Archive for the ‘Solr’ Category

Max Charas

Solr Processing Pipeline

april 19 - 2010 | Max Charas

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 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.

Max Charas

Solr – the Sunny Side of Search

april 1 - 2010 | Max Charas

When I started working for Findwise two years ago, Apache Solr was one of those no-name search platforms. We could barely get our customers to consider Solr even after proving that the platform would be a perfect match for their business needs. As time passed and the financial crisis hit the world, a few of our customers started considering Solr, but then usually for the reason that it was “free” – not for the functionality of the platform.

Things have changed. More and more companies now offer support and training for Solr. It seems that the platform is gaining momentum on the enterprise market.
In fact, I was just in Oslo, Norway to become a certified Lucid Imagination training partner, as the need for training is growing rapidly, even up here in the snow-covered Nordics.

Today we even have customers approaching us asking questions about how, and not if, they should use Solr. I wouldn’t have imagined that two years ago …

Could this be the year that Solr goes head to head with the large enterprise search platforms?
And where will we be in another two years?

I wish I knew.

Maria Johansson

Faceted Search by LinkedIn

mars 12 - 2010 | Maria Johansson

My RSS feeds have been buzzing about the LinkedIn faceted search since it was first released from beta in December. So why is the new search at LinkedIn so interesting that people are almost constantly discussing it? I think it’s partly because LinkedIn is a site that is used by most professionals and searching for people is core functionality on LinkedIn. But the search interface on LinkedIn is also a very good example of faceted search.

I decided to have a closer look into their search. The first thing I realized was just how many different kinds of searches there are on LinkedIn. Not only the obvious people search but also, job, news, forum, group, company, address book, answers and reference search. LinkedIn has managed to integrate search so that it’s the natural way of finding information on the site. People search is the most prominent search functionality but not the only one.

I’ve seen several different people search implementations and they often have a tendency to work more or less like phone books. If you know the name you type it and get the number. And if you’re lucky you can also get the name if you only have the number. There is seldom anyway to search for people with a certain competence or from a geographic area. LinkedIn sets a good example of how searching for people could and should work.

LinkedIn has taken careful consideration of their users; What information they are looking for, how they want it presented and how they need to filter searches in order to find the right people. The details that I personally like are the possibility to search within filters for matching options (I worked on a similar solution last year) and how different filters are displayed (or at least in different order) depending on what query the user types. If you want to know more about how the faceted search at LinkedIn was designed, check out the blog post by Sara Alpern.

But LinkedIn is not only interesting because of the good search experience. It’s also interesting from a technical perspective. The LinkedIn search is built on open source so they have developed everything themselves. For those of you interested in the technology behind the new LinkedIn search I recommend “LinkedIn search a look beneath the hood”, by Daniel Tunkelang where he links to a presentation by John Wang search architect at LinkedIn.

Caroline Abrahamsson

How to create better search – VGR leads the way

januari 11 - 2010 | Caroline Abrahamsson

I realise we are a bit late. Fredrik Wackå, a senior IT-strategist, has already written an excellent article on his blog (in Swedish). He has, among other things, been interviewing Kristian Norling (at Twitter), who has been working with portal strategies and search for many years at Västra Götalands regionen.
Although, for all our non-Swedish speaking guests here is a short summary:

Findwise has during the last few months been working on a new search solution for Västra Götalands regionen.  The two main goals have been to deliver a search experience that seems both fast and accurate.
The result?
Today making a search at VGR takes about 0,1-0,2 seconds, faster than a Google search on the web.

Furthermore, there was a need for context. Large amount of information requires ways to filter and sort – otherwise the users will drown in the result list.
By giving the end-users the ability to sort the search result the users can look for general information within an area as well as quickly narrow down to a specific piece (for example by two clicks be able to see only the PDF-files created in 2009). The filters (and thereby metadata standard) includes:

• Information type
• Where the document resides
• Where it belongs in the organization
• What source it has
• When it was last changed
• Who has written it
• What format it resides in
• Keywords that has been created

VGR

VGR

The search solution also includes a metadata service. As so many others VGR has been struggling with getting the metadata in place.
Apart from the metadata supported by the system (where Dublin Core is being used) the metadata service is doing two things:
• Analyses the content in the text, compares it to taxonomy and gives the writer suggestions of keywords that he/she can use
• Gives the writer the ability to add additional keywords

Apart from this the end-users will be able to add etiquettes (tags). These will be compared with two lists. If the tags appears in the “white list” it will be published right away, if they are in the “blacklist” they will be deleted. Anything inbetween are controlled before they are published.

To conclude: a lot of effort has been put into creating a good search experience and VGR continues to deliver functionality and solutions that are light-years ahead of many others. The combination of supporting systems and using the ”collected intelligence” of the writers and end-users will make it even better over time.
Search is about both supporting systems, content and people.

Read more in Fredrik Wackås blog