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• OWB 11gR2 – Degenerate Dimensions

  - by David Allan

  Ever wondered how to build degenerate dimensions in OWB and get the benefits of slowly changing dimensions and cube loading? Now its possible through some changes in 11gR2 to make the dimension and cube loading much more flexible. This will let you get the benefits of OWB's surrogate key handling and slowly changing dimension reference when loading the fact table and need degenerate dimensions (see Ralph Kimball's degenerate dimensions design tip). Here we will see how to use the cube operator to load slowly changing, regular and degenerate dimensions. The cube and cube operator can now work with dimensions which have no surrogate key as well as dimensions with surrogates, so you can get the benefit of the cube loading and incorporate the degenerate dimension loading. What you need to do is create a dimension in OWB that is purely used for ETL metadata; the dimension itself is never deployed (its table is, but has not data) it has no surrogate keys has a single level with a business attribute the degenerate dimension data and a dummy attribute, say description just to pass the OWB validation. When this degenerate dimension is added into a cube, you will need to configure the fact table created and set the 'Deployable' flag to FALSE for the foreign key generated to the degenerate dimension table. The degenerate dimension reference will then be in the cube operator and used when matching. Create the degenerate dimension using the regular wizard. Delete the Surrogate ID attribute, this is not needed. Define a level name for the dimension member (any name). After the wizard has completed, in the editor delete the hierarchy STANDARD that was automatically generated, there is only a single level, no need for a hierarchy and this shouldn't really be created. Deploy the implementing table DD_ORDERNUMBER_TAB, this needs to be deployed but with no data (the mapping here will do a left outer join of the source data with the empty degenerate dimension table). Now, go ahead and build your cube, use the regular TIMES dimension for example and your degenerate dimension DD_ORDERNUMBER, can add in SCD dimensions etc. Configure the fact table created and set Deployable to false, so the foreign key does not get generated. Can now use the cube in a mapping and load data into the fact table via the cube operator, this will look after surrogate lookups and slowly changing dimension references.   If you generate the SQL you will see the ON clause for matching includes the columns representing the degenerate dimension columns. Here we have seen how this use case for loading fact tables using degenerate dimensions becomes a whole lot simpler using OWB 11gR2. I'm sure there are other use cases where using this mix of dimensions with surrogate and regular identifiers is useful, Fact tables partitioned by date columns is another classic example that this will greatly help and make the cube operator much more useful. Good to hear any comments.

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• OWB 11gR2 - Early Arriving Facts

