Research Information Management – the Sample Reports from the BRUCE project

September 15, 2011

The work to create a CERIF mapping that could then be indexed via Solr and presented via SolrEyes was based on two Sample Reports, a Summary Staff Report and a Simple Publications Report, created by Dr Rosa Scoble at Brunel.

The idea behind the sample reports was to identify key reporting requirements that would be useful to institutions across the sector. In doing so, we hoped to encourage other institutions to have a play with the tools produced by the BRUCE project in order to generate copies of the reports using their own data.

Feedback on draft reports was sought via the Project Advisory Group. That feedback was then incorporated into the reports that are now being made available here.


From the CERIF Model to the Solr Index

September 11, 2011

Part of the challenge of the BRUCE project is to take a highly relational model like CERIF and convert it into something which can be adequately indexed for searching and faceting.

Apache Solr, like many traditional search engines, works on the principle of key-value pairs. A key-value pair is simply an assertion that some value (on the right) is associated with some key (on the left). Examples of key-value pairs are:

name : Richard
project : bruce
organisation : Brunel University

Typically, the keys on the left come from a set of known terms, while the values on the right can vary arbitrarily. Therefore, when you search for documents belonging to “Richard”, you are asking which documents have the value “Richard” associated with the key “name”.

In addition, keys are often repeatable (although depending on the search index schema this might not be always the case), so you could have multiple “name” keys, with different values.


The objective, then, is for us to convert the graph-like structure of CERIF (that is, it has entities and relationships which do not follow a hierarchy) into the flat key-value structure of a search index. It should be clear from the outset, therefore, that data-loss will necessarily result from this conversion; it is not possible to fully and adequately represent a graph as a set of key-value pairs.

The project aimed, instead, to extract the key information from the CERIF schema from the point of view of one of the Base Entities.

There are 3 Base Entities in CERIF: Publications, People and Organisational Units. Since BRUCE is concerned with reporting principally on staff, we selected People as the Base Entity from which we would view the CERIF graph. By doing this we reduce the complexity of the challenge, since a graph viewed from the point of view of one of its nodes behaves like a hierarchy at least in the immediate vicinity (see the real analysis of this, below, for a clear example).

Our challenge is then simplified to representing a tree structure as a set of key-value pairs.

The second trick we need to use is to decide what kind of information we want to actually report on, and narrow our indexing to fields in the CERIF schema which are relevant to those requirements. This allows us to index values which are actually closely related to eachother as totally separate key-value pairs: as long as the index provides enough information for searching and faceting, it won’t matter that information about their relationship to eachother is lost.

For example: suppose we want to index the publications associated with a person, and we want to be able to list those publications as well as providing an integer count of how many publications were published by that person in some time frame. Initially this might look quite difficult, as a “publication” is a collection of related pieces of information, such as the title, the other authors, the date of publication, and other administrative terms such as page counts and so on. To place this in a set of key-value pairs would require us to do something like:

title: My Publication
publication_date: 01-09-2008
pages: 10

This is fine if there is only one publication by the person, but if they have multiple publications it would not be possible to tell which publication_date was associated with which title.

Instead, we have to remember that this is an index and not a data store. If we wish to list publication titles and count publications within date ranges, then it is just necessary for us to index the titles and the dates separately and ensure that they are used separately within the index. So we may have:

title: My First Paper
title: My Second Paper
publication_date: 01-09-2008
publication_date: 23-05-2009

This configuration loses data by not maintaining the links between publication_date and title, but is completely adequate for the indexing and faceting requirements.

To meet our original requirement stated above we can just count the number of publication_date keys which contain a date which lies within our desired time frame and return this integer count, while simultaneously listing the titles of the publication. The fact that these two pieces of information are not related in the index makes no difference in producing the desired outcome.

