Tag Archives: Lucene

Under the hood of the search engine

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While using a search application we rarely think about what happens inside it. We just type a query, sometime refine details with facets or additional filters and pick one of the returned results. Ideally, the most desired result is on the top of the list. The secret of returning appropriate results and figuring out which fits a query better than others is hidden in the scoring, ranking and similarity functions enclosed in relevancy models. These concepts are crucial for the search application user’s satisfaction.

In this post we will review basic components of the popular TF/IDF model with simple examples. Additionally, we will learn how to ask Elasticsearch for explanation of scoring for a specific document and query.

Document ranking is one of the fundamental problems in information retrieval, a discipline acting as a mathematical foundation of search. The ranking, which is literally assigning a rank to a document matching search query corresponds with a term of relevance. Document relevance is a function which determines how well given document meets the search query. A concept of similarity corresponds, in turn, to the relevance idea, since relevance is a metric of similarity between a candidate result document and a search query. Continue reading →

Video: Introducing Hydra – An Open Source Document Processing Framework

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Introducing Hydra – An Open Source Document Processing Framework from presented at Lucene Revolution hosted on Vimeo.

Presented by Joel Westberg, Findwise AB
This presentation details the document-processing framework called Hydra that has been developed by Findwise. It is intended as a description of the framework and the problem it aims to solve. We will first discuss the need for scalable document processing, outlining that there is a missing link between the open source chain to bridge the gap between source system and the search engine, then will move on to describe the design goals of Hydra, as well as how it has been implemented to meet those demands on flexibility, robustness and ease of use. This session will end by discussing some of the possibilities that this new pipeline framework can offer, such as freely seamlessly scaling up the solution during peak loads, metadata enrichment as well as proposed integration with Hadoop for Map/Reduce tasks such as page rank calculations.

Text Analytics in Enterprise Search

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A presentation made by Daniel Ling at Apache Lucene Eurocon in Barcelona, october 2011.

We think this is the first of many forthcoming presentations.

We also want to get more involved in the community in the future. By doing presentations, sponsoring, contributing code. Hope to bring more news on this subject in the next few weeks. Enjoy the presentation:

Text Analytics in Enterprise Search, Daniel Ling, Findwise, Eurocon 2011 from Lucene Revolution on Vimeo.

Distributed processing + search == true?

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In June 2011, I attended the Berlin Buzzwords conference. The main theme of the conference was undoubtedly the current paradigm shift in distributed processing, driven by the major success of Hadoop. Doug Cutting – founder of Apache projects such as Lucene, Nutch and Hadoop – held one of the keynotes. He focused on what he recognized as the new foundations for this paradigm shift:

– Commodity hardware
– Sequential file access
– Sharding
– Automated, high level reliability
– Open source

Distributed processing is done fairly well with Hadoop. Distributed search on the other hand is more or less limited to sharding and/or replicating the index. The downside of sharding is that you perform the same search on multiple servers and then need to combine the results. Due to the nature of algorithms in search such as tf/idf, tasks like ranking results suffers. Andrzej Białecki (another frequent Lucene committer) held a presentation on this topic, and his view can be summarized as: Use local search as long as you can, distribute only when the cost of local search limitations outweighs the cost of distributed search.

The setup of automated replication and sharding, with help from Zookeeper in the Solr Cloud project, is a major step in the right direction but the question on how to properly combine search results from different nodes still remains. One thing is sure though, there is a lot of interesting work being done in this area.

Solr 3.1 released

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Last friday, Solr 3.1 was released along with Lucene 3.1. This might seem like a big step from previous version 1.4.1, but is an effect of the merged development for Solr and Lucene that took place a year ago. The Solr version now reflects the Lucene version that is used.

For a complete list of new features and enhancements, you can read the release notes. Though, some of the most interesting features are:

  • Extended dismax (edismax) query parser. It’s an enhancement over dismax, supports full lucene query syntax etc.
  • Spatial search (ie, we can now enable geo-search; sort by distance, boost by distance etc)
  • Numeric range facets.
  • Lots of optimizations and performance improvements, including better Unicode and 64-bit JVM support.

Update: There’s a good list of features and enhancements at Sematexts blog:

I’m really keen on the Spatial Search which open up a new set of applications, espeacially for Mobile Search where you have the advantage of knowing the position of the user.