  - by Dawei Sun

  A common challenge when building ETL components for a data warehouse is how to handle early arriving facts. OWB 11gR2 introduced a new feature to address this for dimensional objects entitled Orphan Management. An orphan record is one that does not have a corresponding existing parent record. Orphan management automates the process of handling source rows that do not meet the requirements necessary to form a valid dimension or cube record. In this article, a simple example will be provided to show you how to use Orphan Management in OWB. We first import a sample MDL file that contains all the objects we need. Then we take some time to examine all the objects. After that, we prepare the source data, deploy the target table and dimension/cube loading map. Finally, we run the loading maps, and check the data in target dimension/cube tables. OK, let’s start… 1. Import MDL file and examine sample project First, download zip file from here, which includes a MDL file and three source data files. Then we open OWB design center, import orphan_management.mdl by using the menu File->Import->Warehouse Builder Metadata. Now we have several objects in BI_DEMO project as below: Mapping LOAD_CHANNELS_OM: The mapping for dimension loading. Mapping LOAD_SALES_OM: The mapping for cube loading. Dimension CHANNELS_OM: The dimension that contains channels data. Cube SALES_OM: The cube that contains sales data. Table CHANNELS_OM: The star implementation table of dimension CHANNELS_OM. Table SALES_OM: The star implementation table of cube SALES_OM. Table SRC_CHANNELS: The source table of channels data, that will be loaded into dimension CHANNELS_OM. Table SRC_ORDERS and SRC_ORDER_ITEMS: The source tables of sales data that will be loaded into cube SALES_OM. Sequence CLASS_OM_DIM_SEQ: The sequence used for loading dimension CHANNELS_OM. Dimension CHANNELS_OM This dimension has a hierarchy with three levels: TOTAL, CLASS and CHANNEL. Each level has three attributes: ID (surrogate key), NAME and SOURCE_ID (business key). It has a standard star implementation. The orphan management policy and the default parent setting are shown in the following screenshots: The orphan management policy options that you can set for loading are: Reject Orphan: The record is not inserted. Default Parent: You can specify a default parent record. This default record is used as the parent record for any record that does not have an existing parent record. If the default parent record does not exist, Warehouse Builder creates the default parent record. You specify the attribute values of the default parent record at the time of defining the dimensional object. If any ancestor of the default parent does not exist, Warehouse Builder also creates this record. No Maintenance: This is the default behavior. Warehouse Builder does not actively detect, reject, or fix orphan records. While removing data from a dimension, you can select one of the following orphan management policies: Reject Removal: Warehouse Builder does not allow you to delete the record if it has existing child records. No Maintenance: This is the default behavior. Warehouse Builder does not actively detect, reject, or fix orphan records. (More details are at http://download.oracle.com/docs/cd/E11882_01/owb.112/e10935/dim_objects.htm#insertedID1) Cube SALES_OM This cube is references to dimension CHANNELS_OM. It has three measures: AMOUNT, QUANTITY and COST. The orphan management policy setting are shown as following screenshot: The orphan management policy options that you can set for loading are: No Maintenance: Warehouse Builder does not actively detect, reject, or fix orphan rows. Default Dimension Record: Warehouse Builder assigns a default dimension record for any row that has an invalid or null dimension key value. Use the Settings button to define the default parent row. Reject Orphan: Warehouse Builder does not insert the row if it does not have an existing dimension record. (More details are at http://download.oracle.com/docs/cd/E11882_01/owb.112/e10935/dim_objects.htm#BABEACDG) Mapping LOAD_CHANNELS_OM This mapping loads source data from table SRC_CHANNELS to dimension CHANNELS_OM. The operator CHANNELS_IN is bound to table SRC_CHANNELS; CHANNELS_OUT is bound to dimension CHANNELS_OM. The TOTALS operator is used for generating a constant value for the top level in the dimension. The CLASS_FILTER operator is used to filter out the “invalid” class name, so then we can see what will happen when those channel records with an “invalid” parent are loading into dimension. Some properties of the dimension operator in this mapping are important to orphan management. See the screenshot below: Create Default Level Records: If YES, then default level records will be created. This property must be set to YES for dimensions and cubes if one of their orphan management policies is “Default Parent” or “Default Dimension Record”. This property is set to NO by default, so the user may need to set this to YES manually. LOAD policy for INVALID keys/ LOAD policy for NULL keys: These two properties have the same meaning as in the dimension editor. The values are set to the same as the dimension value when user drops the dimension into the mapping. The user does not need to modify these properties. Record Error Rows: If YES, error rows will be inserted into error table when loading the dimension. REMOVE Orphan Policy: This property is used when removing data from a dimension. Since the dimension loading type is set to LOAD in this example, this property is disabled. Mapping LOAD_SALES_OM This mapping loads source data from table SRC_ORDERS and SRC_ORDER_ITEMS to cube SALES_OM. This mapping seems a little bit complicated, but operators in the red rectangle are used to filter out and generate the records with “invalid” or “null” dimension keys. Some properties of the cube operator in a mapping are important to orphan management. See the screenshot below: Enable Source Aggregation: Should be checked in this example. If the default dimension record orphan policy is set for the cube operator, then it is recommended that source aggregation also be enabled. Otherwise, the orphan management processing may produce multiple fact rows with the same default dimension references, which will cause an “unstable rowset” execution error in the database, since the dimension refs are used as update match attributes for updating the fact table. LOAD policy for INVALID keys/ LOAD policy for NULL keys: These two properties have the same meaning as in the cube editor. The values are set to the same as in the cube editor when the user drops the cube into the mapping. The user does not need to modify these properties. Record Error Rows: If YES, error rows will be inserted into error table when loading the cube. 2. Deploy objects and mappings We now can deploy the objects. First, make sure location SALES_WH_LOCAL has been correctly configured. Then open Control Center Manager by using the menu Tools->Control Center Manager. Expand BI_DEMO->SALES_WH_LOCAL, click SALES_WH node on the project tree. We can see the following objects: Deploy all the objects in the following order: Sequence CLASS_OM_DIM_SEQ Table CHANNELS_OM, SALES_OM, SRC_CHANNELS, SRC_ORDERS, SRC_ORDER_ITEMS Dimension CHANNELS_OM Cube SALES_OM Mapping LOAD_CHANNELS_OM, LOAD_SALES_OM Note that we deployed source tables as well. Normally, we import source table from database instead of deploying them to target schema. However, in this example, we designed the source tables in OWB and deployed them to database for the purpose of this demonstration. 3. Prepare and examine source data Before running the mappings, we need to populate and examine the source data first. Run SRC_CHANNELS.sql, SRC_ORDERS.sql and SRC_ORDER_ITEMS.sql as target user. Then we check the data in these three tables. Table SRC_CHANNELS SQL> select rownum, id, class, name from src_channels; Records 1~5 are correct; they should be loaded into dimension without error. Records 6,7 and 8 have null parents; they should be loaded into dimension with a default parent value, and should be inserted into error table at the same time. Records 9, 10 and 11 have “invalid” parents; they should be rejected by dimension, and inserted into error table. Table SRC_ORDERS and SRC_ORDER_ITEMS SQL> select rownum, a.id, a.channel, b.amount, b.quantity, b.cost from src_orders a, src_order_items b where a.id = b.order_id; Record 178 has null dimension reference; it should be loaded into cube with a default dimension reference, and should be inserted into error table at the same time. Record 179 has “invalid” dimension reference; it should be rejected by cube, and inserted into error table. Other records should be aggregated and loaded into cube correctly. 4. Run the mappings and examine the target data In the Control Center Manager, expand BI_DEMO-> SALES_WH_LOCAL-> SALES_WH-> Mappings, right click on LOAD_CHANNELS_OM node, click Start. Use the same way to run mapping LOAD_SALES_OM. When they successfully finished, we can check the data in target tables. Table CHANNELS_OM SQL> select rownum, total_id, total_name, total_source_id, class_id,class_name, class_source_id, channel_id, channel_name,channel_source_id from channels_om order by abs(dimension_key); Records 1,2 and 3 are the default dimension records for the three levels. Records 8, 10 and 15 are the loaded records that originally have null parents. We see their parents name (class_name) is set to DEF_CLASS_NAME. Those records whose CHANNEL_NAME are Special_4, Special_5 and Special_6 are not loaded to this table because of the invalid parent. Error Table CHANNELS_OM_ERR SQL> select rownum, class_source_id, channel_id, channel_name,channel_source_id, err$$$_error_reason from channels_om_err order by channel_name; We can see all the record with null parent or invalid parent are inserted into this error table. Error reason is “Default parent used for record” for the first three records, and “No parent found for record” for the last three. Table SALES_OM SQL> select a.*, b.channel_name from sales_om a, channels_om b where a.channels=b.channel_id; We can see the order record with null channel_name has been loaded into target table with a default channel_name. The one with “invalid” channel_name are not loaded. Error Table SALES_OM_ERR SQL> select a.amount, a.cost, a.quantity, a.channels, b.channel_name, a.err$$$_error_reason from sales_om_err a, channels_om b where a.channels=b.channel_id(+); We can see the order records with null or invalid channel_name are inserted into error table. If the dimension reference column is null, the error reason is “Default dimension record used for fact”. If it is invalid, the error reason is “Dimension record not found for fact”. Summary In summary, this article illustrated the Orphan Management feature in OWB 11gR2. Automated orphan management policies improve ETL developer and administrator productivity by addressing an important cause of cube and dimension load failures, without requiring developers to explicitly build logic to handle these orphan rows.

