Dimensions in data management and data warehousing contain relatively static data about such entities as geographical locations, customers, or products. Data captured by Slowly Changing Dimensions (SCDs) change slowly but unpredictably, rather than according to a regular schedule.[1]
1.5% of 1 = 0.02 1.5% of 2 = 0.03 1.5% of 3 = 0.05 1.5% of 4 = 0.06 1.5% of 5 = 0.08 1.5% of 6 = 0.09 1.5% of 7 = 0.11 1.5% of 8 = 0.12 1.5% of 9 = 0.14. People with LADA might first experience symptoms that appear to be the result of type 2 diabetes, but the condition is more similar to type 1 diabetes. Some people refer to it as “type 1.5. What are the causes of type 1.5 diabetes? Although it is difficult to determine the underlying reasons for type 1.5 diabetes, still past researches have revealed that the autoimmune components of type 1, 1.5 and 2 Diabetes often overlap in some antibodies and become confused. This acts against beta cells of the pancreas. Type 1: The Reformer and Type 5: The Observer. Similarities: Both Ones and Fives can be intellectual and appear to be somewhat aloof or drawn into themselves when they are thinking. Both care about doing things well. Differences: Reformers (1) are concerned with doing well according to a set of rules that they have adopted. They focus on being. Type 1 diabetes was previously known as juvenile or insulin-dependent diabetes but has been re-characterized to reflect absolute insulin deficiency. When autoimmune type 1 diabetes is present, one or more of the islet autoantibodies will be present in about 95% of those affected at the time of initial diagnosis.
Some scenarios can cause referential integrity problems.
For example, a database may contain a fact table that stores sales records. This fact table would be linked to dimensions by means of foreign keys. One of these dimensions may contain data about the company's salespeople: e.g., the regional offices in which they work. However, the salespeople are sometimes transferred from one regional office to another. For historical sales reporting purposes it may be necessary to keep a record of the fact that a particular sales person had been assigned to a particular regional office at an earlier date, whereas that sales person is now assigned to a different regional office.[clarification needed]
Dealing with these issues involves SCD management methodologies referred to as Type 0 through 6. Type 6 SCDs are also sometimes called Hybrid SCDs.
Type 0: retain original[edit]
The Type 0 dimension attributes never change and are assigned to attributes that have durable values or are described as 'Original'. Examples: Date of Birth, Original Credit Score. Type 0 applies to most Date Dimension attributes.[2]
Type 1: overwrite[edit]
This method overwrites old with new data, and therefore does not track historical data.
Example of a supplier table:
Supplier_Key | Supplier_Code | Supplier_Name | Supplier_State |
---|---|---|---|
123 | ABC | Acme Supply Co | CA |
In the above example, Supplier_Code is the natural key and Supplier_Key is a surrogate key. Technically, the surrogate key is not necessary, since the row will be unique by the natural key (Supplier_Code).
If the supplier relocates the headquarters to Illinois the record would be overwritten:
Supplier_Key | Supplier_Code | Supplier_Name | Supplier_State |
---|---|---|---|
123 | ABC | Acme Supply Co | IL |
The disadvantage of the Type 1 method is that there is no history in the data warehouse. It has the advantage however that it's easy to maintain.
If one has calculated an aggregate table summarizing facts by state, it will need to be recalculated when the Supplier_State is changed.[1]
Type 2: add new row[edit]
This method tracks historical data by creating multiple records for a given natural key in the dimensional tables with separate surrogate keys and/or different version numbers. Unlimited history is preserved for each insert.
For example, if the supplier relocates to Illinois the version numbers will be incremented sequentially:
Supplier_Key | Supplier_Code | Supplier_Name | Supplier_State | Version |
---|---|---|---|---|
123 | ABC | Acme Supply Co | CA | 0 |
124 | ABC | Acme Supply Co | IL | 1 |
Another method is to add 'effective date' columns.
