Abstract: An adaptive digital tree data structure incorporates various codes within the parent pointers to indicate the state of the underlying subexpanses. The use of these codes reduces the amount of memory used in storage for the subexpanses and improves the overall efficiency of the system. The structure of the system is flexible so that if values stored within a fully populated expanse are removed, the adaptive digital tree data structure will adopt the most memory efficient representation of the structure available.

Abstract: An adaptive digital tree data structure supports scalability by encoding type bits within unused data bits of the root pointer word or, as the population increases to support it, into an additional word. In both instances the type bits included in the word or additional word contain dataset-global data which pertains to the underlying data structure. The information contained within the dataset-global data represents the total population of the tree or the amount of memory required to support the tree which may be used to determine the global memory efficiency of the data structure.

Abstract: A data structure and related data storage and retrieval method rapidly provide a count of elements stored or referenced by a hierarchical structure of ordered elements (e.g., a tree), access to elements based on their ordinal value in the structure, and identification of the ordinality of elements. In an ordered tree implementation of the invention, a count of elements stored in each subtree is stored, i.e., the cardinality of each subtree is stored either at or associated with a higher level node pointing to that subtree or at or associated with the head node of the subtree. In addition to data structure specific requirements (e.g., creation of a new node, reassignment of pointers, balancing, etc.) data insertion and deletion includes steps of updating affected counts. Elements may be target data itself (e.g., data samples, prime numbers); keys or indices associated with target data (e.g., social security numbers of employees, product numbers and codes, etc. uses to reference associated data records, etc.

Abstract: An adaptive digital tree data structure incorporates a rich pointer object, the rich pointer including both conventional address redirection information used to traverse the structure and supplementary information used to optimize tree traversal, skip levels, detect errors, and store state information. The structure of the pointer is flexible so that, instead of storing pointer information, data may be stored in the structure of the pointer itself and thereby referenced without requiring further redirection.

Abstract: An adaptive digital tree data structure incorporates a rich pointer object, the rich pointer including both conventional address redirection information used to traverse the structure and supplementary information used to optimize tree traversal, skip levels, detect errors, and store state information. The structure of the pointer is flexible so that, instead of storing pointer information, data may be stored in the structure of the pointer itself and thereby referenced without requiring further redirection. The digital tree data structure is self-modifying based on a digital tree (or “trie”) data structure which is stored in the memory, can be treated as a dynamic array, and is accessed through a root pointer. For an empty tree, this root pointer is null, otherwise it points to the first of a hierarchy of branch nodes of the digital tree.

Abstract: An adaptive digital tree data structure incorporates various codes within the parent pointers to indicate the state of the underlying subexpanses. The use of these codes reduces the amount of memory used in storage for the subexpanses and improves the overall efficiency of the system. The structure of the system is flexible so that if values stored within a fully populated expanse are removed, the adaptive digital tree data structure will adopt the most memory efficient representation of the structure available.

Abstract: An adaptive digital tree data structure incorporates a rich pointer object, the rich pointer including both conventional address redirection information used to traverse the structure and supplementary information used to optimize tree traversal, skip levels, detect errors, and store state information. The structure of the pointer is flexible so that, instead of storing pointer information, data may be stored in the structure of the pointer itself and thereby referenced without requiring further redirection. The digital tree data structure is self-modifying based on a digital tree (or “trie”) data structure which is stored in the memory, can be treated as a dynamic array, and is accessed through a root pointer. For an empty tree, this root pointer is null, otherwise it points to the first of a hierarchy of branch nodes of the digital tree.

Abstract: An adaptive digital tree data structure supports scalability by encoding type bits within unused data bits of the root pointer word or, as the population increases to support it, into an additional word. In both instances the type bits included in the word or additional word contain dataset-global data which pertains to the underlying data structure. The information contained within the dataset-global data represents the total population of the tree or the amount of memory required to support the tree which may be used to determine the global memory efficiency of the data structure.

Abstract: An adaptive digital tree data structure incorporates a rich pointer object, the rich pointer including both conventional address redirection information used to traverse the structure and supplementary information used to optimize tree traversal, skip levels, detect errors, and store state information. The structure of the pointer is flexible so that, instead of storing pointer information, data may be stored in the structure of the pointer itself and thereby referenced without requiring further redirection.

Abstract: A data structure and related data storage and retrieval method rapidly provide a count of elements stored or referenced by a hierarchical structure of ordered elements (e.g. a tree), access to elements based on their ordinal value in the structure, and identification of the ordinality of elements. In an ordered tree implementation of the invention, a count of elements stored in each subtree is stored, i.e., the cardinality of each subtree is stored either at or associated with a higher level node pointing to that subtree or at or associated with the head node of the subtree. In addition to data structure specific requirements (e.g., creation of a new node, reassignment of pointers, balancing, etc.) data insertion and deletion includes steps of updating affected counts. Elements may be target data itself (e.g., data samples, prime numbers); keys or indices associated with target data (e.g., social security numbers of employees, product numbers and codes, etc. uses to reference associated data records, etc.