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• select all values from a dimension for which there are facts in all other dimensions

  - by ideasculptor

  I've tried to simplify for the purposes of asking this question. Hopefully, this will be comprehensible. Basically, I have a fact table with a time dimension, another dimension, and a hierarchical dimension. For the purposes of the question, let's assume the hierarchical dimension is zip code and state. The other dimension is just descriptive. Let's call it 'customer' Let's assume there are 50 customers. I need to find the set of states for which there is at least one zip code in which EVERY customer has at least one fact row for each day in the time dimension. If a zip code has only 49 customers, I don't care about it. If even one of the 50 customers doesn't have a value for even 1 day in a zip code, I don't care about it. Finally, I also need to know which zip codes qualified the state for selection. Note, there is no requirement that every zip code have a full data set - only that at least one zip code does. I don't mind making multiple queries and doing some processing on the client side. This is a dataset that only needs to be generated once per day and can be cached. I don't even see a particularly clean way to do it with multiple queries short of simply brute-force iteration, and there are a heck of a lot of 'zip codes' in the data set (not actually zip codes, but the there are approximately 100,000 entries in the lower level of the hierarchy and several hundred in the top level, so zipcode-state is a reasonable analogy)

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• Error : Member 'D-T-D' in the Period dimension has no value for the Period Type property

  - by RahulS

  Workaround for LCM EPMA deploy errors: Error : Member 'D-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'D-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'.  Error : Member 'W-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'W-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'.  Error : Member 'M-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'M-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'.  Error : Member 'Q-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'Q-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'.  Error : Member 'P-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'P-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'.  Error : Member 'S-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'S-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'.  Error : Member 'Y-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'Y-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'.  Error : Member 'H-T-D' in the Period dimension has no value for the Period  Type property.  Error : Member name 'H-T-D' in the Period dimension is only valid when Period  Type is set to 'DTS Time Period'. Fix 1. Edit the Period dimension LCM artifact (Keep the back up of the file before editing.)  2. Delete the DTS members (for example as mentioned below) in the Period dimension hierarchy section.   #root|D-T-D|True||||||||||||||||   #root|W-T-D|True||||||||||||||||   #root|M-T-D|True||||||||||||||||   #root|Q-T-D|True||||||||||||||||   #root|P-T-D|True||||||||||||||||   #root|S-T-D|True||||||||||||||||   #root|Y-T-D|True||||||||||||||||   #root|H-T-D|True||||||||||||||||   3. Delete the DTS members (for example as mentioned below) in the Period member hierarchy section,   D-T-D|True||||||||||||||||   W-T-D|True||||||||||||||||   M-T-D|True||||||||||||||||   Q-T-D|True||||||||||||||||   P-T-D|True||||||||||||||||   S-T-D|True||||||||||||||||   Y-T-D|True||||||||||||||||   H-T-D|True||||||||||||||||   4. Then save the edited Period dimension LCM artifact.   5. Then try to import the Period dimension using LCM.   6. Then Validate/Deploy the Planning application still the same issue. PS: This issue is fixed in 11.1.2.2.

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• SSAS: Using fake dimension and scopes for dynamic ranges

  - by DigiMortal

  In one of my BI projects I needed to find count of objects in income range. Usual solution with range dimension was useless because range where object belongs changes in time. These ranges depend on calculation that is done over incomes measure so I had really no option to use some classic solution. Thanks to SSAS forums I got my problem solved and here is the solution. The problem – how to create dynamic ranges? I have two dimensions in SSAS cube: one for invoices related to objects rent and the other for objects. There is measure that sums invoice totals and two calculations. One of these calculations performs some computations based on object income and some other object attributes. Second calculation uses first one to define income ranges where object belongs. What I need is query that returns me how much objects there are in each group. I cannot use dimension for range because on one date object may belong to one range and two days later to another income range. By example, if object is not rented out for two days it makes no money and it’s income stays the same as before. If object is rented out after two days it makes some income and this income may move it to another income range. Solution – fake dimension and scopes Thanks to Gerhard Brueckl from pmOne I got everything work fine after some struggling with BI Studio. The original discussion he pointed out can be found from SSAS official forums thread Create a banding dimension that groups by a calculated measure. Solution was pretty simple by nature – we have to define fake dimension for our range and use scopes to assign values for object count measure. Object count measure is primitive – it just counts objects and that’s it. We will use it to find out how many objects belong to one or another range. We also need table for fake ranges and we have to fill it with ranges used in ranges calculation. After creating the table and filling it with ranges we can add fake range dimension to our cube. Let’s see now how to solve the problem step-by-step. Solving the problem Suppose you have ranges calculation defined like this: CASE WHEN [Measures].[ComplexCalc] < 0 THEN 'Below 0'WHEN [Measures].[ComplexCalc] >=0 AND  [Measures].[ComplexCalc] <=50 THEN '0 - 50'...END Let’s create now new table to our analysis database and name it as FakeIncomeRange. Here is the definition for table: CREATE TABLE [FakeIncomeRange] (     [range_id] [int] IDENTITY(1,1) NOT NULL,     [range_name] [nvarchar](50) NOT NULL,     CONSTRAINT [pk_fake_income_range] PRIMARY KEY CLUSTERED      (         [range_id] ASC     ) ) Don’t forget to fill this table with range labels you are using in ranges calculation. To use ranges from table we have to add this table to our data source view and create new dimension. We cannot bind this table to other tables but we have to leave it like it is. Our dimension has two attributes: ID and Name. The next thing to create is calculation that returns objects count. This calculation is also fake because we override it’s values for all ranges later. Objects count measure can be defined as calculation like this: COUNT([Object].[Object].[Object].members) Now comes the most crucial part of our solution – defining the scopes. Based on data used in this posting we have to define scope for each of our ranges. Here is the example for first range. SCOPE([FakeIncomeRange].[Name].&[Below 0], [Measures].[ObjectCount])     This=COUNT(            FILTER(                [Object].[Object].[Object].members,                 [Measures].[ComplexCalc] < 0          )     ) END SCOPE To get these scopes defined in cube we need MDX script blocks for each line given here. Take a look at the screenshot to get better idea what I mean. This example is given from SQL Server books online to avoid conflicts with NDA. :) From previous example the lines (MDX scripts) are: Line starting with SCOPE Block for This = Line with END SCOPE And now it is time to deploy and process our cube. Although you may see examples where there are semicolons in the end of statements you don’t need them. Visual Studio BI tools generate separate command from each script block so you don’t need to worry about it.

