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time series - mining structure does not exist

Posted By:      Posted Date: April 10, 2011    Points: 0   Category :
 

I have successfully created a time series model using DMX code, but I haven't been able to export the predictions to my SQL database - the results are too large to copy/paste and I haven't been able to figure out code that will allow me to copy it directly to my SQL database. I know you have the option to save predictions to a SQL table in Visual Studio, so I'm trying to recreate the model using the new mining structure wizard on an entirely new SSAS database in Visual Studio. When I go to deploy the structure, it tells me that the mining structure does not exist. I'm confused because I'm trying to create the structure - of course it doesn't exist. Does anyone have any advice?


Myles McKee


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time series model - mining structure does not exist

  

I have successfully created a time series model using DMX code, but I haven't been able to export the predictions to my SQL database - the results are too large to copy/paste and I haven't been able to figure out code that will allow me to copy it directly to my SQL database. I know you have the option to save predictions to a SQL table in Visual Studio, so I'm trying to recreate the model using the new mining structure wizard on an entirely new SSAS database in Visual Studio. When I go to deploy the structure, it tells me that the mining structure does not exist. I'm confused because I'm trying to create the structure - of course it doesn't exist. Does anyone have any advice?


Myles McKee

Optimizing Data Mining processing time for a Time Series model in SQL Server 2008 R2

  

Does anyone have any references around optimizing the initial processing time for a data mining model? Books, blogs, etc. I have the Wiley “Data Mining with SQL Server 2008” book, and while I’ve learned a lot from it, it doesn’t seem to cover much around trouble shooting things like processing time. I also have a few other books that have a chapter or two on data mining, but again just basic “here’s what it is and how to set it up”, nothing that quite covers trouble shooting or optimization. I’ve also checked out the various Data Mining blogs/sites.

 

I’ve got a Microsoft Time Series model I’m basing on a Cube. Very simple, trying to forecast sales. I have one dimension which is the list of products (about 1600), a second which is the time dimension, finally the measure is daily sales figures for each, about 3.2 million rows in total. On a brand new server, with two quad core processors and 16 gig of ram it took 40 hours to process. Seems rather high?

time series data mining help

  

I have sales data for several sku's over 2 years by month. (by location but that isn't necessary atm)

How do I use the data mining tool to give me strait predictive sales from this pocket of data.

 

I have cubed it, I have run the time series where I have to pick partnumberDim for start, and then nest the date dim.

But I can't seem to make any sense of what this is telling me.


foxjazz

Errors in Olap Mining Models (Time Series)

  

1.    In a model that is

Time series algorithm parametrs

  
Hello everybody! I have the following questions to You: I have mining model with data by working hours(every day from 08AM-05PM). What PEREODICITY_HINT I have to use? {24} or {9} The second problem is that on the chart I see historical data wiht axis format 08AM-05PM, but in prediction part the periods is 00-24. It is not correct! I want to see the same 08AM-05PM periods too!   Help me please!   Thank You!  

what's the Best practice about exploiting the time series predections history?

  
Hi all, We are predicting our revenue with two frequency : The first is monthly , the second is weekly with the same parameters daily . The first aimed to have a big picture of our performance during the month , the second is more operational and it's used to drive operational action. So every day we train the second structure with all data and  MTS predicts different value each day basis on the new data used in training. Example: Day of process : 05/07/2010 data used to train : 01/01/2002 ---->05/07/2010 values predicted between : 06/07/2010------>31/07/2010 the value for 18/07/2010 : 35000 $ ------------------------------------------------------------------- Day of process : 10/07/2010 data used to train : 01/01/2002 ---->10/07/2010 values predicted between : 11/07/2010------>07/08/2010 the value for 18/07/2010 : 42000 $   Certainly The second method (predict daily with new training data)  value will be more accruate but it can't be used to drive strategy because it's change every day. Any reflex about this ? saving all data (all prediction for all series every day for the 30 coming steps)? or update coming value with the actualised prediction(the manager will be confused )  

Building a Mining Structure from a Cube

  
Hi, I have a problem with creating a time series mining structure from an existing cube. I get the error: The data type specified for the  'Date' column of the 'Purchase Date' OLAP mining structure is not compatible with its source attribute   My date column in this dimension is in fact a Datetime, so it should be the right type for this exercise.  I wonder, am I getting this error because the cube dimension has a Name column of type Varchar specified even though the Key column is dateime?   Thanks, Steve

Building a Mining Structure from a Cube

  
Hi, I have a problem with creating a time series mining structure from an existing cube. I get the error: The data type specified for the  'Date' column of the 'Purchase Date' OLAP mining structure is not compatible with its source attribute   My date column in this dimension is in fact a Datetime, so it should be the right type for this exercise.  I wonder, am I getting this error because the cube dimension has a Name column of type Varchar specified even though the Key column is dateime?   Thanks, Steve

Advice on collecting data in a time series

  
Hi everyone,  I just wanted to get some ideas of what you would think be the best way to collect data/numbers that are part of a time series?  Let's say I'm collecting monthly data from the users, related to some product and I'd like to be able to provide them a simple and efficient way of entering these numbers based on some month end period.  So for instance on 6/30/2009, they could enter some numbers for a set of data points that pertain to that product.  Would one of the data controls (such as GridView or DetailsView) be sufficient to do this?  I know the GridView isn't so much able to save data but I believe the DetailsView has some functionality for that. In the end, I'd really like to provide a seemless way to do show this and ability to enter and save this.  Any ideas would be appreciated.Thanks

Best time to save "structure" session variable

  

I have a structure that groups a number of fields together that I want to use to retain values between different web pages. My plan was to transfer the structure to a session variable and retrieve it as each page loads. In theory each of the different fields could be stored in and retrieved from separate session variables but the structure approach seemed more elegant.

