Abstract: A technique for representing the structure of hierarchically-organized data in a non-hierarchical data structure, such as a relation. The hierarchically-organized data is represented as a tree, and each node in the tree is assigned a position identifier that represents both the depth level of the node within the hierarchy, and its ancestor/descendant relationship to other nodes. The data represented by each node, as well as its position identifier, is stored in a row of a relational database, thereby capturing the hierarchical structure of the data in such relational database. A technique is provided for the compressed storage of position identifiers in a format that allows an efficient bytewise comparison of position identifiers to determine relative order and ancestry.

Abstract: A technique for representing the structure of hierarchically-organized data in a non-hierarchical data structure, such as a relation. The hierarchically-organized data is represented as a tree, and each node in the tree is assigned a position identifier that represents both the depth level of the node within the hierarchy, and its ancestor/descendant relationship to other nodes. The data represented by each node, as well as its position identifier, is stored in a row of a relational database, thereby capturing the hierarchical structure of the data in such relational database. A technique is provided for the compressed storage of position identifiers in a format that allows an efficient bytewise comparison of position identifiers to determine relative order and ancestry.

Abstract: Any string in any character set with an arbitrary-leveled weight-based comparison system is transformed into a bitstring in such a way that two transformed strings can be compared byte-by-byte. The resulting bit string has the minimum possible maximum length. The transformed bit strings can be inverted—meaning the original string can be recovered from the transformed string.

Abstract: A technique for representing the structure of hierarchically-organized data in a non-hierarchical data structure, such as a relation. The hierarchically-organized data is represented as a tree, and each node in the tree is assigned a position identifier that represents both the depth level of the node within the hierarchy, and its ancestor/descendant relationship to other nodes. The data represented by each node, as well as its position identifier, is stored in a row of a relational database, thereby capturing the hierarchical structure of the data in such relational database. A technique is provided for the compressed storage of position identifiers in a format that allows an efficient bytewise comparison of position identifiers to determine relative order and ancestry.

Abstract: Any string in any character set with an arbitrary-leveled weight-based comparison system is transformed into a bitstring in such a way that two transformed strings can be compared byte-by-byte. The resulting bit string has the minimum possible maximum length. The transformed bit strings can be inverted—meaning the original string can be recovered from the transformed string.

Abstract: A technique for representing the structure of hierarchically-organized data in a non-hierarchical data structure, such as a relation. The hierarchically-organized data is represented as a tree, and each node in the tree is assigned a position identifier that represents both the depth level of the node within the hierarchy, and its ancestor/descendant relationship to other nodes. The data represented by each node, as well as its position identifier, is stored in a row of a relational database, thereby capturing the hierarchical structure of the data in such relational database. A technique is provided for the compressed storage of position identifiers in a format that allows an efficient bytewise comparison of position identifiers to determine relative order and ancestry.