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• Loading Hybrid Dimension Table with SCD1 and SCD2 attributes + SSIS

  - by Nev_Rahd

  Hello I am just in a process of starting a new task, wherein in i need to load Hybrid Dimension Table with SCD1 and SCD2. This need to be achieved as a SSIS Package. Can someone guide what would be the best way dealing this in SSIS, should i used SCD component or there is other way? What are the best practices for this. For SCD2 type, am using Merge statement. Thanks

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• Unity3d: Box collider attached to animated FBX models through scripts at run-time have wrong dimension

  - by Heisenbug

  I have several scripts attached to static and non static models of my scene. All models are instantiated at run-time (and must be instantiated at run-time because I'm procedural building the scene). I'd like to add a BoxCollider or SphereCollider to my FBX models at runtime. With non animated models it works simply requiring BoxCollider component from the script attached to my GameObject. BoxCollider is created of the right dimension. Something like: [RequireComponent(typeof(BoxCollider))] public class AScript: MonoBehavior { } If I do the same thing with animated models, BoxCollider are created of the wrong dimension. For example if attach the script above to penelopeFBX model of the standard asset, BoxCollider is created smaller than the mesh itself. How can I solve this?

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• multi-dimension array problem in RGSS (RPG Maker XP)

  - by AzDesign

  This is my first day code script in RMXP. I read tutorials, ruby references, etc and I found myself stuck on a weird problem, here is the scenario: I made a custom script to display layered images Create the class, create an instance variable to hold the array, create a simple method to add an element into it, done The draw method (skipped the rest of the code to this part): def draw image = [] index = 0 for i in [email protected] if image.size > 0 index = image.size end image[index] = Sprite.new image[index].bitmap = RPG::Cache.picture(@components[i][0] + '.png') image[index].x = @x + @components[i][1] image[index].y = @y + @components[i][2] image[index].z = @z + @components[i][3] @test =+ 1 end end Create an event that does these script > $layerz = Layerz.new $layerz.configuration[0] = ['root',0,0,1] > $layerz.configuration[1] = ['bark',0,10,2] > $layerz.configuration[2] = ['branch',0,30,3] > $layerz.configuration[3] = ['leaves',0,60,4] $layerz.draw Run, trigger the event and the result : ERROR! Undefined method`[]' for nil:NilClass pointing at this line on draw method : image[index].bitmap = RPG::Cache.picture(@components[i][0] + '.png') THEN, I changed the method like these just for testing: def draw image = [] index = 0 for i in [email protected] if image.size > 0 index = image.size end image[index] = Sprite.new image[index].bitmap = RPG::Cache.picture(@components[0][0] + '.png') image[index].x = @x + @components[0][1] image[index].y = @y + @components[0][2] image[index].z = @z + @components[0][3] @test =+ 1 end I changed the @components[i][0] to @components[0][0] and IT WORKS, but only the root as it not iterates to the next array index Im stuck here, see : > in single level array, @components[0] and @components[i] has no problem > in multi-dimension array, @components[0][0] has no problem BUT > in multi-dimension array, @components[i][0] produce the error as above > mentioned. any suggestion to fix the error ? Or did I wrote something wrong ?

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• Managing Slowly Changing Dimension with MERGE Statement in SQL Server

  Slowly Changing Dimension (SCD) Transformation is a quick and easy way to manage smaller slowly changing dimensions but it has several limitations and does not perform well when the number of rows or columns gets larger. Arshad Ali explores some of the alternatives you can use for managing larger slowly changing dimensions. How to automate your .NET and SQL Server deploymentsDeploy .NET code and SQL Server databases in a single repeatable process with Red Gate Deployment Manager. Start deploying with a 28-day trial.

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• Excel 2010 & SSAS – Search Dimension Members

  - by Davide Mauri

  Today I’ve connected my Excel 2010 to an Analysis Services 2008 Cube and I got a very nice (and unexpected) surprise! It’s now finally possibly to search and filter Dimension Members directly from the combo box window: As you can easily imagine, for medium/big dimensions is really – really – really useful! Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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• Can a table be both Fact and Dimension

  - by PatFromCanada

  Ok, I am a newbie and don't really think "dimensionally" yet, I have most of my initial schema roughed out but I keep flipping back and forth on one table. I have a Contract table and it has a quantity column (tonnes), and a net price column, which need to be summed up a bunch of different ways, and the contract has lots of foreign keys (producer, commodity, futures month etc.) and dates so it appears to be a fact table. Also the contract is never updated, if that makes a difference. However, we create cash tickets which we use to pay out part or all of the contract and they have a contract ID on them so then the contract looks like a dimension in the cash ticket's star schema. Is this a problem? Any ideas on the process to resolve this, because people don't seem to like the idea of joining two fact tables. Should I put producerId and commodityId on the cash ticket? It would seem really weird not to have a contractID on it.

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• Converting 3 dimension byte array to a single byte array [on hold]

  - by Andrew Simpson

  I have a 3 dimensional byte array. The 3-d array represents a jpeg image. Each channel/array represents part of the RGB spectrum. I am not interested in retaining black pixels. A black pixel is represented by this atypical arrangement: myarray[0,0,0] =0; myarray[0,0,1] =0; myarray[0,0,2] =0; So, I have flattened this 3d array out to a 1d array by doing this byte[] AFlatArray = new byte[width x height x 3] and then assigning values respective to the coordinate. But like I said I do not want black pixels. So this array has to only contain color pixels with the x,y coordinate. The result I want is to re-represent the image from the i dimension byte array that only contains non-black pixels. How do I do that? It looks like I have to store black pixels as well because of the xy coordinate system. I have tried writing to a binary file but the size of that file is greater than the jpeg file as the jpeg file is compressed. I am using c#.