Supplier_Key | Supplier_Code | Supplier_Name | Supplier_State | Start_Date | End_Date |
---|---|---|---|---|---|
123 | ABC | Acme Supply Co | CA | 2000-01-01T00:00:00 | 2004-12-22T00:00:00 |
124 | ABC | Acme Supply Co | IL | 2004-12-22T00:00:00 | NULL |
The Start date/time of the second row is equal to the End date/time of the previous row. The null End_Date in row two indicates the current tuple version. A standardized surrogate high date (e.g. 9999-12-31) may instead be used as an end date, so that the field can be included in an index, and so that null-value substitution is not required when querying.
And a third method uses an effective date and a current flag.
Supplier_Key | Supplier_Code | Supplier_Name | Supplier_State | Effective_Date | Current_Flag |
---|---|---|---|---|---|
123 | ABC | Acme Supply Co | CA | 2000-01-01T00:00:00 | N |
124 | ABC | Acme Supply Co | IL | 2004-12-22T00:00:00 | Y |
Araxis merge pro full edition 2016 4807 download free. The Current_Flag value of 'Y' indicates the current tuple version.
Transactions that reference a particular surrogate key (Supplier_Key) are then permanently bound to the time slices defined by that row of the slowly changing dimension table. An aggregate table summarizing facts by state continues to reflect the historical state, i.e. the state the supplier was in at the time of the transaction; no update is needed. To reference the entity via the natural key, it is necessary to remove the unique constraint making Referential integrity by DBMS impossible.
If there are retroactive changes made to the contents of the dimension, or if new attributes are added to the dimension (for example a Sales_Rep column) which have different effective dates from those already defined, then this can result in the existing transactions needing to be updated to reflect the new situation. This can be an expensive database operation, so Type 2 SCDs are not a good choice if the dimensional model is subject to frequent change.[1]
Type 3: add new attribute[edit]
This method tracks changes using separate columns and preserves limited history. The Type 3 preserves limited history as it is limited to the number of columns designated for storing historical data. The original table structure in Type 1 and Type 2 is the same but Type 3 adds additional columns. In the following example, an additional column has been added to the table to record the supplier's original state - only the previous history is stored.
Supplier_Key | Supplier_Code | Supplier_Name | Original_Supplier_State | Effective_Date | Current_Supplier_State |
---|---|---|---|---|---|
123 | ABC | Acme Supply Co | CA | 2004-12-22T00:00:00 | IL |
This record contains a column for the original state and current state—cannot track the changes if the supplier relocates a second time. Anamorphic pro 1 4 download free.
One variation of this is to create the field Previous_Supplier_State instead of Original_Supplier_State which would track only the most recent historical change.[1]
Type 4: add history table[edit]
The Type 4 method is usually referred to as using 'history tables', where one table keeps the current data, and an additional table is used to keep a record of some or all changes. Both the surrogate keys are referenced in the Fact table to enhance query performance.
For the above example, the original table name is Supplier and the history table is Supplier_History.
Supplier_key | Supplier_Code | Supplier_Name | Supplier_State |
---|---|---|---|
124 | ABC | Acme & Johnson Supply Co | IL |
Supplier_key | Supplier_Code | Supplier_Name | Supplier_State | Create_Date |
---|---|---|---|---|
123 | ABC | Acme Supply Co | CA | 2003-06-14T00:00:00 |
124 | ABC | Acme & Johnson Supply Co | IL | 2004-12-22T00:00:00 |
This method resembles how database audit tables and change data capture techniques function.
