While purusing through Zite, I came across this blog post on the Design Instruct web site by Jacob Gube. Jacob is the co-founder and a managing editor of Design Instruct. He’s a web developer, and also the owner of Six Revisions. Follow Jacob on Twitter: @sixrevisions.
6 Ways to Increase the Visual Weight of Something
In a design composition, the visual weight of an object refers to how well it draws attention to itself compared to other components of the composition. The “heavier” the object is, the more eye-grabbing it is.
When creating a design, it’s a good idea to prioritize key elements in the visual space by giving them heavier visual weights. For example, things you might consider giving heavier visual weights to — so that they’re more easily seen by the viewer — are call-to-action buttons in a web design, or the subject of a photograph.
I’ll talk about a few tricks for increasing the visual weight of an object.
1. Give It a Different Color
When the color-contrast between an object and its surroundings (including its background) is high, the more able it is to garner our attention.
In the example above, notice how, even though the size, shape and margins of the stars are identical, the red star is able to get your attention simply because of how distinctive its color is compared to other elements in the composition.
2. Move It Away from Other Objects
One easy trick for increasing the visual weight of an object is distancing it from other objects. Adding plenty of negative space around the object separates it from other objects, which in turn makes the object stand out.
In the example above, look at how our eyes interpret the composition as two groups of rabbits: A big group of 12 rabbits and a small group consisting of only one rabbit. By being farther away from the others, the estranged rabbit is able to command our attention more than any other rabbit in the composition.
3. Make It Look Different
When things look alike, it’s naturally hard for us to differentiate them. So, quite simply, we can make the visual weight of an object heavier by making it look different from other objects.
Even a slight change in the style properties of an object can heavily influence its visual weight if objects in the composition look similar. In the above example, notice how the circle at the center of the first row is able to get our eyes’ attention compared to the other circles.
4. Point to It
A simple trick for increasing the visual weight of something is to direct the viewer’s eyes to it using visual queues such as arrows.
In the above example, check out how the visual weight of the house is increased because it’s surrounded by arrows that point to its location. No matter where our attention goes, we’re redirected to look at the house because of the arrows.
5. Make It Look Visually Complex
An ornate object attracts our eyes more when it’s set among simple and unadorned objects. We can make the appearance of an object complex by giving it textures, drop shadows, changing its shape, adding more color to it, and so forth.
In the example above, the multi-colored circle has the heaviest visual weight because the surrounding objects are styled plainly.
6. Make It Bigger
Making an object larger than the other objects around it will increase its visual weight. It’s a reasonable proposition: The more visual space an object takes up, the more visible it is.
In the example above, notice how our eyes are quickly drawn to the biggest heart . The only thing different with it is its size.
Visual weight is a simple but incredibly powerful design tool for strategically arranging elements so that more important elements are readily seen by our viewers.
What tricks do you use to increase the visual weight of an object? Share your advice in the comments.
In Part 5 of this series on data blending, we reviewed Tableau’s Data Blending Architecture. With Part 5, I have wrapped up the Tableau portion of this series.
I am now going to post, over the next week or so, several parts discussing how we do data blending using MicroStrategy. Fortunately, MicroStrategy just publish a nice technical note on their Knowledgebase (TN Key: 46940)  discussing this. Most of what I am sharing today is derived from that technical note.
I probably will have 2-4 parts for this topic in my Data Blending series including how the MicroStrategy Analytical Engine deals with multiple datasets.
I want to thank Kristi Morton (et al) for the wonderful research paper she wrote at The University of Washington . It helped me provide some real insight into the topic and mechanics of data blending, particularly with Tableau. You can learn more about Ms. Morton’s research as well as other resources used to create this blog post by referring to the References at the end of the blog post.
So, let’s now dig into how MicroStrategy provides us data blending capabilities.
Data Blending using MicroStrategy
In Part 6, we will begin examining using data blending in MicroStrategy. We will first look at how to use attributes from multiple datasets in the same Visual Insight dashboard and link them to existing attributes using the Data Blend feature in MicroStrategy Analytics Enterprise Web 9.4.1.
Prior to v9.4.1 of MicroStrategy, data blending was referred to as Cube Joining.
In MicroStrategy Analytics Enterprise Web 9.4.1, the new Report Services Documents Engine automatically links common attributes using the modeled schema whenever possible. The manual linking is not allowed between different modeled attributes. Just in case the requirement needs to link different attributes, this can be done by using MicroStrategy Architect at the schema level. The join behavior by default for linking related attributes is done using a full outer join. In case there is no relationship between the attributes, then a cross join is used.
The manual attribute linking can be done as shown in the images below.
2. Browse the file to match the existing data and select Continue.
3. Set the attribute forms if needed. MicroStrategy will automatically assign the detected ones.
4. The attributes can be mapped manually by selecting Link to Project Attribute.
5. Select the attribute form that matches the desired join:
6. The attribute should appear similar to the ones existing in the schema as shown below.
7. Save the recently created dataset.
8. Now there are two cubes used as datasets in the same Visual Insight dashboard, as shown below.
The attributes icons now have a blue link, as shown below. This indicates that MicroStrategy has automatically linked them to elements in the Information dataset.
