MOLAP

MOLAP is a Multidimensional, Open-Source, In-Memory, Object-Oriented, and Embeddable API for aggregating data through computationally efficient multidimensional cubes developed by Macrofocus. It supports the usual drill-down, drill-up, slicing, dicing, and pivot operations, alongside a wide choice of built-in aggregation functions, with the following distinctive features:

• Indexed data structured similarly to R and Pandas dataframe
• Arbitrary group by without the need to describe the cube structure
• Automatic optimization of the cheapest aggregation path
• Just-in-time calculation of aggregates
• Drill-across queries
• Expressive programming style through fluent interfaces
• Custom aggregation functions
• Java implementation

Conceptually it works by:

• Proving the input data as a table (DataFrame interface)
• Creating a data cube (Cube interface, with InMemoryDataCube providing the default implementation)
• Grouping the data into cuboids (Cuboid interface) along some nested dimensions (Dimensions interface)
• Drilling-down (Cuboid.drillDown()), Drilling-up (Cuboid.drillUp()), Slicing (Cuboid.slice())and dicing (Cuboid.dice()) the cuboids
• Defining how the data should be aggregated (Aggregation interface)
• Extracting data from the cube through by placing queries (Query interface)

Future plans includes:

• Parallel and distributed processing
• Creation and aggregation of time series
• Aggregation of geospatial objects
• Real-time and incremental updates made to the input table
• Persistance of the data cube
• Query language (potentially with MDX query parser and support for DAX expressions)
• XML/A Server
• Port to other languages (e.g. JavaScript)