Type 5[edit]
The type 5 technique builds on the type 4 mini-dimension by embedding a “current profile” mini-dimension key in the base dimension that’s overwritten as a type 1 attribute. This approach, called type 5 because 4 + 1 equals 5, allows the currently-assigned mini-dimension attribute values to be accessed along with the base dimension’s others without linking through a fact table. Logically, we typically represent the base dimension and current mini-dimension profile outrigger as a single table in the presentation layer. The outrigger attributes should have distinct column names, like “Current Income Level,” to differentiate them from attributes in the mini-dimension linked to the fact table. The ETL team must update/overwrite the type 1 mini-dimension reference whenever the current mini-dimension changes over time. If the outrigger approach does not deliver satisfactory query performance, then the mini-dimension attributes could be physically embedded (and updated) in the base dimension.[3]
Type 6: combined approach[edit]
The Type 6 method combines the approaches of types 1, 2 and 3 (1 + 2 + 3 = 6). One possible explanation of the origin of the term was that it was coined by Ralph Kimball during a conversation with Stephen Pace from Kalido[citation needed]. Ralph Kimball calls this method 'Unpredictable Changes with Single-Version Overlay' in The Data Warehouse Toolkit.[1]
The Supplier table starts out with one record for our example supplier:
Supplier_Key | Row_Key | Supplier_Code | Supplier_Name | Current_State | Historical_State | Start_Date | End_Date | Current_Flag |
---|---|---|---|---|---|---|---|---|
123 | 1 | ABC | Acme Supply Co | CA | CA | 2000-01-01T00:00:00 | 9999-12-31T23:59:59 | Y |
The Current_State and the Historical_State are the same. The optional Current_Flag attribute indicates that this is the current or most recent record for this supplier.
When Acme Supply Company moves to Illinois, we add a new record, as in Type 2 processing, however a row key is included to ensure we have a unique key for each row:
Supplier_Key | Row_Key | Supplier_Code | Supplier_Name | Current_State | Historical_State | Start_Date | End_Date | Current_Flag |
---|---|---|---|---|---|---|---|---|
123 | 1 | ABC | Acme Supply Co | IL | CA | 2000-01-01T00:00:00 | 2004-12-22T00:00:00 | N |
123 | 2 | ABC | Acme Supply Co | IL | IL | 2004-12-22T00:00:00 | 9999-12-31T23:59:59 | Y |
We overwrite the Current_State information in the first record (Row_Key = 1) with the new information, as in Type 1 processing. We create a new record to track the changes, as in Type 2 processing. And we store the history in a second State column (Historical_State), which incorporates Type 3 processing.
For example, if the supplier were to relocate again, we would add another record to the Supplier dimension, and we would overwrite the contents of the Current_State column:
Supplier_Key | Row_Key | Supplier_Code | Supplier_Name | Current_State | Historical_State | Start_Date | End_Date | Current_Flag |
---|---|---|---|---|---|---|---|---|
123 | 1 | ABC | Acme Supply Co | NY | CA | 2000-01-01T00:00:00 | 2004-12-22T00:00:00 | N |
123 | 2 | ABC | Acme Supply Co | NY | IL | 2004-12-22T00:00:00 | 2008-02-04T00:00:00 | N |
123 | 3 | ABC | Acme Supply Co | NY | NY | 2008-02-04T00:00:00 | 9999-12-31T23:59:59 | Y |
Type 2 / type 6 fact implementation[edit]
Type 2 surrogate key with type 3 attribute[edit]
In many Type 2 and Type 6 SCD implementations, the surrogate key from the dimension is put into the fact table in place of the natural key when the fact data is loaded into the data repository.[1] The surrogate key is selected for a given fact record based on its effective date and the Start_Date and End_Date from the dimension table. This allows the fact data to be easily joined to the correct dimension data for the corresponding effective date.
Here is the Supplier table as we created it above using Type 6 Hybrid methodology:
Supplier_Key | Supplier_Code | Supplier_Name | Current_State | Historical_State | Start_Date | End_Date | Current_Flag |
---|---|---|---|---|---|---|---|
123 | ABC | Acme Supply Co | NY | CA | 2000-01-01T00:00:00 | 2004-12-22T00:00:00 | N |
124 | ABC | Acme Supply Co | NY | IL | 2004-12-22T00:00:00 | 2008-02-04T00:00:00 | N |
125 | ABC | Acme Supply Co | NY | NY | 2008-02-04T00:00:00 | 9999-12-31T23:59:59 | Y |
Once the Delivery table contains the correct Supplier_Key, it can easily be joined to the Supplier table using that key. The following SQL retrieves, for each fact record, the current supplier state and the state the supplier was located in at the time of the delivery:
Pure type 6 implementation[edit]
Having a Type 2 surrogate key for each time slice can cause problems if the dimension is subject to change.[1]
A pure Type 6 implementation does not use this, but uses a Surrogate Key for each master data item (e.g. each unique supplier has a single surrogate key).
