Category Archives: Open source

Enterprise search case study: Vårdaktörsportalen makes reliable information easy to find for health professionals

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Vårdaktörsportalen (VAP) is a portal for health care providers made by Västra Götalandsregionen (VGR). This portal makes information from a number of reliable and authorised sources findable and accessible for the people who need it in their daily work. This stretches from doctors and nurses to medical secretaries primarily located in the region of Västra Götaland, Sweden. The site and most of its features and information is also accesible (in Swedish) to anyone through http://vap.vgregion.se. In November 2012 the first version of this site went live and Findwise had a big role in the creation of this search centric site. The main source of information for VAP is the regional guidelines found in a document repository within VGR but some other external sources are also included. These include trustworthy authorities like

  • Socialstyrelsen
  • Läkemedelsverket
  • SBU
  • TLV
  • 1177 - the public health care information site for citizens

This search solution is built around the open source search engine Apache Solr and our common tools for processing and indexing. For this site we have also implemented a rather unique metadata enhancement service that automatically extracts keywords from the document to index and attaches it as metadata. The keyword extraction is based on information from the medical term database SweMeSH. More information (in Swedish) can be found on Google Code. We also include synonyms to keywords to increase recall, making it easier to find documents regardless of what synonym used.

The metadata enhancement service was included because the quality of metadata on the external sites was not very good. VGR will work with the above mentioned authorities to try and make them understand that they would benefit if they improved their data. The source 1177 stands out with very good meta data and overall good quality texts.

We have conducted a user study to see how this first version is able to satisfy user demands. The result of that study shows that some local sources are missing, but a general positive feedback on the idea and the graphical design was collected.

Findwise is looking forward to continue working on VAP with VGR in the future to make it an even better tool.

Related links:

Using log4j in Tomcat and Solr and How to Make a Customized File Appender

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This article shows how to use log4j for both tomcat and solr, besides that, I will also show you the steps to make your own customized log4j appender and use it in tomcat and solr.

Default Tomcat log mechanism

Tomcat by default uses a customized version of java logging api. The configuration is located at ${tomcat_home}/conf/logging.properties. It follows the standard java logging configuration syntax plus some special tweaks(prefix property with a number) for identifying logs of different web apps.

An example is below:

handlers = 1catalina.org.apache.juli.FileHandler, 2localhost.org.apache.juli.FileHandler, 3manager.org.apache.juli.FileHandler, 4host-manager.org.apache.juli.FileHandler, java.util.logging.ConsoleHandler

.handlers = 1catalina.org.apache.juli.FileHandler, java.util.logging.ConsoleHandler

1catalina.org.apache.juli.FileHandler.level = FINE

1catalina.org.apache.juli.FileHandler.directory = ${catalina.base}/logs

1catalina.org.apache.juli.FileHandler.prefix = catalina.

2localhost.org.apache.juli.FileHandler.level = FINE

2localhost.org.apache.juli.FileHandler.directory = ${catalina.base}/logs

2localhost.org.apache.juli.FileHandler.prefix = localhost.

Default Solr log mechanism

Solr uses slf4j logging, which is kind of wrapper for other logging mechanisms. By default, solr uses log4j syntax and wraps java logging api (which means that it looks like you are using log4j in the code, but it is actually using java logging underneath). It uses tomcat logging.properties as configuration file. If you want to define your own, it can be done by placing a logging.properties under ${tomcat_home}/webapps/solr/WEB-INF/classes/logging.properties

Switching to Log4j

Log4j is a very popular logging framework, which I believe is mostly due to its simplicity in both configuration and usage. It has richer logging features than java logging and it is not difficult to make an extension.

Log4j for tomcat

  1. Rename/remove ${tomcat_home}/conf/logging.properties
  2. Add log4j.properties in ${tomcat_home}/lib
  3. Add log4j-xxx.jar in ${tomcat_home}/lib
  4. Download tomcat-juli-adapters.jar from extras and put it into ${tomcat_home}/lib
  5. Download tomcat-juli.jar from extras and replace the original version in ${tomcat_home}/bin

(extras are the extra jar files for special tomcat installation, it can be found in the bin folder of a tomcat download location, fx. http://archive.apache.org/dist/tomcat/tomcat-6/v6.0.33/bin/extras/)

Log4j for solr

  1. Add log4j.properties in ${tomcat_home}/webapps/solr/WEB-INF/classes/ (create classes folder if not present)
  2. Replace slf4j-jdkxx-xxx.jar with slf4j-log4jxx-xxx.jar in ${tomcat_home}/webapps/solr/WEB-INF/lib (which means switching underneath implementation from java logging to log4j logging)
  3. Add log4jxxx.jar to ${tomcat_home}/webapps/solr/WEB-INF/lib

Make our own log4j file appender

Log4j has 2 types of common fileappender,

DailyRollingFileAppender – rollover at certain time interval

RollingFileAppender – rollover at certain size limit

And I found a nice customized file appender -  CustodianDailyRollingFileAppender online.

