Anchor Modeling

The online modeling tool has been extended with new functionality that keeps a history of how the anchor schema has evolved over time. In Anchor Modeling all previous schemas are available as subsets of the current schema. Now, using this new functionality all previous schemas are also accessible through the _Schema table. On top of this table four views have been added that shred the XML of the schema into rows and columns: _Knot, _Anchor, _Attribute, and _Tie, through which details about the tables can be followed over time. A utility function, _Evolution(@timepoint), that compares the tables in the current database with the schema that was in effect at the given @timepoint is also available. To see this in action, please watch our new tutorial available in our Tutorials section, or by clicking here.

We will be speaking about Anchor Modeling in a presentation named “New trends in data modeling” at the GSE Nordic Region Conference, 2011 (7-9 June), in Stockholm, Sweden.

Here is an excerpt from the abstract:

We need to rethink the concept of a data model. We live in a world where the available information changes more rapidly for every day that passes. This is true both for the content and structure of the information. On top of that, search requirements are also becoming inherently harder to predict. In such a turbulent environment, current data models do not stand the test of time…

We have added some new tutorials that highlight recent features in the anchor modeler (the online Anchor Modeling tool). Among these are the naming convention and how the tool enforces this, context sensitive keyboard shortcuts for modeling operations, and a discussion on the layout algorithm and some of the options affecting it. You can find them under the “Tutorials” menu, or by clicking here.

Due to a change in the Google App Engine backend for handling logins, the Anchor Modeler is currently unable to connect to the cloud. We expect to have fixed this early next week. We apologize for any inconveniences this may cause.

Update March 7, 2011:
The issue has now been resolved.

We are happy to announce that our journal article has been published in the December 2010 Special Edition issue of Data & Knowledge Engineering. The final version of the paper can be found by clicking here.

A guest lecture has been held at DSV, Stockhom University, for the Data Warehousing class. This year the lecture was split over two sessions in which the basics of Anchor Modeling was covered. The online modeling tool was also introduced and an assigment was given to the students in which they will create a simple model. In other words, close to 200 students will help out with the testing of the tool.

The Anchor Modeler (https://www.anchormodeling.com/modeler) is now connected to the Cloud for loading and saving models. Models can also be saved as “public” and become viewable by everyone. The connection to the Cloud is done using Google’s App Engine, so all you need is a Google account to use it. Come share your models!

We are happy to announce that our journal article “Anchor Modeling – Agile Information Modeling in Evolving Data Environments” for the Special Edition Issue of Data & Knowledge Engineering has been accepted for publication. A preprint of the article can be read by clicking here.

Summary

Maintaining and evolving data warehouses is a complex, error prone, and time consuming activity. In order to support the design and maintenance of data warehouses, we have developed a novel information modeling technique, called Anchor Modeling. A key benefit of Anchor Modeling is that changes in a data warehouse environment only require extensions, not modifications, to a data warehouse. This makes it possible to iteratively model and apply fragments of a data warehouse, which supports agile ways of working. Furthermore, Anchor Modeling results in databases that in many situations perform substantially better than databases constructed using traditional modeling techniques.

The automatically generated insert triggers on the latest views have been simplified such that attribute metadata values default to the anchor metadata value if not specified. This makes insertion into the view (in order to create a new instance of the anchor and all attributes) as simple as the following code shows:

insert into lAC_Actor (
    Metadata_AC,
    AC_NAM_Actor_Name,
    AC_NAM_ValidFrom,
    GEN_Gender,
    PLV_ProfessionalLevel,
    AC_PLV_ValidFrom
)
values (42, 'Lars S', '1972-08-20', 'Male', 'Master', '2009-09-21'); 

If there is a need to ‘update’ the name this can also be done using the insert trigger on the latest view (given that the surrogate key generated for AC_ID in the previous step was 1):

insert into lAC_Actor (
    AC_ID, 
    AC_NAM_Actor_Name, 
    AC_NAM_ValidFrom,
    Metadata_AC_NAM
)
values (1, 'Lars R', '2004-06-19', 43);

We are proud to announce that our Minified Layout Engine reached 7^th place in the JS1k competition, out of 427 submitted demos. We will be eagerly awaiting some of the nice prices coming our way. The full engine is used in our online modeling tool Anchor Modeler, and has many more features.