I was initially assuming I would be a "sensible time" (e.g. specific event) when I could consistently save the structure content to the session variable but I realise that I may be on the wrong track and that it may be necessary to transfer to the session variable to the structure each time one of the individual field values is updated as there is no way of determining when the user is about to move to another page.

Is there a solution to this or a better approach?


Unusual results using Time Series Algorithm

  

I am trying to forecast Q4 2010 activity.  The dataset is constant with no gaps from 200401 - 200909.  I want to compare my model with the actual Q4 2009 to verify its accuracy.  The dataset has two columns (month - Key Time and Sales - Predict Only).  I set the PERIODICITY_HINT to {12} since our data is very seasonal.  The problem is every December has increased in sales from the previous year in the dataset, but the prediction fails to recognize this trend.  Is the problem that for 2009 I only give 9 months?  Please help.  A snapshot of the data set is below:

Dataset:

 

200401 866 200402 709 200403 748 200404 1053 200405 829 200406 957 200407 872 200408 917 200409

892

...

 

200907 3909 200908 3829 200909

3976

 

 

 


Cannot assign multiple keys in the Time Series Algorithm

  

We had a small problem, which we really need to know how to solve as soon as possible, as I told you in our last session that we are running out of time, and this issue is stopping us from completing our predictions. I will try to mention our exact business case and explain it as much detailed as I can so you can help us as soon as possible.

 

                                Kindly check the following scenario:

 

                                Table name: X

                                Columns: ID [Identity column], DATE_TIME [its granularity is hour], NODE_NAME, REASON, REQUESTS

                          

Converting daily snapshots into format for time series analysis

  

I wasn't sure if this was an app question or a database question, so I'll start here first ...

Desired End State:
I'd like a process/app/database/whatever that converts daily inventory snapshots into one or two tables for easy analysis over time. Ideally, I'd like the solution to be efficient, flexible, and resource-sensitive.

What is the best way to go about this?

Current State/Background:
I'm working with a vendor who sends me daily Excel sheets with assets under management. The reports are based a snapshot of master records from the inventory system. For discussion, let's say a sample looks like this:

AssetID, Serial#, Status
123456, ABC123, Deployed
365494, D2-F39, Retired
B63489, 123GR2, Pending

The inventory is mostly static. Of the 40K rows in the spreadsheet, only 20 or so change from day-to-day. Further, once an asset is retired, its record never changes again.

Because they're complete snapshots of the master data, the spreadsheets are massive (over 40K rows, currently around 40 MB each). This makes them awkward to work with.

I have two main business requirements:
1) From today's spreadsheet, I need an easy way to identify the 20 rows that changed yesterday.
2) For a specific asset, I need to trac

i can't process and run my mining structure in SSBIDS (SQL Server 2008 R2)

  

Hello.

i'am a new user in SQL Server 2008 R2. i'am working in DATA Mining project. in one of my tests working with adventureworksDW2008R2

So after creating the data source and the data source view, and the mining structure using the bike buyer prediction...

i could process and deploy, then when i click run a failure message appears telling me

"Internal error: The operation terminated unsuccessfully."

"Server: The operation has been cancelled."

"The datasource, 'Adventure Works DW2008R2', contains an ImpersonationMode that is not supported for processing operations."

"Errors in the high-level relational engine. A connection could not be made to the data source with the DataSourceID of 'Adventure Works DW2008R2', Name of 'Adventure Works DW2008R2'."

Please anyone can help me in this one??????????????????

 


badrou zeggar

time series

  

hello,

I am wondering what I need to do to use the time series algorithm on a table that is like this. (part from a table from a relational db)

table is like this:

 

memberid--- joindate --- leavedate

1 --- nov 11 2010 --- NULL

2 --- mrt 05 2010 --- sep 09 2010 

And I want to analyse/predict the expected number of members    for a month or a year

 

any help would be appreciated!

 

 


Error when building a Data Mining Structure with nested tables on cubes

  

Apparently there is a bug in Analysis Services (2008R2) when building a Data Mining Structure (Time Series model) with nested tables on cubes. If the dimension key has a column name, AS becomes confused and gives an error that do not allow to process the structure.

"Error (Data mining): The data type specified for the  'xxxxx' column of the 'nnnnnn' OLAP mining structure is not compatible with its source attribute ('xxxxx' in the 'dddddd' cube dimension")

 Everything is solved by removing the name column, in the attribute of the dimension used. This bug was already reported for SQL2005 in: http://social.msdn.microsoft.com

Error Processing Mining Structure - Errors in the high-level relational engine.

  

Hi community,

I am trying to learn some datamining with SQL Server. I have some demo projects I am trying to run under my test environment: Windows Server 2008 + SQL Server 2008 R2 Enterprise. Sample projects seems to be ok, but when I try to process a mining structure, I get this error:

"Errors in the high-level relational engine. The following exception occurred while an operation was being performed on a data source view: The type initializer for 'System.Xml.Schema.XmlSchemaComplexType' threw an exception.;Bad IL range.."

Any ideas?

Thanks in advance

 

 


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