Abstract: Typically, field names are saved separately from tables as metadata in modern databases. Databases did not traditionally get built into interlocking trees datastores that recorded the data as events. However, in cases where one may wish to do that, thus avoiding the need for saving separate metadata from the table data of the data base, a need was found to establish an identity for particular columns or fields when working with databases or sources of data that provide table data in field/record format. So, to build interlocking trees datastores from such records a mechanism to record such data was created, adding a column ID, preferably to each field within each record or sequence that is to be recorded. Putting the column ID or identifier is inserted into the record during particlization between each column variable. In preferred embodiments a delimiter was included between the column ID or field name and the field variable. Appropriate hardware and software systems were employed to implement the invention.

Abstract: The KStore or K is a datastore made up of a forest of interconnected, highly unconventional trees of one or more levels. Values of elemental root nodes within a KStore can be changed without requiring the KStore to be rebuilt. The elemental root node whose value is to be altered is identified, and the new value is inserted directly into the node or in the area identified by the pointer to the node value within the node. Values can include those needed to recognize user data and/or values for elemental root nodes used internally within the KStore structure. Values of the elemental root nodes can be updated after the instantiation of the KStore and the population of the KStore.

Abstract: A method for accessing a KStore including at least one K path having at least one subcomponent node and at least one root node includes providing a utility routine and accessing the KStore using the utility routine. The accessing may include traversing the at least one K path. The at least one K path includes a further node and bidirectional links between the at least one subcomponent node and the further node. The method includes traversing between the at least one subcomponent node and the further node by way of the bidirectional links. The further node can be a beginning of thought node, an end of thought node, root node, or a subcomponent node. A location may be determined within the KStore. The K Store may be queried. Searches for information within the KStore may be performed. The KStore may be traversed to constrain and focus the KStore. The KStore may be updated.

Abstract: An effective means for building an interlocking trees datastore from multiple data sources that contained within the datastore the knowledge of the origin of the data was needed, and is thus described herein. Should it be desired or necessary to identify the different sources of data within the Kstore data structure, the present invention teaches how an additional field for the origin of the data is created, how the origin of the data is stored in that newly created field, and how the interlocking trees data store takes that field and creates nodes within the structure, therefore embedding the origin information within the knowledge contained in the Kstore data structure.

Abstract: A method for accessing a KStore including at least one K path having at least one subcomponent node and at least one root node includes providing a utility routine and accessing the KStore using the utility routine. The accessing may include traversing the at least one K path. The at least one K path includes a further node and bidirectional links between the at least one subcomponent node and the further node. The method includes traversing between the at least one subcomponent node and the further node by way of the bidirectional links. The further node can be a beginning of thought node, an end of thought node, root node, or a subcomponent node. A location may be determined within the KStore. The K Store may be queried. Searches for information within the KStore may be performed. The KStore may be traversed to constrain and focus the KStore. The KStore may be updated.

Abstract: A set of mechanisms handles communication with a Knowledge Store and its K Engine(s). The Knowledge Store (Kstore) does not need indexes or tables to support it but instead is formed by the construction of interlocking trees of pointers in nodes of the interlocking trees. The K Engine builds and is used to query a KStore by using threads that use software objects together with a K Engine to learn particlized events, thus building the KStore, and these or other software objects can be used to make queries and get answers from the KStore, usually with the help of a K Engine. Under some circumstances, information can be obtained directly from the KStore, but is generally only available through the actions of the K Engine. The mechanisms provide communications pathways for users and applications software to build and/or query the KStore. Both these processes can proceed simultaneously, and in multiple instances. There can be a plurality of engines operating on a KStore essentially simultaneously.

Abstract: A method for transmitting information in a KStore system having a KStore, an application programming interface and an application layer wherein the information is transmitted between the KStore and the application layer includes transmitting the information between the KStore and the application layer by way of the application programming interface. The KStore is constrained by way of the application programming interface with at least one constraint to provide at least one selected K path. The KStore is focused to provide a further selected K path. At least one KStore parameter is determined in accordance with the selected K path to provide at least one determined K parameter. The constraining of the KStore includes traversing the at least one selected K path.

Abstract: A method for processing a particle stream in a KStore having a current sequence of nodes includes receiving at least one particle within the particle stream to provide a received particle and determining whether the current sequence is complete in accordance with the received particle to provide a sequence complete determination. Processing a completed sequence indicator in accordance with the sequence complete determination is also set forth. A determination is made whether the received particle is a delimiter to provide a delimiter determination and a received delimiter. A determination is made whether a Result node of an asCase node of the current K node matches the received delimiter to provide a match determination. A new K node is created in accordance with the match determination. The new K node can be an end product node. The current K node is set to the end product node.

Abstract: A method for recording information in an interlocking trees datastore having a plurality of K paths includes receiving an input particle and building a new K node in accordance with the received input particle. A K node is locked in accordance with the building of the new K node to provide a locked node. The locked node can be the Case node of the new K node. The Case bi-directional link between the locked Case node and the new K node is completed while the locked Case node is locked. A pointer is added to the new K node to an asCase list of the locked Case node. The locked node is locked only while adding the pointer to the new K node to the asCase list of the locked Case node. The locked node can also be a Result node of the new K node.

Abstract: In learning for an interlocking trees datastore or KStore, the process is made more efficient by noting the (n-level) address within the KStore during the learning of each particle. In a pre-particle stream of data, which may be organized within or before the Learn Engine prior to this, “Marks” and “References” are inserted. Each Mark identifies where any number of References may start the learning process, enabling the avoidance of re-learning redundant data. Thus, in a field record data set, the redundant data fields (or even partial fields) can be skipped over and only the new data learned. The Marks and References are removed before processing into a particle stream. When particles are learned the K Engine returns the n-level address or pointer(s) which the Learn Engine uses to associate with the relevant Reference(s). The system can be implemented in hardware if desired to speed processing. No limit to the distribution or numbers of KStores, Learn Engines being used or K Engines being used is indicated.

Abstract: A tree-based datastore comprising a forest of interconnected trees that can be generated and/or accessed may require specialized saving and restoring processes to ensure that all the links are properly maintained whether it will be restored in full or in part. The processes act on known features of the trees based datastore to generate a file of metadata and packetize each of the nodes of the structure, carefully saving the links and lists of links with old memory addresses accounted for. When restoring the full or partial save to a new memory, a translation table is used to convert the addresses of the nodes and their links to appropriately matched locations in the new memory so that the structure of the data store can be reconstructed in the new location and ensuring that there is sufficient protected memory for the structure as the restore is accomplished.

Abstract: A tree-based datastore comprising a forest of interconnected trees that can be generated and/or accessed may require specialized saving and restoring processes to ensure that all the links are properly maintained whether it will be restored in full or in part. A previous process acted on known features of the trees based datastore to generate a file of metadata and packetize each of the nodes of the structure, carefully saving the links and lists of links with old memory addresses accounted for. This shows how to build a save file without saving all links, using a new restore process to restore the links instead.