I’m glad the community pulled of this release after the merge with Lucene and it will be fun to start working with it. What’s your favorite feature in 3.1? Drop a comment!

OmniFind Enterprise Edition 9.1 – New Capabilities Discussed Over Breakfast

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During the last year a number of interesting things has happened to IBM’s search platform and the new version, OmniFind 9.1, was released this summer. Apart from a large number of improvements in the interface, the change to basing the new solution on open source (Lucene) has proven to be a genius by-pass of some of OmniFinds previous shortcomings.

The licensing model is still quite complicated, something Stephen E Arnold highlighted earlier this year. Since a number of our customers have chosen to take a closer look at OmniFind as a search solution we decided to host a breakfast seminar together with IBM last Thursday, in order to discuss the new features and show how some of our customer are working with it.

Without a doubt, the most interesting part is always to discuss how the solution can be utilized for intranets, extranets, external sites and e-business purposes.

Apart from this, we also took a look at some of the new features:
Type ahead (query suggestion), based on either search statistics or indexed content

Type ahead

Faceted search i.e. the ability to filter on dates, locations, format etc as well as numeric and date range. The later is of course widely used within e-business.

Facets for e-business

Thumbnail views of documents (yes, exactly what it sounds like: a thumbnail view for first page of documents in results page)

Thumbnail of a document

Search analytics in OmniFind 9.1 holds a number of interesting statistic capabilities. Some things worth mentioning is number of queries, query popularity, number of users, average response time (ms) and worst response time (ms).

Save searches (to be able to go back and see if new information has been included), search within result sets (to further narrow your result set within a given result set) and did-you-mean functionality (spell checking) are also included.

..and improvements on the administrator side, just to mention a few:

  • Ability to change the relevancy i.e. to adjust and give certain types of information higher ranking
  • Support for incremental indexing i.e. to only re-index the information that is new or changed since the last time you made it searchable

To conclude: IBM is making a whole lot of improvements in the new version, which are worth taking a closer look at. During the spring we are running upgrading projects for some of our customers, and we will keep you up-to-date with the different application areas OmniFind Enterprise Edition 9.1 is being used for. Please let us know if you have any particular questions or have areas that you are interested in.

Apache Nutch Making Use of Open Pipeline

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During the last couple of months I’ve been working on a project for Uppsala University. The project’s goal is to improve the findability on the university web site. The solution that we are working on is based on Apache Nutch 1.1 in conjunction with Apache Solr 1.4. Nutch provides us with a robust web crawler that scales very well and also gives us a page rank for each page that we can use for relevance tuning. Besides the web information crawled by Nutch, the search application will also be used to search people and organizational information that we index from another source. I thought that I would share some details on how we are using Nutch.

We have made two extensions to Nutch, one is a parser plug-in that can run Open Pipeline embedded in it. This was an important extension in order to get better control of the information that we index to Solr and also to be able to reuse our different Open Pipeline components. The main stages of the pipeline are the following:

  1. Extract the encoding of a web page
  2. Extract all links from a web page
  3. Extract all headings (hx) from a web page
  4. Remove all tags that don’t contain complete sentences on a web page
  5. Extract text and metadata from different types of documents with Tika
  6. Do some metadata mapping and cleaning
  7. Populate facets according to metadata and/or URL
  8. Do static URL ranking
  9. Replace certain common titles with the largest heading of the web page

The other extension we made to Nutch is an indexing filter that makes sure all our metadata fields are indexed to Solr.

So far so good. The fetching, parsing and indexing works well now and currently our largest challenge is tuning all the different relevance parameters we have, as well as harmonizing the relevance of web information to that of people and organizational information. I will have to get back to you on how that went!

Solr Processing Pipeline

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Hi again Internet,

For once I have had time to do some thinking. Why is there no powerful data processing layer between the Lucene Connector Framework and Solr? I´ve been looking into the Apache Commons Processing Pipeline. It seems like a likely candidate to do some cool stuff.  Look at the diagram below.

A schematic drawing of a Solr Pipeline concept. (Click to enlarge)

What I´m thinking of is to make a transparent Solr processing pipeline that speaks the Solr REST protocol on each end. This means that you would be able to use SolrJ or any other API to communicate with the Pipeline.

Has anyone attempted this before?  If you’re interested in chatting about the pipeline drop me a mail or just grab me at Eurocon in Prague this year.