Cfa Level 1 Details
Jamey Saldana
This article explains how level of detail expressions are computed and how they function in Tableau. For more information about LOD expressions and how they work, see the Understanding Level of Detail (LOD) Expressions(Link opens in a new window) whitepaper on the Tableau website.
In Tableau, expressions referencing unaggregated datasource columns are computed for each row in the underlying table. In this case, the dimensionality of the expression is row level. An example of a row-level expression is:
This calculation will be evaluated in each row of the database. For each row, the Sales value in that row will be divided by the Profit value in that row, producing a new column with the result of the multiplication (a profit ratio).
By contrast, expressions referencing aggregated data source columns are computed at the dimensionality defined by the dimensions in the view. In this case, the dimensionality of the expression is view level. An example of a view-level expression is:
Level of detail expressions that reference floating-point measures can behave unreliably when used in a view that requires comparison of the values in the expression. For details, see Understanding data types in calculations(Link opens in a new window).
FIXED level of detail expressions can result in measures or dimensions, depending on the underlying field in the aggregate expression. So MIN([Date])} will be a dimension because [Date] is a dimension, and fixed Store : SUM([Sales]) will be a measure because [Sales] is a measure. When a FIXED level of detail expression is saved as a measure you have the option of moving it to dimensions.
FIXED calculations are applied before dimension filters, so unless you promote the fields on your Filter shelf to Use Context Filters, they will be ignored. For example, consider if you have the following calculation on one shelf in a view, along with [State] on a different shelf:
An expression has a coarser level of detail than the view when it references a subset of the dimensions in the view. For example, for a view that contained the dimensions [Category] and [Segment], you could create a level of detail expression that uses only one of these dimensions:
In this case, the expression has a coarser level of detail than the view. It bases its values on one dimension ([Segment]), whereas the view is basing its view on two dimensions ([Segment] and [Category]).
Replicated values are useful for comparing specific values against average values within a category. For example the following calculation subtracts average sales for a customer from the average sales overall:
An expression has a finer level of detail than the view when it references a superset of the dimensions in the view. When you use such an expression in the view, Tableau will aggregate results up to the view level. For example, the following level of detail expression references two dimensions:
FIXED level of detail expressions can have a finer level of detail than the view, a coarser level of detail, or the same level of detail. The need to aggregate the results of a FIXED level of detail depends on what dimensions are in the view.
EXCLUDE level of detail expressions always cause replicated values to appear in the view. When calculations including EXCLUDE level of detail expressions are placed on a shelf, Tableau defaults to the ATTR aggregation (as opposed to SUM or AVG) to indicate that the expression is not actually being aggregated and that changing the aggregation will have no effect on the view.
Cross join occur when there is no explicit field to join on. For example, for a level of detail expression fixed [Product Type] : sum(sales) when the view only contains one dimension [Ship Mode], Tableau creates a cross-join. A cross join produces rows which combine each row from the first table with each row from the second table.
Level of Detail expressions (also known as LOD expressions) allow you to compute values at the data source level and the visualization level. However, LOD expressions let you control the granularity you want to compute. They can be performed at a more granular level (INCLUDE), a less granular level (EXCLUDE), or an entirely independent level (FIXED).
You can now see both the sum of all sales and the average sales per customer for each region. For example, you can see that in the Central region, the sales totaled approximately $500,000 with an average sale for each customer being approximately 800 USD.
The aggregation in the aggregate expression will come from the default aggregation on the measure. This is usually SUM. To change the aggregation or otherwise edit the LOD, right click on the new field and edit the calculation.
The aggregate expression is the calculation that is performed. For example, SUM(Sales) or AVG(Discount). The results of the calculation in the aggregate expression depend on the dimension declaration and keyword.
The view level of detail is [Region] and [State]. But FIXED level of detail expressions don't look at the dimensions in the view, only the dimensions specified in the calculation (here, Region). Therefore, the values for the individual states in each region are identical. For more information, see Aggregation and Level of Detail Expressions.
If the keyword had been INCLUDE instead of FIXED, the values would be different for each state. INCLUDE uses the dimension in the expression ([Region]) and any additional dimensions in the view ([State]) when evaluating the expression.
INCLUDE can be useful when you want to calculate at a fine level of detail in the database, but reaggregate at a coarser level of detail in your view. Fields based on INCLUDE level of detail expressions change as you add or remove dimensions from the view.
When Segment is added to the Columns shelf and the calculation is moved to Label, the LOD expression results update. Now you can see how the average sum of sales per state varies across categories and segments.
This page contains information on level of detail (LOD) for meshes. For information on LOD for shadersA program that runs on the GPU. More info
See in Glossary, see SubShader LOD value.
The LOD technique allows Unity to reduce the number of triangles it renders for a GameObject based on its distance from the Camera. To use it, a GameObject must have a number of meshes with decreasing levels of detail in its geometry. These meshes are called LOD levels. The farther a GameObject is from the Camera, the lower-detail LOD level Unity renders. This technique reduces the load on the hardware for these distant GameObjects, and can therefore improve rendering performance.
To use LOD, you must have a GameObject with a LOD GroupA component to manage level of detail (LOD) for GameObjects. More info
See in Glossary component. The LOD Group component provides controls to define how LOD behaves on this GameObject, and references the GameObjects that Unity shows or hides for each LOD level. See LOD Group for details on the properties in this component.
In computer graphics, level of detail (LOD) refers to the complexity of a 3D model representation.[1][2][3] LOD can be decreased as the model moves away from the viewer or according to other metrics such as object importance, viewpoint-relative speed or position.LOD techniques increase the efficiency of rendering by decreasing the workload on graphics pipeline stages, usually vertex transformations.The reduced visual quality of the model is often unnoticed because of the small effect on object appearance when distant or moving fast.
Although most of the time LOD is applied to geometry detail only, the basic concept can be generalized. Recently, LOD techniques also included shader management to keep control of pixel complexity.A form of level of detail management has been applied to texture maps for years, under the name of mipmapping, also providing higher rendering quality.
It is commonplace to say that "an object has been LOD-ed" when the object is simplified by the underlying LOD-ing algorithm as well as a 3D modeler manually creating LOD models.[citation needed]
The origin[1] of all the LOD algorithms for 3D computer graphics can be traced back to an article by James H. Clark in the October 1976 issue of Communications of the ACM.At the time, computers were monolithic and rare, and graphics were being driven by researchers. The hardware itself was completely different, both architecturally and performance-wise. As such, many differences could be observed with regard to today's algorithms but also many common points.
The original algorithm presented a much more generic approach to what will be discussed here. After introducing some available algorithms for geometry management, it is stated that most fruitful gains came from "...structuring the environments being rendered", allowing to exploit faster transformations and clipping operations.
For example, a dodecahedron looks like a sphere from a sufficiently large distance and thus can be used to model it so long as it is viewed from that or a greater distance. However, if it must ever be viewed more closely, it will look like a dodecahedron. One solution to this is simply to define it with the most detail that will ever be necessary. However, then it might have far more detail than is needed to represent it at large distances, and in a complex environment with many such objects, there would be too many polygons (or other geometric primitives) for the visible surface algorithms to efficiently handle.
The proposed algorithm envisions a tree data structure which encodes in its arcs both transformations and transitions to more detailed objects. In this way, each node encodes an object and according to a fast heuristic, the tree is descended to the leaves which provide each object with more detail. When a leaf is reached, other methods could be used when higher detail is needed, such as Catmull's recursive subdivision[2].
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