CERIF schema

The CERIF schema that we are working with is a limited sub-set of the project, and has been presented in a previous post. The set of tables which describe the graph contain the following fields that we are interested in are:

CERIF Table Columns
cfPers cfPersId, cfGender
cfPers_Class cfPersId, cfClassSchemeId, cfClassId
cfPersName cfPersId, cfFirstNames, cfOtherNames, cfFamilyNames
cfPers_ResPubl cfPersId, cfResPublId, cfClassSchemeId, cfClassId
cfPers_OrgUnit cfPersId, cfOrgUnitId, cfClassSchemeId, cfClassId, cfFraction, cfEndDate
cfPers_Pers cfPersId1, cfPersId2, cfClassSchemeId, cfClassId
cfPers_Fund cfPersId, cfFundId
cfFund cfFundId, cfCurrCode, cfAmount
cfOrgUnit cfOrgUnitId, cfHeadcount
cfOrgUnitName cfOrgUnitId, cfName
cfOrgUnit_OrgUnit cfOrgUnitId1, cfOrgUnitId2, cfClassSchemeId, cfClassId
cfResPubl cfResPublId, cfResPublDate
cfResPublTitle cfResPublId, cfTitle
cfResPubl_Class cfResPublId, cfClassSchemeId, cfClassId

Next, imagine that we pick up the graph by cfPers using cfPersId as the identifier which relates the person to all the other entities, and we can see that a rough hierarchy emerges:


With the exception of the Org Unit data (marked with **), the result is a straightforward enough hierarchy. We can avoid considering the graph that emerges under the organisation unit data by ensuring that the cfPers_OrgUnit table contains all the relevant relationships that we want to consider during indexing, so that we don’t have to attempt to index the org unit graph when preparing an index from the perspective of the person.

Solr index

The Solr index allows us to specify a field name (the key, in the key-value pair), and whether that field is repeatable or not. Each set of key-value pairs is grouped together into a “document”, and that document will represent a single person in the CERIF dataset, along with all the relevant data associated with them. When we have fully built our index, there will be one document per person.

The Solr index which then meets our requirements is constructed from the above CERIF data as follows:

Field Single/Multi Value Notes
entity single “cfPers” Indicates that this is a person oriented document. This allows us to extend the index to view other kinds of entities as well, all represented within one schema.
id unique cfPersId A unique id representing the entity. When other entities are included in the index, this could also be their ids (e.g. cfResPublId)
gender single cfGender
name single a combination of cfFirstNames, cfOtherNames and cfFamilyNames This is the first person name that is encountered in the database, and is used for sorting and presented as the authors actual name. There is another field for name variants
name_variants multi a combination of cfFirstNames, cfOtherNames and cfFamilyNames This allows us to have multiple names for the author for the purposes of searching, although they will not be used for sorting or presented to the end user
contract_end single cfOrgUnit/cfEndDate Taken from the cfEndDate field in the cfPers_OrgUnit table which is tagged by cfClassId as Employee
funding_code multi cfFundId
org_unit_name multi cfOrgUnit/cfName
org_unit_id multi cfOrgUnit/cfOrgUnitId
primary_department single cfOrgUnit/cfName This differs from org_unit_name in that it is the department that the person should be considered most closely affiliated with. This would be, for example, their department or research group. It is used specifically for display and sorting, which is why it may only be single valued.
primary_department_id single cfOrgUnit/cfOrgUnitId The id for the department contained in primary_department
primary_position single cfOrgUnit/cfClassId The position that the person holds in their primary department (e.g. “Lecturer”)
fte single cfOrgUnit/cfFraction The fraction of the time that the person works for their organisational unit which is tagged with cfClassId of Employee.
supervising multi cfPers_Pers/cfPersId2 This lists the ids of the people that the person is supervising. These can be identified as the cfPers_Pers relationship has a cfClassId of Supervising
publication_date multi cfResPubl/cfResPublDate This lists the dates upon which the person published any result publications. This is a catch-all for all types of publication. Individual publication types are broken down in the following index fields
publication_id multi cfResPubl/cfResPublId This lists the ids of all the publications of any kind which the person published.
journal_date multi cfResPubl/cfResPublDate This is the list of dates of publication of all publications which have a cfClassId of “Journal Article”.
journal_id multi cfResPubl/cfResPublDate This is the list of ids publications which have a cfClassId of “Journal Article”.
book_date multi cfResPubl/cfResPublDate This is the list of dates of publication of all publications which have a cfClassId of “Book”.
book_id multi cfResPubl/cfResPublDate This is the list of ids publications which have a cfClassId of “Book”.
chapter_date multi cfResPubl/cfResPublDate This is the list of dates of publication of all publications which have a cfClassId of “Inbook”.
chapter_id multi cfResPubl/cfResPublDate This is the list of ids publications which have a cfClassId of “Inbook”.
conference_date multi cfResPubl/cfResPublDate This is the list of dates of publication of all publications which have a cfClassId of “Conference Proceedings Article”.
conference_id multi cfResPubl/cfResPublDate This is the list of ids publications which have a cfClassId of “Conference Proceedings Article”.