Next: How Data Blending Affects the Analytical Engine’s Behavior in MicroStrategy
 MicroStrategy Knowledgebase, How to use attributes from multiple datasets in the same Visual Insight dashboard and link them to existing attributes using the Data Blend feature in MicroStrategy Analytics Enterprise Web 9.4.1, TN Key: 46940, 04/24/2014, https://resource.microstrategy.com/support/mainsearch.aspx.
NOTE: You may need to register to view MiroStrategy’s Knowledgebase.
 Kristi Morton, Ross Bunker, Jock Mackinlay, Robert Morton, and Chris Stolte, Dynamic Workload Driven Data Integration in Tableau, University of Washington and Tableau Software, Seattle, Washington, March 2012, http://homes.cs.washington.edu/~kmorton/modi221-mortonA.pdf.
Starting with MicroStrategy 9.2.1, Intelligent Cubes have a new feature to update information without republishing it. It is referred to as Incremental Refresh. There are different ways each Incremental Refresh type option works.
Defining an Incremental Refresh Report
Prior to MicroStrategy 9.2.1, if the data in an Intelligent Cube needed to be updated, users had to re-publish the Intelligent Cube, either manually or using a schedule. This process will cause all the data for the Intelligent Cube to be loaded from the data warehouse into Intelligence Server’s memory, so that the existing data for the Intelligent Cube is overwritten.
MicroStrategy 9.2.1 introduced a new feature known as Incremental Refresh Options, which allow Intelligent Cubes to be updated based on one or more attributes by setting up incremental refresh settings to update the Intelligent Cube with only new data. This can reduce the time and system resources necessary to update the Intelligent Cube periodically.
For example, if a user has an Intelligent Cube that contains weekly sales data, the user may want this Intelligent Cube to be updated at the end of every week with the sales data for that week. By setting up incremental refresh settings, he can make it so that only data for one week is added to the Intelligent Cube, without affecting the existing data and without having to reload all existing data.
Users can select two types of objects for the incremental fetch: a report or a filter.
- Filter: The data returned by a filter is compared to the data that is already in the cube. By default, the filter defined for the Intelligent Cube is used as the filter for the incremental refresh.
- Report: The results of a report are used to populate the Intelligent Cube. By default, the report template used is the same as the Intelligent Cube’s template.
In order to set up an incremental refresh report, the user should first right-click on the Intelligent Cube and select Define Incremental Refresh Report:
This will bring up the Incremental Refresh Options editor:
Here, the user can define one of the following Refresh type options:
- Update: If new data is available, it is fetched and added to the Intelligent Cube, and if the data returned is already in the Intelligent Cube, it is updated where applicable.
- Insert: If new data is available, it is fetched and added to the Intelligent Cube. Data that was already in the Intelligent Cube is not altered.
- Delete: The data that meets the filter or report’s definition is deleted from the cube. For example, if the Intelligent Cube contains data for 2008, 2009 and 2010, and the filter or report returns data for 2009, all the data for 2009 is deleted from the cube.
- Update only: If the data available is already in the Intelligent Cube, it is updated where applicable. No new data is added to the Intelligent Cube.
The type of object used for the incremental fetch can be selected in the Advanced tab:
Users simply have to run the incremental fetch report, and this will automatically refresh the data in the Intelligent Cube.
Incremental Refresh Options Examples
In this example, the following database table is used. This is a transaction table for item, status, quantity sold (qty_sold) and transaction number.
– Transaction Number
Filter: Transaction Number greater than or equal to 100
Data is updated as below on the database side:
Line 2 – qty_sold number is updated
Line 3 – status is altered from confirmed to canceled
Line 4 – newly added
Line 5 – newly added
Insert new rows from report data and overwrite overlapping rows between old cube data and report data.
Line 2 – qty_sold number is updated.
Line 3 – Status canceled row is newly inserted, and line 4, the original data is not modified. For any change for any other attribute, a new line is added and the previous line also persists.
Line 5 – Newly added transaction is inserted.
And the new data with transaction_number 1 is not added because it does not meet the filter criteria to have transaction_number >= 100.
Only insert new, non-overlapping rows from report data.
Line 2 – qty_sold number is NOT updated.
Line 3 – Status canceled row is newly inserted.
Line 5 – Newly added transaction is inserted.
And the new data with transaction_number 1 is not added.
Remove overlapping rows from old cube data.
Delete Incremental Refresh report is not executed against the warehouse, and executed for Intelligent Cube with the following query. All the data meeting the criteria is deleted.
Delete from CUBE IncrementalRefreshTestwhere [Transaction Number]@[transaction_date] >= 100
Only overwrite overlapping rows from report data.
Line 2 – qty_sold number is updated.
In summary, when defining an Incremental Refresh report, take the following behavior into consideration.
- Update/Update only option does not compare all the attribute elements.
- Delete option is performed on the Intelligent Cube, and data is not compared with the warehouse.