olap

问题 I need to define a calculated member in MDX (this is SAS OLAP, but I'd appreciate answers from people who work with different OLAP implementations anyway). The new measure's value should be calculated from an existing measure by applying an additional filter condition. I suppose it will be clearer with an example: Existing measure: "Total traffic" Existing dimension: "Direction" ("In" or "Out") I need to create a calculated member "Incoming traffic", which equals "Total traffic" with an

问题 I have an ecommerce system where users go through a few stages before they make a purchase (visit website, browse catalog, pick a product, goto purchase page, insert credit details etc...). Each event is saved in the following SQL events table: SessionId EventTypeId (PageView, Click, Type), EventValue (HomePage, Button-1 etc...) Timestamp Each session is saved in the following Sessions table (simplified): SessionId SelectedProductId SessionPurchaseAmount (0 for sessions without a purchase) My

问题 I know how to connect to a MS OLAP cube using Python on Windows - well, at least one method :-). Usually I use the win32py package and invoke a .COM object to connect: import win32com.client connection = win32com.client.Dispatch(r'ADODB.Connection') connString = 'Connection String to MSOLAP CUBE' connection.Open(connString) However, now I want to do the same with Linux (RHEL 7.2), and I have no idea how to do that. Can you please help me with that? What I tried so far: I tried using pyodbc

问题 I have a Purchase FactTable with some measures and dimension keys. Then, there's another another table: Discount Table. Purchase FactTable is in a 1-N relationship with Discount Table (for each purchase I might have bought several discounted items). Discount table has some attributes (description, note) and some numeric values (for example: discount in $) that I would like to roll-up. If I create a dimension out of this Discount Table, I'll get a wrong number of purchase counts in a sum count

问题 As is usual with cubes, the users want things that don't fit into a hierarchy to be displayed hierarchically. They'd like to see Day > Week > Month > Quarter > Year as the hierarchy, but the problem with weeks is they can be part of 1-2 months, not just 1 month (and by extension part of 2 quarters, semesters, years). So my question is: how to set up the attribute relationships, and how to set up the hierarchy? Here is what I have, but I know it's not optimal. Hierarchies (cycle == weeks):

问题 In one local financial institution I was rebuked by their programmers for expressing them my opinion that (their programmers' obsession with) (b)locking issues in their MS SQL Server 2005 OLAP (SSAS) database(s) did not make much sense to me. (The OLTP databases are SQL Server, Oracle and non-RDBMS ERP). What are locking issues in OLAP processing if OLAP (SSAS) databases are used only for reading (read-only after deployment)? In which context do such (an whic?) issues arise - during SSIS

问题 There is a problem when several users can process the same cube simultaniously and as a result processing of cube fails. So I need to check if certain cube is processing at current moment. 回答1: I dont think you can prevent a cube from being processed if someone else is already processing it. What you can do to "help" is run a MDX query to check the last time the cube was processed: SELECT CUBE_NAME, LAST_DATA_UPDATE FROM $System.MDSCHEMA_CUBES or check the sys.process table on the realted sql

问题 We are migrating an ActivePivot application to a new server (4 sockets Intel Xeon, 512GB of memory). After deploying we launched our application benchmark (that's a mix of large OLAP queries concurrent to real-time transactions). The measured performance is almost twice slower than on our previous server, that has similar processors but twice less cores and twice less memory. We have investigated the differences between the two servers, and it appears the big one has a NUMA architecture (non

问题 I'm working for a company running a software product based on a MS SQL database server, and through the years I have developed 20-30 quite advanced reports in PHP, taking data directly from the database. This has been very successful, and people are happy with it. But it has some drawbacks: For new changes, it can be quite development intensive The user can't experiment much with the data - it is locked to a hard-coded view It can be slow for big reports I am considering gradually going to a

问题 I am in process of designing a Data Warehouse Architecture. While exploring various options to Extract data from Production and putting into Data Warehouse, I came across many articles which mainly suggested following two approaches - Production DB ----> Data Warehouse (Star Schema) ----> OLAP Cube Production DB ----> Staging Database ----> Data Warehouse (Star Schema) ----> OLAP Cube I am still not sure which one is the better approach in terms of Performance and reducing processing load on