These terms are encoded in a formal schema for Solr which can be found here.

Data Import

Apache Solr provides what it calls “Data Import Handlers” which allow you to import data from different kinds of sources into the index. Once we have configured the index as per the previous section we can construct a Data Import Handler which will import from the CERIF MySQL database.

This is effectively a set of SQL queries which are used to populate the index fields in the ways described in the previous section. A representitive example of the kinds of query include:

SELECT cfPers.cfPersId, cfPers.cfGender, 'cfPers' AS entity
FROM cfPers 
    INNER JOIN cfPers_Class 
        ON cfPers.cfPersId = cfPers_Class.cfPersId 
WHERE cfPers_Class.cfClassSchemeId = 'BRUCE' 
    AND cfPers_Class.cfClassId = 'Main';

This query is at the root of the Data Import Handler, and selects our cfPersId which will be the central identifier that we will use to retrieve all other information, as well as any information which we can quickly and easily obtain by performing a JOIN operation across the cfPers* tables.

SELECT concat(cfFamilyNames, ', ', cfFirstNames, ' ', cfOtherNames) AS cfName 
FROM cfPersName 
WHERE cfPersId = '${person.cfPersId}'

This query selects the first person’s name and performs the appropriate concatenation to turn the three name parts cfFamilyNames, cfFirstNames and cfOtherNames into a usable single string.

SELECT cfEndDate 
FROM cfPers_OrgUnit
WHERE cfPersId = '${person.cfPersId}'
    AND cfClassId = 'Employee' 
    AND cfClassSchemeId = 'cfCERIFSemantics_2008-1.2';

This query selects the person’s contract end date by looking for the organisational unit to which the person’s relationship (cfPers_OrgUnit) is annotated with the cfClassId ‘Employee’.

SELECT cfResPubl.cfResPublId, cfResPubl.cfResPublDate 
FROM cfResPubl 
    INNER JOIN cfPers_ResPubl 
        ON cfPers_ResPubl.cfResPublId = cfResPubl.cfResPublId
    INNER JOIN cfResPubl_Class
        ON cfResPubl.cfResPublId = cfResPubl_Class.cfResPublId
WHERE cfPers_ResPubl.cfPersId = '${person.cfPersId}'
    AND cfResPubl_Class.cfClassSchemeId = 'cfCERIFSemantics_2008-1.2'
    AND cfResPubl_Class.cfClassId = 'Journal Article';

This query selects the ids and dates of publications by the selected person which have a class of ‘Journal Article’.

Here we will not go into this at any further length; instead the code which provides the Data Import functionality can be obtained here.

It is probably worth noting, though, that these queries are quite long and involve JOINing across multiple database tables, which makes reporting on the data hard work if done directly from source. The BRUCE approach means that this is all compressed into one single Data Import Handler, and leaves all the exciting stuff to the much simpler search engine query.