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• Database warehouse design: fact tables and dimension tables

  - by morpheous

  I am building a poor man's data warehouse using a RDBMS. I have identified the key 'attributes' to be recorded as: sex (true/false) demographic classification (A, B, C etc) place of birth date of birth weight (recorded daily): The fact that is being recorded My requirements are to be able to run 'OLAP' queries that allow me to: 'slice and dice' 'drill up/down' the data and generally, be able to view the data from different perspectives After reading up on this topic area, the general consensus seems to be that this is best implemented using dimension tables rather than normalized tables. Assuming that this assertion is true (i.e. the solution is best implemented using fact and dimension tables), I would like to seek some help in the design of these tables. 'Natural' (or obvious) dimensions are: Date dimension Geographical location Which have hierarchical attributes. However, I am struggling with how to model the following fields: sex (true/false) demographic classification (A, B, C etc) The reason I am struggling with these fields is that: They have no obvious hierarchical attributes which will aid aggregation (AFAIA) - which suggest they should be in a fact table They are mostly static or very rarely change - which suggests they should be in a dimension table. Maybe the heuristic I am using above is too crude? I will give some examples on the type of analysis I would like to carryout on the data warehouse - hopefully that will clarify things further. I would like to aggregate and analyze the data by sex and demographic classification - e.g. answer questions like: How does male and female weights compare across different demographic classifications? Which demographic classification (male AND female), show the most increase in weight this quarter. etc. Can anyone clarify whether sex and demographic classification are part of the fact table, or whether they are (as I suspect) dimension tables.? Also assuming they are dimension tables, could someone elaborate on the table structures (i.e. the fields)? The 'obvious' schema: CREATE TABLE sex_type (is_male int); CREATE TABLE demographic_category (id int, name varchar(4)); may not be the correct one.

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• Database warehoue design: fact tables and dimension tables

  - by morpheous

  I am building a poor man's data warehouse using a RDBMS. I have identified the key 'attributes' to be recorded as: sex (true/false) demographic classification (A, B, C etc) place of birth date of birth weight (recorded daily): The fact that is being recorded My requirements are to be able to run 'OLAP' queries that allow me to: 'slice and dice' 'drill up/down' the data and generally, be able to view the data from different perspectives After reading up on this topic area, the general consensus seems to be that this is best implemented using dimension tables rather than normalized tables. Assuming that this assertion is true (i.e. the solution is best implemented using fact and dimension tables), I would like to see some help in the design of these tables. 'Natural' (or obvious) dimensions are: Date dimension Geographical location Which have hierarchical attributes. However, I am struggling with how to model the following fields: sex (true/false) demographic classification (A, B, C etc) The reason I am struggling with these fields is that: They have no obvious hierarchical attributes which will aid aggregation (AFAIA) - which suggest they should be in a fact table They are mostly static or very rarely change - which suggests they should be in a dimension table. Maybe the heuristic I am using above is too crude? I will give some examples on the type of analysis I would like to carryout on the data warehouse - hopefully that will clarify things further. I would like to aggregate and analyze the data by sex and demographic classification - e.g. answer questions like: How does male and female weights compare across different demographic classifications? Which demographic classification (male AND female), show the most increase in weight this quarter. etc. Can anyone clarify whether sex and demographic classification are part of the fact table, or whether they are (as I suspect) dimension tables.? Also assuming they are dimension tables, could someone elaborate on the table structures (i.e. the fields)? The 'obvious' schema: CREATE TABLE sex_type (is_male int); CREATE TABLE demographic_category (id int, name varchar(4)); may not be the correct one.

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• Python regex to parse text file, get the items in list and count the list