This avoids any changes in the master data having an impact on the existing transaction data.
It also allows more options when querying the transactions.
Here is the Supplier table using the pure Type 6 methodology:
Supplier_Key | Supplier_Code | Supplier_Name | Supplier_State | Start_Date | End_Date |
---|---|---|---|---|---|
456 | ABC | Acme Supply Co | CA | 2000-01-01T00:00:00 | 2004-12-22T00:00:00 |
456 | ABC | Acme Supply Co | IL | 2004-12-22T00:00:00 | 2008-02-04T00:00:00 |
456 | ABC | Acme Supply Co | NY | 2008-02-04T00:00:00 | 9999-12-31T23:59:59 |
The following example shows how the query must be extended to ensure a single supplier record is retrieved for each transaction.
A fact record with an effective date (Delivery_Date) of August 9, 2001 will be linked to Supplier_Code of ABC, with a Supplier_State of 'CA'. A fact record with an effective date of October 11, 2007 will also be linked to the same Supplier_Code ABC, but with a Supplier_State of 'IL'.
While more complex, there are a number of advantages of this approach, including:
- Referential integrity by DBMS is now possible, but one cannot use Supplier_Code as foreign key on Product table and using Supplier_Key as foreign key each product is tied on specific time slice.
- If there is more than one date on the fact (e.g. Order Date, Delivery Date, Invoice Payment Date) one can choose which date to use for a query.
- You can do 'as at now', 'as at transaction time' or 'as at a point in time' queries by changing the date filter logic.
- You don't need to reprocess the Fact table if there is a change in the dimension table (e.g. adding additional fields retrospectively which change the time slices, or if one makes a mistake in the dates on the dimension table one can correct them easily).
- You can introduce bi-temporal dates in the dimension table.
- You can join the fact to the multiple versions of the dimension table to allow reporting of the same information with different effective dates, in the same query.
The following example shows how a specific date such as '2012-01-01T00:00:00' (which could be the current datetime) can be used.
Both surrogate and natural key[edit]
An alternative implementation is to place both the surrogate key and the natural key into the fact table.[4] This allows the user to select the appropriate dimension records based on:
- the primary effective date on the fact record (above),
- the most recent or current information,
- any other date associated with the fact record.
This method allows more flexible links to the dimension, even if one has used the Type 2 approach instead of Type 6.
Here is the Supplier table as we might have created it using Type 2 methodology:
Supplier_Key | Supplier_Code | Supplier_Name | Supplier_State | Start_Date | End_Date | Current_Flag |
---|---|---|---|---|---|---|
123 | ABC | Acme Supply Co | CA | 2000-01-01T00:00:00 | 2004-12-22T00:00:00 | N |
124 | ABC | Acme Supply Co | IL | 2004-12-22T00:00:00 | 2008-02-04T00:00:00 | N |
125 | ABC | Acme Supply Co | NY | 2008-02-04T00:00:00 | 9999-12-31T23:59:59 | Y |
The following SQL retrieves the most current Supplier_Name and Supplier_State for each fact record:
If there are multiple dates on the fact record, the fact can be joined to the dimension using another date instead of the primary effective date. For instance, the Delivery table might have a primary effective date of Delivery_Date, but might also have an Order_Date associated with each record.
The following SQL retrieves the correct Supplier_Name and Supplier_State for each fact record based on the Order_Date:
Some cautions:
- Referential integrity by DBMS is not possible since there is not a unique key to create the relationship.
- If relationship is made with surrogate to solve problem above then one ends with entity tied to a specific time slice.
- If the join query is not written correctly, it may return duplicate rows and/or give incorrect answers.
- The date comparison might not perform well.
- Some Business Intelligence tools do not handle generating complex joins well.
- The ETL processes needed to create the dimension table needs to be carefully designed to ensure that there are no overlaps in the time periods for each distinct item of reference data.
Combining types[edit]
Scd model example
Different SCD Types can be applied to different columns of a table. For example, we can apply Type 1 to the Supplier_Name column and Type 2 to the Supplier_State column of the same table.
See also[edit]
- Entity–attribute–value model - Vertical
Notes[edit]
- ^ abcdefgKimball, Ralph; Ross, Margy. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling.