Use of the index

Once we have produced the index, we feed it into SolrEyes (discussed in more detail here) which is configured to produce the following functionality based on the indexed values:

Field Usage
entity facet
id unused, required for index only
gender facet, result display
name sort, result display
name_variants currently unused
contract_end facet, sort, result display
funding_code result display
org_unit_name currently unused
org_unit_id currently unused
primary_department sort, result display
primary_department_id currently unused
primary_position facet, result display
fte facet, sort, result display
supervising result display (a function presents the number of people being supervised by the person)
publication_date facet, result display (a function counts the number of publications in between the date ranges specified by the facet)
publication_id currently unused
journal_date result display (a function counts the number of journal articles in between the date ranges specified by the publication_date facet)
journal_id currently unused
book_date result display (a function counts the number of journal articles in between the date ranges specified by the publication_date facet)
book_id currently unused
chapter_date result display (a function counts the number of journal articles in between the date ranges specified by the publication_date facet)
chapter_id currently unused
conference_date result display (a function counts the number of journal articles in between the date ranges specified by the publication_date facet)
conference_id currently unused


used to create the faceted browse navigation
result display
used when presenting a “document” to the user. Sometimes the value is a function of the actual indexed content.
used for sorting the result set

Note that a more thorough treatment of the Solr index would split the fields up into multiple indexed fields which are customised for their purposes, but that we have not done this in the prototype. For example, fields used for sorting will go through normalising functions to ensure consistent sorting across all values, while displayable values will be stored unprocessed.

We can now produce a user interface like that shown in the screen shot below.

The approach used here could be used to extend to more features of the person Base Entity, but also other Base Entities (and, indeed, any entity in the CERIF model) could be placed at the centre of the report, and its resulting hierarchy of properties mapped into a set of key-value pairs, and all could co-exist comfortably in the same search index.


August 19, 2011

SolrEyes is a basic but effective wrapper around Apache Solr which has been developed by the BRUCE project as a replacement for Blacklight.

As per our previous post, significant problems were had in stabilising Blacklight, so a brief exploratory was carried out in attempting to replicate that functionality which was necessary to the project (not all of the existing functionality was necessary for our needs). Having been successful we went on to introduce support for ranged facets, allowing us to limit by date or any other rangeable field.


SolrEyes uses the SolrPy Python library to communicate with Apache Solr and the Mako templating language to provide the User Interface. It presents the results of search requests to the user with facet counts and current search parameters alongside the search results themselves. It allows for facets to be added and removed, as well as allowing for sorting and sub-sorting and fully supports flexible paging over result sets.

Note that the data presented in these screen shots is artificial, and should not be considered indicative of anything.

It is strongly inspired by Blacklight, and provides all of the basic search and facet functionality from that system. In addition, the configuration has been done as a JSON document which makes it easier to separate from the application and to modify and extend.

Reporting features

A key difference between SolrEyes and Blacklight is that SolrEyes comes pre-prepared for some reporting features. Every search constraint and every search result field is passed through a processing pipeline which converts it from the value in the Solr index into the display value, and that pipeline is created in configuration by the user.

In the simplest case, this allows us to switch the indexed value M in the gender field for the word Male before it is displayed. This is done by specifying that the facet values for the gender field should be passed to a function called value_map which maps M to Male and F to Female:

“facet_value_functions” : {
    “gender” : {“value_map” : {“M” : “Male”, “F” : “Female”}},

This shows a configuration option which exchanges facet values for display values in the gender field.

This approach could also be used, though, to substitute date ranges for descriptions, such as “RAE 2008”, or other useful terms.

At the more powerful end of the spectrum, though, this feature can be used to process result values themselves to present information as functions of those values. An example of the way this is used in BRUCE is as follows:

We wish to present counts of the number of publications that researchers have published in the reporting period. The reporting period can be set by choosing the appropriate date range from the navigation (this constrains the publication_date field to contain values from that range). This means that we cannot index this data in advance, as it is dependent on the exact date range that the user selects, which could be absolutely anything. Instead we pipe the date range selected and a result field which contains the dates on which the user published to a function which can compare those publication dates with the constraint range and return a count of those publications which fall within it. In order to achieve this effect the documents in our index contain a list of the dates upon which the author published.

“dynamic_fields” :{
    “period_publications_count” : {
        “date_range_count” : {
            “bounding_field” : “publication_date”,
            “results_field” : “publication_date”

This shows a configuration of a “dynamic field” which presents the count of values in the index field publication_date which fall within the constraining facet publication_date.