  - by Nemo

  I have a text file which contains some data. I m particularly interested in finding the count of the number of items in v_dims v_dims pattern in my text file looks like this : v_dims={ "Sales", "Product Family", "Sales Organization", "Region", "Sales Area", "Sales office", "Sales Division", "Sales Person", "Sales Channel", "Sales Order Type", "Sales Number", "Sales Person", "Sales Quantity", "Sales Amount" } So I m thinking of getting all the elements in v_dims and dumping them out in a Python list. Then compute the len(mylist) to get the count of the items. The challenge is in getting all the elements of v_dims from my text file and putting them in an empty list. I m particularly interested in items in v_dims in my text file. The text file has data in the form of v_dims pattern i showed in my original post. Some data has nested patterns of v_dims. Thanks. Here's what I have tried and failed. Any help is appreciated. TIA. import re fname = "C:\Users\XXXX\Test.mrk" with open(fname, "r") as fo: content_as_string = fo.read() match = re.findall(r'v_dims={\"(.+?)\"}',content_as_string) Though I have a big text file, Here's a snippet of what's the structure of my text file version "1"; // Computer generated object language file object 'MRKR' "Main" { Data_Type=2, HeaderBlock={ Version_String="6.3 (25)" }, Printer_Info={ Orientation=0, Page_Width=8.50000000, Page_Height=11.00000000, Page_Header="", Page_Footer="", Margin_type=0, Top_Margin=0.50000000, Left_Margin=0.50000000, Bottom_Margin=0.50000000, Right_Margin=0.50000000 }, Marker_Options={ Close_All="TRUE", Hide_Console="FALSE", Console_Left="FALSE", Console_Width=217, Main_Style="Maximized", MDI_Rect={ 0, 0, 892, 1063 } }, Dives={ { Dive="A", Windows={ { View_Index=0, Window_Info={ Window_Rect={ 0, -288, 400, 1008 }, Window_Style="Maximized Front", Window_Name="Theater [Previous Qtr Diveplan-Dive A]" }, Dependent_bool="FALSE", Colset={ Dive_Type="Normal", Dimension_Name="Theater", Action_List={ Actions={ { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater" }, Key_Indexes={ { "AMERICAS" } } }, { Action_Type="Focus", Focus_Rows="True" }, { Action_Type="Dimensions", v_dims={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag", "Product Item Family" }, Xtab_Bool="False", Xtab_Flip="False" }, { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag" }, Key_Indexes={ { "AMERICAS", "ATMOS", "Latin America CS Division", "37000 CS Region", "Mexico", "", "", "", "", "DIRECT", "EMC", "N", "0" } } } } }, Num_Palette_cols=0, Num_Palette_rows=0 }, Format={ Window_Type="Tabular", Tabular={ Num_row_labels=8 } } } } } }, Widget_Set={ Widget_Layout="Vertical", Go_Button=1, Picklist_Width=0, Sort_Subset_Dimensions="TRUE", Order={ } }, Views={ { Data_Type=1, dbname="Previous Qtr Diveplan", diveline_dbname="Current Qtr Diveplan", logical_name="Current Qtr Diveplan", cols={ { name="Total TSS installs", column_type="Calc[Total TSS installs]", output_type="Number", format_string="." }, { name="TSS Valid Connectivity Records", column_type="Calc[TSS Valid Connectivity Records]", output_type="Number", format_string="." }, { name="% TSS Connectivity Record", column_type="Calc[% TSS Connectivity Record]", output_type="Number" }, { name="TSS Not Applicable", column_type="Calc[TSS Not Applicable]", output_type="Number", format_string="." }, { name="TSS Customer Refusals", column_type="Calc[TSS Customer Refusals]", output_type="Number", format_string="." }, { name="% TSS Refusals", column_type="Calc[% TSS Refusals]", output_type="Number" }, { name="TSS Eligible for Physical Connectivity", column_type="Calc[TSS Eligible for Physical Connectivity]", output_type="Number", format_string="." }, { name="TSS Boxes with Physical Connectivty", column_type="Calc[TSS Boxes with Physical Connectivty]", output_type="Number", format_string="." }, { name="% TSS Physical Connectivity", column_type="Calc[% TSS Physical Connectivity]", output_type="Number" } }, dim_cols={ { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Connect In Type", column_type="Dimension[Connect In Type]", output_type="None" }, { name="Connect Home Type", column_type="Dimension[Connect Home Type]", output_type="None" }, { name="SymmConnect Enabled", column_type="Dimension[SymmConnect Enabled]", output_type="None" }, { name="Theater", column_type="Dimension[Theater]", output_type="None" }, { name="Division", column_type="Dimension[Division]", output_type="None" }, { name="Region", column_type="Dimension[Region]", output_type="None" }, { name="Sales Order Number", column_type="Dimension[Sales Order Number]", output_type="None" }, { name="Product Item Family", column_type="Dimension[Product Item Family]", output_type="None" }, { name="Item Serial Number", column_type="Dimension[Item Serial Number]", output_type="None" }, { name="Sales Order Deal Number", column_type="Dimension[Sales Order Deal Number]", output_type="None" }, { name="Item Install Date", column_type="Dimension[Item Install Date]", output_type="None" }, { name="SYR Last Dial Home Date", column_type="Dimension[SYR Last Dial Home Date]", output_type="None" }, { name="Maintained By Group", column_type="Dimension[Maintained By Group]", output_type="None" }, { name="PS Flag", column_type="Dimension[PS Flag]", output_type="None" }, { name="Connect Home Refusal Reason", column_type="Dimension[Connect Home Refusal Reason]", output_type="None", col_width=177 }, { name="Cust Name", column_type="Dimension[Cust Name]", output_type="None" }, { name="Sales Order Channel Type", column_type="Dimension[Sales Order Channel Type]", output_type="None" }, { name="Sales Order Type", column_type="Dimension[Sales Order Type]", output_type="None" }, { name="Part Model Key", column_type="Dimension[Part Model Key]", output_type="None" }, { name="Ship Date", column_type="Dimension[Ship Date]", output_type="None" }, { name="Model Number", column_type="Dimension[Model Number]", output_type="None" }, { name="Item Description", column_type="Dimension[Item Description]", output_type="None" }, { name="Customer Classification", column_type="Dimension[Customer Classification]", output_type="None" }, { name="CS Customer Name", column_type="Dimension[CS Customer Name]", output_type="None" }, { name="Install At Customer Number", column_type="Dimension[Install At Customer Number]", output_type="None" }, { name="Install at Country Name", column_type="Dimension[Install at Country Name]", output_type="None" }, { name="TLA Serial Number", column_type="Dimension[TLA Serial Number]", output_type="None" }, { name="Product Version", column_type="Dimension[Product Version]", output_type="None" }, { name="Avalanche Flag", column_type="Dimension[Avalanche Flag]", output_type="None" }, { name="Product Family", column_type="Dimension[Product Family]", output_type="None" }, { name="Project Number", column_type="Dimension[Project Number]", output_type="None" }, { name="PROJECT_STATUS", column_type="Dimension[PROJECT_STATUS]", output_type="None" } }, Available_Columns={ "Total TSS installs", "TSS Valid Connectivity Records", "% TSS Connectivity Record", "TSS Not Applicable", "TSS Customer Refusals", "% TSS Refusals", "TSS Eligible for Physical Connectivity", "TSS Boxes with Physical Connectivty", "% TSS Physical Connectivity", "Total Installs", "All Boxes with Valid Connectivty Record", "% All Connectivity Record", "Overall Refusals", "Overall Refusals %", "All Eligible for Physical Connectivty", "Boxes with Physical Connectivity", "% All with Physical Conectivity" }, Remaining_columns={ { name="Total Installs", column_type="Calc[Total Installs]", output_type="Number", format_string="." }, { name="All Boxes with Valid Connectivty Record", column_type="Calc[All Boxes with Valid Connectivty Record]", output_type="Number", format_string="." }, { name="% All Connectivity Record", column_type="Calc[% All Connectivity Record]", output_type="Number" }, { name="Overall Refusals", column_type="Calc[Overall Refusals]", output_type="Number", format_string="." }, { name="Overall Refusals %", column_type="Calc[Overall Refusals %]", output_type="Number" }, { name="All Eligible for Physical Connectivty", column_type="Calc[All Eligible for Physical Connectivty]", output_type="Number" }, { name="Boxes with Physical Connectivity", column_type="Calc[Boxes with Physical Connectivity]", output_type="Number" }, { name="% All with Physical Conectivity", column_type="Calc[% All with Physical Conectivity]", output_type="Number" } }, calcs={ { name="Total TSS installs", definition="Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Valid Connectivity Records", definition="Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% TSS Connectivity Record", definition="Total[PS Boxes w/ valid connectivity record (1=yes)] /Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Not Applicable", definition="Total[Bozes w/ valid connectivity record (1=yes)]-Total[Boxes Eligible (1=yes)]-Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="TSS Customer Refusals", definition="Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="% TSS Refusals", definition="Total[TSS Refusals]/Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="TSS Eligible for Physical Connectivity", definition="Total[TSS Eligible]-Total[Exception]", ts_flag="Not TS Calc" }, { name="TSS Boxes with Physical Connectivty", definition="Total[PS Physical Connectivity] - Total[PS Physical Connectivity, SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% TSS Physical Connectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" }, { name="Total Installs", definition="Total[Total Installs]", ts_flag="Not TS Calc" }, { name="All Boxes with Valid Connectivty Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% All Connectivity Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]/Total[Total Installs]", ts_flag="Not TS Calc" }, { name="Overall Refusals", definition="Total[Overall Refusals]", ts_flag="Not TS Calc" }, { name="Overall Refusals %", definition="Total[Overall Refusals]/Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="All Eligible for Physical Connectivty", definition="Total[Boxes Eligible (1=yes)]-Total[Exception]", ts_flag="Not TS Calc" }, { name="Boxes with Physical Connectivity", definition="Total[Boxes w/ phys conn]-Total[Boxes w/ phys conn,SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% All with Physical Conectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" } }, merge_type="consolidate", merge_dbs={ { dbname="connectivityallproducts.mdl", diveline_dbname="/DI_PSREPORTING/connectivityallproducts.mdl" } }, skip_constant_columns="FALSE", categories={ { name="Geography", dimensions={ "Theater", "Division", "Region", "Install at Country Name" } }, { name="Mappings and Flags", dimensions={ "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "Customer Installable", "PS Flag", "Top Level Flag", "Avalanche Flag" } }, { name="Product Information", dimensions={ "Product Family", "Product Item Family", "Product Version", "Item Description" } }, { name="Sales Order Info", dimensions={ "Sales Order Deal Number", "Sales Order Number", "Sales Order Type" } }, { name="Dates", dimensions={ "Item Install Date", "Ship Date", "SYR Last Dial Home Date" } }, { name="Details", dimensions={ "Item Serial Number", "TLA Serial Number", "Part Model Key", "Model Number" } }, { name="Customer Infor", dimensions={ "CS Customer Name", "Install At Customer Number", "Customer Classification", "Cust Name" } }, { name="Other Dimensions", dimensions={ "Model" } } }, Maintain_Category_Order="FALSE", popup_info="false" } } };

