Each cell in the cube represents an aggregate value for a unique combination of each dimension. Now, we turn this fact table into a hypercube with multiple dimensions. Prod <- sample(prod_table$key, no_of_recs, replace=T, prob=c(1, 3, 2)) Time_month <- sample(month_table$key, no_of_recs, replace=T) Loc <- sample(state_table$key, no_of_recs, Here we first create a sales fact table that records each sales transaction.ĭata.frame(key=c("CA", "NY", "WA", "ON", "QU"), For example, the time dimension can be broken down into days, weeks, months, quarter and annual Similarly, location dimension can be broken down into countries, states, cities. But it is a hard rule that Dimension table NEVER points / references Fact tableĪ simple STAR schema is shown in following diagram.Įach dimension can also be hierarchical so that the analysis can be done at different degree of granularity.However, different Fact table usually share the same set of dimension tables.Also this is not a hard rule, Fact table tends to be independent of other Fact table and usually doesn't contain reference pointer among each other.This provides a sophisticated detail breakdown of the contextual aspects. Dimension tables can contain foreign keys that references other dimensional tables.In the most simple form, it is called a STAR schema. Each fact table contains foreign keys that references the primary key of multiple dimension tables.In a typical setting of Multi-dimensional model.
Each transaction records contains categorical attributes (which describes contextual aspects of the transaction, such as space, time, user) as well as numeric attributes (called "measures" which describes quantitative aspects of the transaction, such as no of items sold, dollar amount).Ī Dimension table contain records that further elaborates the contextual attributes, such as user profile data, location details. The core part of OLAP is a so-called "multi-dimensional data model", which contains two types of tables "Fact" table and "Dimension" tableĪ Fact table contains records each describe an instance of a transaction.
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In this post, I will highlight the key ideas in OLAP operation and illustrate how to do this in R.
This is a well-established field in Business Intelligence / Reporting. OLAP (Online Analytical Processing) is a very common way to analyze raw transaction data by aggregating along different combinations of dimensions.
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