- ^http://www.kimballgroup.com/2013/02/design-tip-152-slowly-changing-dimension-types-0-4-5-6-7/
- ^https://www.kimballgroup.com/2013/02/design-tip-152-slowly-changing-dimension-types-0-4-5-6-7/
- ^Ross, Margy; Kimball, Ralph (March 1, 2005). 'Slowly Changing Dimensions Are Not Always as Easy as 1, 2, 3'. Intelligent Enterprise.
References[edit]
- Bruce Ottmann, Chris Angus: Data processing system, US Patent Office, Patent Number 7,003,504. February 21, 2006
- Ralph Kimball:Kimball University: Handling Arbitrary Restatements of History[1]. December 9, 2007
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Slowly_changing_dimension&oldid=978412623'
- 4.5
- 5.5
- 6.5
- 7.5
- 8.5
- 9.5
- 10.5
- 11.5
- 4.0
- 5.0
- 6.0
- 7.0
- 8.0
- 9.0
- 10.0
- 11.0
- 12.0
An A1c of 5.9 indicates Prediabetes.
View the full A1c chart to learn more about A1c levels.
What does an A1c of 5.9 mean?
An A1C of 5.9 means that you have prediabetes, which puts you at risk for developing diabetes.
The A1c test measures blood sugar over the last three months by looking at the percentage of hemoglobin saturated with sugar. An A1c of 5.9 means that 5.9% of the hemoglobin in your blood are saturated with sugar.
While there are no signs or symptoms of prediabetes, the damage diabetes can have on your heart, blood vessels and kidneys may have already begun.
A score of 5.9 doesn’t automatically mean that you will get diabetes. However, you should focus on reducing your A1c score and improving your overall health.
A1c 5.9 conversion rates
Blood sugar can be measured in a variety of ways, which often leads to confusion.
An A1c of 5.9 is equal to blood sugar of 123 mg/dl or 6.8 mmol/l.
View the full A1c conversion chart to better understand these tests and numbers.
What to do if your A1c is 5.9
An A1c of 5.9 falls into the prediabetic range between 5.7 and 6.4. People with prediabetes are likely to get type 2 diabetes within 10 years unless they make serious changes to their lifestyle.
Type 2 diabetes is the most common form of diabetes. Type 2 means that your body still produces insulin but isn’t using it properly. Many people can control their blood sugar levels with lifestyle changes while others may need insulin or other medications to manage it.
Keep an eye on your blood sugar by testing at home. It’s easier than ever and there are a variety of affordable blood glucose monitors available.
A prediabetes A1c reading is a call to action. At a minimum you’ll need to make some lifestyle changes. You and your doctor can discuss whether medication is necessary.
Medications with A1c of 5.9
Many doctors won’t prescribe diabetes medication for someone with an A1c of 5.9. However, when other risk factors are present, such as high blood pressure or high cholesterol, your doctor might prescribe a first line drug to reduce your blood sugar.
The most common first line drug is Metformin, an oral drug that reduces glucose production in the liver, decreases the absorption of glucose in the stomach and improves your body’s insulin sensitivity.
Already on medication to manage your diabetes? If so, an A1c of 5.9 might be considered adequate, though getting below 5.6 is still recommended.
Talk to your doctor about whether an A1c of 5.9 is the optimal level for you and if medication, dosage or injection adjustments are necessary.
Lifestyle changes with A1c of 5.9
“An ounce of prevention is worth a pound of cure.”
If you have an A1c level of 5.9 you can minimize the chance of developing diabetes through the following lifestyle changes:
10.0.1.5 Admin
- Increase exercise
- Reduce calories
- Monitor carbohydrates
- Limit alcohol
- Stop smoking
- Lose weight
- Alleviate stress
Skip dessert. Ditch the fast food. Take the stairs instead of the elevator. Meditate. Changing a few habits can make a difference and help ensure your blood sugar stays under control.
Type To 1 5 0 3
Remember to review your plan with a doctor before pursuing lifestyle modifications. Each patient may have specific medical conditions, such as a heart condition, that could make certain activities dangerous.