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• RGB values from image into a one dimension array in c#

  - by velocityxyz

  I was wondering if there is a was a way to read rgb values from an image into a one dimensional array in C#. If it doesnt make sense, in java I would do something like this. int[] pixels; BufferedImage image = getClass().getResourceAsStream("asdfghjkl.png"); int w = image.getWidth(); int h = image.getHeight(); pixels = new int[w * h]; image.getRGB(0, 0, w, h, pixels, 0, w) ; So any help would be great, or if you can point me in the right direction, that'd be great

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• Changing DisplayMode seems not to update Input&Graphic Dimension

  - by coding.mof

  I'm writing a small game using Slick and Nifty-GUI. At the program startup I set the DisplayMode using the following lines: AppGameContainer app = new ... app.setDisplayMode( 800, 600, false ); app.start(); I wrote a Nifty-ScreenController for my settings dialog in which the user can select the desired DisplayMode. When I try to set the new DisplayMode within this controller class the game window gets resized correctly but the Graphics and Input objects aren't updated accordingly. Therefore my rendering code just uses a part of the new window. I tried to set different DisplayModes in the main method to test if it's generally possible to invoke this method multiple times. It seems that changing the DisplayMode only works before I call app.start(). Furthermore I tried to update the Graphics & Input object manually but the init and setDimensions methods are package private. :( Does someone know what I'm doing wrong and how to change the DisplayMode correctly?

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• T-SQL (SCD) Slowly Changing Dimension Type 2 using a merge statement

  - by AtulThakor

  Working on stored procedure recently which loads records into a data warehouse I found that the existing record was being expired using an update statement followed by an insert to add the new active record. Playing around with the merge statement you can actually expire the current record and insert a new record within one clean statement. This is how the statement works, we do the normal merge statement to insert a record when there is no match, if we match the record we update the existing record by expiring it and deactivating. At the end of the merge statement we use the output statement to output the staging values for the update,  we wrap the whole merge statement within an insert statement and add new rows for the records which we inserted. I’ve added the full script at the bottom so you can paste it and play around.   1: INSERT INTO ExampleFactUpdate 2: (PolicyID, 3: Status) 4: SELECT -- these columns are returned from the output statement 5: PolicyID, 6: Status 7: FROM 8: ( 9: -- merge statement on unique id in this case Policy_ID 10: MERGE dbo.ExampleFactUpdate dp 11: USING dbo.ExampleStag s 12: ON dp.PolicyID = s.PolicyID 13: WHEN NOT MATCHED THEN -- when we cant match the record we insert a new record record and this is all that happens 14: INSERT (PolicyID,Status) 15: VALUES (s.PolicyID, s.Status) 16: WHEN MATCHED --if it already exists 17: AND ExpiryDate IS NULL -- and the Expiry Date is null 18: THEN 19: UPDATE 20: SET 21: dp.ExpiryDate = getdate(), --we set the expiry on the existing record 22: dp.Active = 0 -- and deactivate the existing record 23: OUTPUT $Action MergeAction, s.PolicyID, s.Status -- the output statement returns a merge action which can 24: ) MergeOutput -- be insert/update/delete, on our example where a record has been updated (or expired in our case 25: WHERE -- we'll filter using a where clause 26: MergeAction = 'Update'; -- here   Complete source for example 1: if OBJECT_ID('ExampleFactUpdate') > 0 2: drop table ExampleFactUpdate 3:  4: Create Table ExampleFactUpdate( 5: ID int identity(1,1), 3: go 6: PolicyID varchar(100), 7: Status varchar(100), 8: EffectiveDate datetime default getdate(), 9: ExpiryDate datetime, 10: Active bit default 1 11: ) 12:  13:  14: insert into ExampleFactUpdate( 15: PolicyID, 16: Status) 17: select 18: 1, 19: 'Live' 20:  21: /*Create Staging Table*/ 22: if OBJECT_ID('ExampleStag') > 0 23: drop table ExampleStag 24: go 25:  26: /*Create example fact table */ 27: Create Table ExampleStag( 28: PolicyID varchar(100), 29: Status varchar(100)) 30:  31: --add some data 32: insert into ExampleStag( 33: PolicyID, 34: Status) 35: select 36: 1, 37: 'Lapsed' 38: union all 39: select 40: 2, 41: 'Quote' 42:  43: select * 44: from ExampleFactUpdate 45:  46: select * 47: from ExampleStag 48:  49:  50: INSERT INTO ExampleFactUpdate 51: (PolicyID, 52: Status) 53: SELECT -- these columns are returned from the output statement 54: PolicyID, 55: Status 56: FROM 57: ( 58: -- merge statement on unique id in this case Policy_ID 59: MERGE dbo.ExampleFactUpdate dp 60: USING dbo.ExampleStag s 61: ON dp.PolicyID = s.PolicyID 62: WHEN NOT MATCHED THEN -- when we cant match the record we insert a new record record and this is all that happens 63: INSERT (PolicyID,Status) 64: VALUES (s.PolicyID, s.Status) 65: WHEN MATCHED --if it already exists 66: AND ExpiryDate IS NULL -- and the Expiry Date is null 67: THEN 68: UPDATE 69: SET 70: dp.ExpiryDate = getdate(), --we set the expiry on the existing record 71: dp.Active = 0 -- and deactivate the existing record 72: OUTPUT $Action MergeAction, s.PolicyID, s.Status -- the output statement returns a merge action which can 73: ) MergeOutput -- be insert/update/delete, on our example where a record has been updated (or expired in our case 74: WHERE -- we'll filter using a where clause 75: MergeAction = 'Update'; -- here 76:  77:  78: select * 79: from ExampleFactUpdate 80: 