This screenshot shows a single record which has been constrained to all publications from a single year (see the green box which displays the constraint). The final 6 result columns contain values which are dynamically generated by comparing the publication dates of the different publication types with that constraint. So, here, S W Burnham is seen to have published 2 items in 1880: 1 Book and 1 Conference Paper.

External Take Up

SolrEyes has proved sufficiently simple to operate and configure while providing useful functionality that it has also had some take-up outside of the project.

The functionality was designed deliberately to be flexible to other use cases (although the reporting use cases were the ones focussed upon by the project team), and as such it has also found use as a front-end for a bibliographic data index.

The Open Bibliography project (which provided the MedLine data that the BRUCE project built the CERIF test data from), the OKF and Cottage Labs are also involved in the development of the BibJSON standard and related BibServer software which powers the under-development BibSoup service. This service is using SolrEyes to operate the search and faceted browse features, and so the software is already getting feedback and enhancements from external developers.

We hope that SolrEyes fulfills a niche for a simple but powerful interface to Apache Solr. Its advantages over Blacklight and VuFind are in the simplicity of the environment and a generic approach to presenting the contents of a search index (both Blacklight and VuFind are more geared towards providing catalogue interfaces).

Using SolrEyes

The SolrEyes software can be downloaded here.

To use SolrEyes successfully, it requires the most recent development version of Solrpy (we found a bug in 0.9.4 and submitted a patch which was accepted, but which has not yet been packaged in a formal release). You can install the latest version with (all on one line):

sudo pip install -e hg+

You will also need to install and mako which you can do with easy_install:

sudo easy_install
sudo easy_install mako

Next go into the directory where you downloaded SolrEyes and modify the config.json file with your custom configuration (documentation is inline).

Finally, you can start SolrEyes by executing:

python 8080

This will start SolrEyes on port 8080 on localhost.

We are very interested in taking the development of SolrEyes forward so please contact us if you have any questions, feedback or suggestions.

CERIF Test Data

August 17, 2011

Due to the privacy and data protection status of the real research information at Brunel – which includes data such as pay scales and so forth – it is not possible for the project to demonstrate its tools to people who are not Brunel employees (at the very least). Furthermore, that data cannot even be taken off-site or placed onto computers which are not under direct control of the university. Combine this with the need within the project for two parallel development tracks: one mapping source data (such as HR and publications) into CERIF and the other indexing and reporting on that CERIF data, and there is a compelling need for a test dataset.

A test CERIF dataset could be used in any demonstrations of the project outputs, and could be put in-place for the CERIF indexing side of the project so that it is not critically dependent on the outputs of the data mapping side.

Initially we had hoped that such a dataset already existed, but there was nothing available on the euroCRIS website (the CERIF guardian organisation) and extensive searching turned up nothing of value. There are other JISC projects which may ultimately have yielded some useful data (such as CERIFy), but they are also running in parallel to BRUCE.

The project therefore developed a piece of software which can be used to generate test data, and has made it available open source here (in the cerifdata folder at that link).

The approach to developing the test data and the software were as follows:

1. Identify a seed dataset

We were lucky that at exactly the time that we were seeking for a seed dataset, the Open Bibliography project – also JISC funded – had succeeded in liberating the MedLine dataset consisting of around 20 million publication records.

This was an ideal source of the most difficult data to artificially generate: author names and publication titles. By using this dataset as our seed we would be able to generate artificial research data based on open access bibliographic data, which would give us the freedom necessary to do as we needed with the dataset at the same time as making it look suitably realistic.

2. Define the model we are populating

Although actually done in several iterations, the model we worked towards was as presented in a previous post.

This meant generating data about Staff, Organisational Units and Publications. We have only written code to generate the data required for our example model, but we have endeavoured to write the software itself in a way which allows it to be extended throughout the project and into the future.

3. Develop a flexible production mechanism

The test data is generated by the following process:

First, source data is obtained from the MedLine data file. This source data is then passed through a set of CERIF data “aspect generators” which produce CERIF entites and relationships (such as staff records and their relationships to organisational units and publications). These are then written to CSVs which reflect the database table structure in the CERIF SQL schema. The CSVs are finally converted into a single large SQL file suitable for import into a database.