I happen to need a file appender which should  rollover at certain time interverl(each day) and backup earlier logs in backup folder and get zipped. Plus removing logs older than certain days. CustodianDailyRollingFileAppender already has the rollover feature, so I decide to start with making a copy of this class,

Parameters

Besides the default parameters in DailyRollingFileAppender, I need 2 more parameters,

Outdir – backup directory

maxDaysToKeep – the number of days to keep the log file

You only need to define these 2 parameters in the new class, and add get/set methods for them (no constructor involved). The rest will be handled by log4j framework.

Logging entry point

When there comes a log event, the subAppend(…) function will be called, inside which a super.subAppend(event); will just do the log writing work. So before that function call, we can add the mechanism for back up and clean up.

Clean up old log

Use a file filter to find all log files start with the filename, delete those older than maxDaysToKeep.

Backup log

Make a separate Thread for zipping the log file and delete original log file afterwards(I found CyclicBarrier very easy to use for this type of wait thread to complete task, and a thread is preferable for avoiding file lock/access ect. problems). Call the thread at the point where current log file needs to be rolled over to backup.

Deploy the customized file appender

Let’s say we make a new jar called log4jxxappender.jar, we can deploy the appender by copying the jar file to ${tomcat_home}/lib and in ${tomcat_home}/webapps/solr/WEB-INF/lib

Example configuration for solr,

log4j.rootLogger=INFO, solrlog

log4j.appender.solrlog=com.findwise.xx.log4j.fileappender.YyRollingFileAppender

log4j.appender.solrlog.File=${catalina.home}/logs/solr.log

log4j.appender.solrlog.Append=true

log4j.appender.solrlog.Encoding=UTF-8

log4j.appender.solrlog.DatePattern='.'yyyy-MM-dd

log4j.appender.solrlog.MaxDaysToKeep=10

log4j.appender.solrlog.Outdir=${catalina.base}/logs/backup

log4j.appender.solrlog.layout=org.apache.log4j.PatternLayout

log4j.appender.solrlog.layout.ConversionPattern = %d [%t] %-5p %c - %m%n

Solr.war

Last thing to remember about solr is to zip the deployment folder ${tomcat_home}/webapps/solr and rename the zip file solr.zip to solr.war. Now you should have a log4j enabled solr.war file with your customized fileappender.

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.

Open Source Tools for Text Analytics

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Recently, both clients of Findwise as well as the Enterprise Search community in general are increasingly showing interest in text analytics in order to get a higher business value out of their (often large) volumes of unstructured information.

Text Analytics merges techniques from linguistics, computer science, machine learning, statistics and many of the central algorithms in this field are publically available as open source tools and packages with easily accessible APIs. While many customers of commercial Enterprise Search solutions, such as Automomy, IBM Omnifind, Microsoft FAST ESP, etc., have long benefitted from some sort of Text Analytics (e.g. Entity Extraction, Keyword Extraction and document summarization), the open source components have now come a long way in providing alternative, free of charge solutions with similar performance and feature set.

As every modern enterprise search architecture today has some kind of document processing that is extensible by additional stages or APIs (for example the Open Pipeline with Solr or the pipeline that comes with Microsoft FAST) – the opportunity for plugging new text analytics stages to existing search implementations is open and ready for new innovation.

Among the most popular applications of text analytics that have emerged lately are customized entity extraction, sentiment analysis and document classification – each with a set of open source alternatives (such as Balie, OpenNLP and GATE) readily available for customization and implementation to your document processing.

Regardless of your industry domain, these techniques open up for a wide variety of new ways to interpret the content and discover new trends from your unstructured textual data – be it through sentiment analysis to support the decision making process, trend analysis or relevance model of search, or entity extraction in order to navigate your content by entities (such as company name or person), the enhancement of your texts by meta-data tagging or finding similar and related content.