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• How does the "Fourth Dimension" work with arrays?

  - by Questionmark

  Abstract: So, as I understand it (although I have a very limited understanding), there are three dimensions that we (usually) work with physically: The 1st would be represented by a line. The 2nd would be represented by a square. The 3rd would be represented by a cube. Simple enough until we get to the 4th -- It is kinda hard to draw in a 3D space, if you know what I mean... Some people say that it has something to do with time. The Question: Now, that is all great with me. My question isn't about this, or I'd be asking it on MathSO or PhysicsSO. My question is: How does the computer handle this with arrays? I know that you can create 4D, 5D, 6D, etc... arrays in many different programming languages, but I want to know how that works.

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• Installing 12.04 on a Dell Dimension 2200

  - by Corn

  I'm attempting to install Ubuntu from a 12.04 live CD. A few boots have gotten me (initframs) in BusyBox. During one attempt, it actually said it had loaded Ubuntu and gave me a command prompt, before quickly taking it away. After the working attempt, the HDD was removed from the BIOS menu for some reason, but was auto-detected on a boot into Windows. I haven't got a clue what I'm doing wrong. I don't get the little Ubuntu graphical splash while it boots either, but F6 (or rather spamming many keys) will display a list of the various [starting]/[stopping] it's doing.

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• C++ MTL Library dimension.h bug?

  - by avanwieringen

  I've installed MTL on my Fedora Core 12 x64 system, but when building an application I get the following error: In file included from /usr/local/include/mtl/matrix.h:41, from /usr/local/include/mtl/mtl.h:40, from ltiSystem.hxx:4, from strTools.hxx:4, from ff.cxx:3: /usr/local/include/mtl/envelope2D.h:72: error: declaration of ‘typedef struct mtl::twod_tag mtl::envelope2D<T>::dimension’ /usr/local/include/mtl/dimension.h:19: error: changes meaning of ‘dimension’ from ‘class mtl::dimension<typename mtl::dense1D<T, 0>::size_type, 0, 0>’ make[1]: *** [ff.o] Error 1 Which would imply an error in MTL. I have changed to different MTL versions and the problem persists, but on Google there is no proper answer. I use the g++ compiler. Does anyone have a clye?

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• Highest populated dimension of an array vba

  - by Ommit

  Say i have an single dimension array (to keep it simple). Is there a simple way to tell how many entries are populated, or the highest dimension of populated entries, other than to loop through and count them? I know Ubound finds the highest dimension of the array but that's not what I need. Is there something like Ubound but it only find populated entries, or the highest dimension populated? Also, what if the array is multidimensional. I'm working in excel vba.

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• Upgrade the Graphics Card for a Dell Dimension 3100

  - by Pat Foran

  Hi, I have a Dell Dimension 3100 Desktop with a 128MB Graphics Card Integrated into the Mother Board. I need to upgrade this 128MB to at least 256MB or 512MB if the system will support same. I am told by Dell that all I have is a PCIx1 slot and that they do not stock a Graphics Card for this. I was told to shop around at Amazon and ebay etc and I would find one there. I have shopped around for some time now and do not know exactly what I am looking for. There are several PCI Graphics Card out there but which one would be the correct one for a Dell Dimension 3100. Can you help me resolve this problem. If you know of a PCIx1 card that will sort out my problem you might please let me have all the details for to purchase it. Regards, Pat,

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• Star schema [fact 1:n dimension]...how?

  - by Mike Gates

  I am a newcomer to data warehouses and have what I hope is an easy question about building a star schema: If I have a fact table where a fact record naturally has a one-to-many relationship with a single dimension, how can a star schema be modeled to support this? For example: Fact Table: Point of Sale entry (the measurement is DollarAmount) Dimension Table: Promotions (these are sales promotions in effect when a sale was made) The situation is that I want a single Point Of Sale entry to be associated with multiple different Promotions. These Promotions cannot be their own dimensions as there are many many many promotions. How do I do this?

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• Dell Dimension 2350 with a Pentium IV processor and integrated video and network chips running Fedor

  - by Jim Dobbs

  Dell Dimension 2350 with a Pentium IV processor and integrated video and network chips running Fedora12 does a "Sleeping Beauty" and I, apparently, am not am not a "handsome prince"! The system puts video and network to sleep and it will not wakeup. I have heard of this problem on laptops, but this is a tower. Any ideas or help is appreciated. I tried to ping the network card from another system and ping fails. The logs indicate that the system continues to be active. Pressing keyboard short-cut keys makes the disk light blink but neither the video or network card comes alive. Failing all else, are there any Linux commands that I could schedule in cron to pulse video and network adapters hourly that will keep them awake? Or, should I wait on Fedora13? Before this machine, I built a Dimension 2400 with Pentium IV and it had the same problem. Fedora9 on the same hardware is fine.

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