Abstract: The present subject matter discloses system and method for querying a dwarf cube. The method comprises receiving a query for retrieving data from a dwarf cube comprising a set of dwarf nodes. The dwarf nodes are clustered based on group by views and a dimension index is assigned for each dwarf node. The query is processed through the clustered dwarf nodes. One or more group by views of the query is determined based on dimensions and hierarchy levels associated with the query. The clustered dwarf nodes to be searched for the query are identified according to the group by views of the query. The clustered dwarf nodes are read from the views based on the identifying. The dimension index is utilized for determining a relationship between values of different dimensions queried for the reading. The response towards the query is generated.

Abstract: The present subject matter discloses system and method for querying a dwarf cube. The method comprises receiving a query for retrieving data from a dwarf cube comprising a set of dwarf nodes. The dwarf nodes are clustered based on group by views and a dimension index is assigned for each dwarf node. The query is processed through the clustered dwarf nodes. One or more group by views of the query is determined based on dimensions and hierarchy levels associated with the query. The clustered dwarf nodes to be searched for the query are identified according to the group by views of the query. The clustered dwarf nodes are read from the views based on the identifying. The dimension index is utilized for determining a relationship between values of different dimensions queried for the reading. The response towards the query is generated.

Abstract: Systems and methods for building a distributed dwarf cube comprising dwarf cuboid using mapreduce technique are disclosed. Data comprising cube values and a cube definition may be received. The cube definition comprises dimensions defined for the cube values. The data received is processed. The data may be transformed to a format. Based upon the format of the data, indexes may be generated. The cube values in one or more dimensions may be sorted based on a cardinality of the cube values. The cube values may be sorted in an order of highest cardinality to lowest cardinality. The cardinality indicates distinctiveness of the cube values in the one or more dimensions. The data may be partitioned into data blocks. A dwarf cuboid may be built for one or more data blocks based upon the order of the cardinality of the cube values.

Abstract: Systems and methods for querying a distributed dwarf cube are disclosed. A query for retrieving data from a distributed dwarf cube is received. The distributed dwarf cube is built of the data. The data comprises cube values. The distributed dwarf cube is built by processing the data to generate indexes for the data. The cube values in one or more dimensions are sorted based on a cardinality of the cube values. The data is partitioned into data blocks to build distributed dwarf cube from each data block based upon the cardinality of the cube values. The distributed dwarf cube comprises one or more ranges defined for the cube values. The one or more ranges of the cube values are checked based upon the query. Using the cube values, a list is created. The list of the cube values is transmitted from the distributed dwarf cube corresponding to the query.

Abstract: Systems and methods for building a dwarf data structure with reduced size and improved query performance is disclosed. The system is configured to perform three major steps for reducing size of Dwarf data structure and improving query performance. In the first step, the system is configured to reducing the Dwarf data structure size by physical compression of the clustered node blocks of the Dwarf data structure when writing the nodes on a disk. In the second step, the system is configured to improve query performance by look-ahead reading, wherein an entire block of nodes is loaded into random access memory, as there is a very high probability of occurrence of the nodes required to be accessed from same block. In the third step, the system is configured to reduce the number of nodes/blocks read while serving range queries thereby improving query performance while retrieving data from Dwarf data structure.

Abstract: Systems and methods for building a distributed dwarf cube comprising dwarf cuboid using mapreduce technique are disclosed. Data comprising cube values and a cube definition may be received. The cube definition comprises dimensions defined for the cube values. The data received is processed. The data may be transformed to a format. Based upon the format of the data, indexes may be generated. The cube values in one or more dimensions may be sorted based on a cardinality of the cube values. The cube values may be sorted in an order of highest cardinality to lowest cardinality. The cardinality indicates distinctiveness of the cube values in the one or more dimensions. The data may be partitioned into data blocks. A dwarf cuboid may be built for one or more data blocks based upon the order of the cardinality of the cube values.

Abstract: Systems and methods for querying a distributed dwarf cube are disclosed. A query for retrieving data from a distributed dwarf cube is received. The distributed dwarf cube is built of the data. The data comprises cube values. The distributed dwarf cube is built by processing the data to generate indexes for the data. The cube values in one or more dimensions are sorted based on a cardinality of the cube values. The data is partitioned into data blocks to build distributed dwarf cube from each data block based upon the cardinality of the cube values. The distributed dwarf cube comprises one or more ranges defined for the cube values. The one or more ranges of the cube values are checked based upon the query. Using the cube values, a list is created. The list of the cube values is transmitted from the distributed dwarf cube corresponding to the query.