How are you taking advantage of modern text analytics?

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!

Development Techniques for Solr: Structure First or Structure Last?

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I’d like to share two different development techniques for Solr I commonly use when setting up a Apache Solr project. To explain it I’ll start by introducing the way I used to work. (The wrong way ;) )

Development Techniques for Solr: The Structure First

Since I work as a enterprise search consultant I come across a lot of different data sources.  All of these data sources have at least some structure, some more than others.

My objective as a backend developer was then to first of all figure out how the data source was structured and then design a Solr schema that fit the requirements, both technical and business.

The problem with this was of course that the requirements were quite fuzzy until I actually figured out how the data was structured and even more importantly what the data quality was.

In many cases I would spend a lot of time on extracting a date from the source, converting that to an ISO 8601 date format (Supported by Solr), updating the schema with that field and then finally reindexing. Only to learn that the date was either not required or had too poor data quality to be used.

My point being that I spent a lot of time designing a schema (and connector) for a source which I, and most others, knew almost nothing about.

Development Techniques for Solr: The Structure Last

Ok so what’s the supposed “right way” of doing this?

In Solr there is a concept called dynamic fields. It allows you to map fields that fulfil a certain name criteria to a specific type. In the example Solr schema you can find the following section:

<!– uncomment the following to ignore any fields that don’t already match an existing

field name or dynamic field, rather than reporting them as an error.

alternately, change the type=”ignored” to some other type e.g. “text” if you want

unknown fields indexed and/or stored by default –>

<!–dynamicField type=”ignored” multiValued=”true” /–>

The section above will drop any fields that are not explicitly declared in the schema. But what I usually do to start with is to do the complete opposite. I map all fields to a string type.

<dynamicField multiValued=”true” indexed=”true” stored=”true”/>

I start with a minimalist schema that only has an id field and the above stated dynamic field.

With this schema it doesn’t matter what I do, everything is mapped to a string field, exactly as it is entered.

This allows me to focus on getting the data into Solr without caring about what to name the fields, what properties they should have and most importantly to even having to declare them at all.

Instead I can focus on getting the data out of the source system and then into Solr. When that’s done I can use Solr´s schema browser to see what fields are high quality, contain a lot of text or are suited to be used as facets and use this information to help out in the requirements process.

The Structure Last Technique lets you be more pragmatic about your requirements.

Information Flow in VGR

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The previous week Kristian Norling from VGR (Västra Götaland Regional Council) posted a really interesting and important blog post about information flow. Those of you who doesn’t know what VGR has been up to previously, here is a short background.

For a number of years VGR has been working to give reality to a model for how information is created, managed, stored and distributed. And perhaps the most important part – integrated.

Information flow in VGR

Why is Information Flow Important?

In order to give your users access to the right information it is essential to get control of the whole information flow i.e. from the time it is created until it reaches the end user. If we lack knowledge about this, it is almost impossible to ensure quality and accuracy.

The fact that we have control also gives us endless possibilities when it comes to distributing the right information at the right time (an old cliché that is finally becoming reality). To sum up: that is what search is all about!

When information is being created VGR uses a Metadata service which helps the editors to tag their content by giving keyword suggestions.

In reality this means that the information can be distributed in the way it is intended. News are for example tagged with subject, target group and organizational info (apart from dates, author, expiring date etc which is automated) – meaning that the people belonging to specific groups with certain roles will get the news that are important to them.

Once the information is tagged correctly and published it is indexed by search. This is done in a number of different ways: by HTML-crawling, through RSS, by feeding the search engine or through direct indexing.

The information is after this available through search and ready to be distributed to the right target groups. Portlets are used to give single sign-on access to a number of information systems and template pages in the WCM (Web Content Management system) uses search alerts to give updated information.

Simply put: a search alert for e.g. meeting minutes that contains your department’s name will give you an overview of all information that concerns this when it is published, regardless of in which system it resides.

Furthermore, the blog post describes VGRs work with creating short and persistent URL:s (through an URL-service) and how to ”monitor” and “listen to” the information flow (for real-time indexing and distribution) – areas where we all have things to learn. Over time Kristian will describe the different parts of the model in detail, be sure to keep an eye on the blog.

What are your thoughts on how to get control of the information flow? Have you been developing similar solutions for part of this?

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.

Solr – the Sunny Side of Search

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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.

Faceted Search by LinkedIn

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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.