Methods and systems for biodirectional indexing

Abstract

In an aspect, provided is a method comprising receiving a data model, generating a bidirectional table index (BTI) based on the data model, generating a bidirectional association index (BAI) based on the data model and the bidirectional table index, and loading a portion of the data model, the BAI, and the BTI in-memory.

1 . A method comprising:
 receiving a data model;   generating, based on the data model, a first bidirectional table index (BTI), a second BTI, and a third BTI;   generating a bidirectional association index (BAI) based on the data model, the second BTI, and the third BTI, wherein a number identifying a position of the BAI corresponds to a row number of the second BTI; and   loading a portion of the data model, the BAI, and at least one of the first BTI, the second BTI, or the third BTI in-memory.   
     
     
         2 . The method of  claim 1 , wherein a value at the position of the BAI corresponds to a row number of the third BTI. 
     
     
         3 . The method of  claim 1 , wherein one or more of the first BTI, the second BTI, or the third BTI comprises a hash index. 
     
     
         4 . The method of  claim 1 , wherein the data model comprises a plurality of tables, wherein each table of the plurality of tables comprises at least one row and at least one column. 
     
     
         5 . The method of  claim 4 , further comprising generating a plurality of BTIs, wherein a BTI is generated for each column of each of the plurality of tables. 
     
     
         6 . The method of  claim 1 , further comprising:
 determining an update to the data model;   regenerating one or more of the first BTI, the second BTI, or the third BTI based on the updated data model; and   regenerating the BAI based on the updated data model and the regenerated one or more of the first BTI, the second BTI, or the third BTI.   
     
     
         7 . The method of  claim 1 , wherein loading the portion of the data model in-memory comprises sequentially loading the portion of the data model in-memory. 
     
     
         8 . A method comprising:
 generating a first bidirectional table index (BTI), a second BTI, a third BTI, and a bidirectional association index (BAI), wherein a number identifying a position of the BAI corresponds to a row number of the second BTI;   recalculating a state space based on a user selection in a user interface, the BAI, and one or more of the first BTI, the second BTI, or the third BTI, wherein the user interface comprises one or more objects representing data in the state space; and   providing an updated version of the user interface comprising one or more objects updated according to the state space based on the user selection.   
     
     
         9 . The method of  claim 8 , wherein recalculating the state space comprises querying the state space to gather all combinations of dimensions and values to perform the recalculation. 
     
     
         10 . The method of  claim 8 , wherein the user selection comprises a first attribute. 
     
     
         11 . The method of  claim 10 , wherein recalculating the state space based on the user selection comprises:
 accessing the first BTI to determine which of a plurality of rows of a first table comprise the first attribute, wherein the first BTI is associated with the first table;   generating a row state indicating which of the plurality of rows of the first table comprise the first attribute;   comparing the row state to an inverted index of the second BTI to determine a second attribute, wherein the second BTI is associated with the first table; and   identifying the second attribute as associated with the first attribute.   
     
     
         12 . The method of  claim 8 , wherein a value at the position of the BAI corresponds to a row number of the third BTI. 
     
     
         13 . The method of  claim 8 , further comprising determining a binary state of each field and of each data table of a data source, resulting in the state space. 
     
     
         14 . The method of  claim 13 , wherein determining the binary state comprises generating the first BTI, the second BTI, the third BTI, and the BAI. 
     
     
         15 . A method comprising:
 receiving a user selection of data, wherein the data comprises one or more tables;   determining, based on a user selection of data, distinct values in one or more tables of the data using a bidirectional association index (BAI) and one or more of a first bidirectional table index (BTI), a second BTI, or a third BTI, wherein a number identifying a position of the BAI corresponds to a row number of the second BTI;   performing a first calculation on the distinct values; and   generating a graphical object based on the first calculation.   
     
     
         16 . The method of  claim 15 , wherein performing the first calculation on the distinct values results in a hypercube. 
     
     
         17 . The method of  claim 16 , wherein the graphical object comprises the hypercube. 
     
     
         18 . The method of  claim 15 , wherein a value at the position of the BAI corresponds to a row number of the third BTI. 
     
     
         19 . The method of  claim 15 , wherein the user selection comprises a first attribute. 
     
     
         20 . The method of  claim 19 , wherein determining, based on the user selection of data, the distinct values in the one or more tables of the data using the BAI and one or more of the first the BTI, the second BTI, or the third BTI comprises:
 accessing the first BTI to determine which of a plurality of rows of a first table comprise the first attribute, wherein the first BTI is associated with the first table;   generating a row state indicating which of the plurality of rows of the first table comprise the first attribute;   comparing the row state to an inverted index of the second BTI to determine a second attribute, wherein the second BTI is associated with the first table; and   identifying the second attribute as associated with the first attribute.