The architecture of the software is designed to be flexible so that new aspects can easily be added and existing aspects can easily be modified.

4. Produce the test data

We simply provide one of the MedLine source data files to the program and it will generate our test data in SQL format for us:

python /path/to/medline.xml

Which produces the CSVs:

$ ls *.csv

For example, the following data are all related through a single person (cfPers):

f0b2517b-4b65-4fa5-b562-ff931cd213f2, F

f0b2517b-4b65-4fa5-b562-ff931cd213f2, Teresa, J, Krassa

f0b2517b-4b65-4fa5-b562-ff931cd213f2, MM122

f0b2517b-4b65-4fa5-b562-ff931cd213f2, 1, cfCERIFSemantics_2008-1.2, Employee, 1.0, 2019-10-05
f0b2517b-4b65-4fa5-b562-ff931cd213f2, 3, cfCERIFSemantics_2008-1.2, PhD, 1.0, 2019-10-05
f0b2517b-4b65-4fa5-b562-ff931cd213f2, 1, cfCERIFSemantics_2008-1.2, Member, 1.0, 2019-10-05
f0b2517b-4b65-4fa5-b562-ff931cd213f2, 2, cfCERIFSemantics_2008-1.2, Member, 1.0, 2019-10-05
f0b2517b-4b65-4fa5-b562-ff931cd213f2, 3, cfCERIFSemantics_2008-1.2, Member, 1.0, 2019-10-05

f0b2517b-4b65-4fa5-b562-ff931cd213f2, 1936bdc4-aadd-4028-bb9e-b9eec2561c00, cfCERIFSemantics_2008-1.2, Author

This shows us a person with ID f0b2517b-4b65-4fa5-b562-ff931cd213f2 who is Female (from cfPers.csv) who has the name Teresa,J,Krassa (from cfPersName.csv) who has funding from funding code MM122 (from cfPers_Fund.csv), who is an Employee of Organisational Unit 1, is a PhD Student in Organisational Unit 3 and is a Member of Organisational Units 1, 2 and 3 (from cfPers_OrgUnit.csv). It also shows that this person is the Author of a Result Publication with ID 1936bdc4-aadd-4028-bb9e-b9eec2561c00 (from cfPers_ResPubl.csv).

This repeats with variations on the data across the entire seed dataset, giving us a rich spread of people, publications, organisational units and relationships between them upon which to carry out our development, testing and demonstrations.

These CSVs are then converted into a single SQL file, which can then be imported into our MySQL database and used.

If you wish to use the software yourself, you can download it from the version control but unfortunately at time of writing the MedLine data in the format required by the program is not publicly available. It is available as n-quads on CKAN and the project is discussing with Open Bibliography the possibilities of publishing the data in its original format also. In the mean time, please feel free to contact us and we will be able to help you obtain the data in the relevant format.

3rd BRUCE Steering Group Meeting

July 22, 2011

The Project Steering Group, chaired by Professor Geoff Rodgers the Pro Vice Chancellor (Research) at Brunel, met for the third time on Thursday 21st July.  The minutes of the second meeting (Minutes_BPSG2) were agreed and, as agreed by the Group, are now being made public here.

Technical Team Update

June 16, 2011


At this stage in the project our main objective is to implement a “vertical” slice of the research reporting process, by taking some source data, mapping it into CERIF, storing it in a CERIF compliant database and then indexing that data with Apache Solr for display and interaction via Blacklight, which will ultimately be used to generate reports on the research information. There are a number of challenges involved in this process:

  • How to map the data sources such as HESA, SITS, HR and Publications data into CERIF. In some cases there will be clear mappings, and in other some creativity may be required, and in yet others it may not be possible.
  • How to turn the complex relational schema that is CERIF into a flat, indexable, set of key/value pairs which can be used by Solr and make sense to the user of the reporting software
  • How to configure Solr
  • How to configure Blacklight

Status Update

At the moment we have the following technical outputs from the project:

