieve them.

  • Solr vs. ElasticSearch: Part 1 – Overview
  • Solr vs. ElasticSearch: Part 2 – Indexing and Language Handling
  • Solr vs. ElasticSearch: Part 3 – Searching
  • Solr vs. ElasticSearch: Part 4 – Faceting
  • Solr vs. ElasticSearch: Part 5 – API Usage Possibilities

Definitely a series to follow.

September 27, 2012

Couchbase and Full-text Search: The Couchbase Transport for Elastic Search

Filed under: Couchbase,ElasticSearch,Full-Text Search,Searching — Patrick Durusau @ 3:36 pm

Couchbase and Full-text Search: The Couchbase Transport for Elastic Search

From the post:

Couchbase Server 2.0 adds powerful indexing and querying capabilities through its distributed map reduce implementation. But in addition to that many applications, particularly content applications also need full-text search capabilities. Today we are releasing a developer preview of the Couchbase Transport Plugin for Elastic Search. This plugin uses the new Cross Data Center Replication functionality which will be a part of Couchbase Server 2.0. Using this new transport, you can get started with Couchbase and ElasticSearch easily. This blog explains how you can have this integration up and running in minutes.

There goes the weekend! Already! 😉

EdSense:… [Sepulcher or bricks for next silo?]

Filed under: Couchbase,Education,ElasticSearch — Patrick Durusau @ 2:55 pm

EdSense: Building a self-adapting, interactive learning portal with Couchbase by Christopher Tse.

From the description:

Talk from Christopher Tse (@christse), Director of McGraw-Hill Education Labs (MHE Labs), on how to architect a scalable adaptive learning system using a combination of Couchbase 2.0 and ElasticSearch as back-ends. These slides are the presented at CouchConf San Francisco on September 21, 2012.

Code for the proof-of-concept project, called “Learning Portal” has been open sourced and is available via Github at http://github.com/couchbaselabs/learningportal

When you hear about semantic diversity, do you ever think about EdSense, Moodle, EdX, Coursera, etc., as examples of semantic diversity?

And semantic silos?

All content delivery systems are semantic silos.

They have made choices about storage, access and delivery that had semantics. In addition to the semantics of your content.

The question is whether your silo will become a sepulcher for your content or bricks for the next silo in turn.

September 25, 2012

Battle of the Giants: Apache Solr 4.0 vs ElasticSearch

Filed under: ElasticSearch,Lucene,SolrCloud — Patrick Durusau @ 1:28 pm

Battle of the Giants: Apache Solr 4.0 vs ElasticSearch

From the post:

Apache Solr 4.0 release is imminent and we have a heavily anticipated Solr vs. ElasticSearch blog post series going on. What better time to share that our Rafał Kuć will be giving a talk titled Battle of the giants: Apache Solr 4.0 vs ElasticSearch at the upcoming ApacheCon/Lucene EuroCon in Germany this November.

Abstract:

In this talk audience will be able to hear about how the long awaited Apache Solr 4.0 (aka SolrCloud) compares to the second search engine built on top of Apache Lucene – ElasticSearch. From understanding the architectural differences and behavior in situations like split – brain, to cluster recovery. From distributed indexing and document distribution control, to handling multiple shards and replicas in a single cluster. During the talk, we will also compare the most used and anticipated features such as faceting handling, documents grouping and so on. At the end we will talk about performance differences, cluster monitoring and troubleshooting.

ApacheCon Europe 2012
Rhein-Neckar-Arena, Sinsheim, Germany
5–8 November 2012

Email, tweet, publicize ApacheCon Europe 2012!

Blog especially! A pale imitation but those of us unable to attend benefit from your posts!

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