  • A test CERIF dataset created using the Open Biblio project’s Medline dataset as the seed data
  • A MySQL CERIF schema which was acquired from euroCRIS
  • A theoretical mapping from the datasources to CERIF (not yet implemented)
  • A set of Solr configuration files and data importers which relate the MySQL CERIF database to a set of flat key/value pairs which meet the requirements of the project’s exemplar report. No general configuration has been produced for CERIF yet, as we are focussed on this specific vertical.
  • Some installation and configuration experience with Blacklight. We have done a number of demonstrations of Blacklight to investigate what the final interface will look like, but as yet no realistic data has been presented through it.
  • A high-spec dedicated project server with the capacity for storing and processing the large quantites of data that will be generated throughout has been installed and is ready to start working with the data.

Experiences with CERIF

Overall, mapping data to and from CERIF has not been too troublesome. It is a relational standard, which means that flattening it for Solr has been a bit tricky (more on that later). In addition, it does not always have clear ways of representing the data we want to represent, and it appears that the Semantic Layer is where most of the complexity will ultimately reside.

Experiences with Solr

Solr has been reliable (if complex to configure) throughout the process, and the project team is now comfortable and confident that it meets most if not all of the requirements that will be placed on it.

Experiences with Blacklight

Blacklight has so far been the weak link in the project. It is extremely difficult to install and configure, and no two installations go the same way so a large amount of time has been sunk in trying to make it work at all. It is partly for this reason that the project is not yet displaying the data from Solr in Blacklight.

Flattening CERIF for Solr

As CERIF is a relational format, flattening it for indexing by Solr has been a careful task for the project. We cannot represent all of the data in the CERIF database exactly as it appears in MySQL, since Solr does not strictly have the relational qualities of a database.

Instead we have begun to construct solr documents (effectively these are Object Classes) which are designed to meet the reporting requirements. That is, for our exemplar report (see linked presentation), which is focussed on the individuals, we create Solr documents which have the person as the key entity, and we add to the document extensive information about the organisational units that the person is part of, their publications, and so on.

Later we will construct documents which are designed to meet other reporting requirements, and may therefore be organisation or publication oriented. With a well designed Solr schema, all these different documents will co-exist comfortably side-by-side in the index, and we’ll be able to generate a variety of different kinds of report based on that data.

Next Steps

  • Finalise the datasource mappings to CERIF
  • Harden the CERIF to Solr indexing process based on the final datasource mappings
  • Get Blacklight to behave
  • Generate reports from search results. The the project is looking at Prawn, a rails application which can generate PDFs of the results.

Solr Cheat Sheet

May 24, 2011

This is a very small list of useful Solr URL parameters. It’s mostly for the benefit of the project group, but you might find it useful too!

q : q=* or q=*:*
The basic query parameter. In this field you can put your full Solr query. If you are using the dismax query type (see below) then you can only put freetext searches in here (like q=whatever), otherwise you can construct full Lucene queries (like q=author:richard). If you are using the dismax query type, use q.alt for full Lucene power instead.
q.alt : q.alt=*:*
For use with the dismax query type, this allows you to do a full Lucene query
fl : fl=title,author,score
Field List. The list of fields to be returned in the result set. In addition to those fields in the Solr schema you can also specify score which will give you the relevance rank that Lucene allocated the result document
sort : sort=title asc
Sort field and direction. Specify a field followed by a space followed by the direction (desc/asc). You can also specify multiple sort fields, and present them here in the order that you want to sort them by; so sort=title asc,author desc and so on.
defType : defType=dismax
Specify the query type. In particular, the dismax is very useful for freetext searches. See for details. When using dismax the q parameters will only work for freetext searching, and q.alt should be used for full Lucene query power.
facet : facet=on
Turn facets on or off. If on, then the fields specified by the facet.field parameters will be returned
facet.field : facet.field=author
Return along with the result set a facet count for the author field. This will only have an effect if facet=on is also specified. You can specify multiple facet.field parameters as separate URL arguments: facet.field=author&facet.field=year&facet.field=subject

Obviously the full list of URL parameters for Solr is much larger, and we’ll add to this cheat sheet the parameters which we think are the most useful as we go through the project.