Abstract: In a KStore having K nodes with respective K node counts a method for updating the K node counts includes processing a K node in accordance with a received particle to provide a processed K node and updating a node count in response to the processing. The processing of the processed K node includes building the processed K node in response to the received particle. A node count of a beginning of sequence node is updated. A beginning of sequence node includes a beginning of thought node. The received particle can be a particle of sensor data having a corresponding sensor node to provide a sensor node determination. A node count of the corresponding sensor node is updated in response to the sensor node determination. A subcomponent node is determined in response to the sensor determination and a K node count of the subcomponent node is updated.

Abstract: The process of traversing a K may involve determining a match between a root node and a Result node of a node on the asCase list of a current K node. When learning is off and a match is not found, the procedure may ignore the particle being processed. An alternative solution determines which node on the asCase list is the most likely to be the next node. While the K Engine is traversing and events are being recorded into a K structure, a count field may be added to each K node to contain a record of how many times each K path has been traversed. The count field may be updated according to the processes traversing the K. Typically, the count is incremented only for learning functions. This count field may be used in determining which node may be the most (or least) probable.

Abstract: In a KStore having a plurality of K nodes with count fields a method for updating count fields, receiving a particle to provide a received particle, updating selected node counts of the plurality of nodes counts in response to the received particle to provide first updated K node count fields, and saving selected K node count fields for later updating to provide second updated count fields are recited. The K nodes include elemental root nodes and the second updated K node count fields include elemental root nodes of the plurality of elemental root nodes. The second updated K node count fields include only elemental root nodes of the plurality of elemental root nodes. The first updated K node count fields include no elemental root nodes. The second updated K node count fields include K nodes pointed to by the Result pointers of the first updated K node count fields.

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: A tree-based datastore comprising a forest of interconnected trees is generated and/or accessed. The tree-based datastore comprises a first tree that depends from a first root node and may include a plurality of branches. Each of the branches of the first tree ends in a leaf node. Each leaf node may represent an end product, as described more fully below. A second root of the same tree-based datastore is linked to each leaf node representing an end product. Hence, the second root is essentially a root to an inverted order of the first tree, but the first tree is not duplicated. Finally, the tree-based datastore comprises a plurality of trees in which the root node of each of these trees is an elemental node, as described more fully below. The root node of each of these trees may be linked to one or more nodes in one or more branches of the first tree. The nodes of the tree-based datastore contain only pointers to other nodes in the tree-based datastore.

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 method for processing a particle stream in a KStore having a sensor level and a first level above the sensor level, including receiving a particle to provide a received particle and first determining a current K node to determine a partial sequence location. The method also includes second determining a match in accordance with the received particle and the current K node to provide a match determination and processing the current K node based upon the match determination. A new node is created if the second determining is negative. A current K node is set to the new node. A node count is incremented and a delimiter particle type of the received particle is determined. A match is determined between the delimiter and a Result node of an asCase node of the current K node to provide a delimiter match determination.

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 optimizing the construction of a KStore includes providing a data set having a plurality of records with a plurality of fields, providing an optimization structure from a sample of the data set and analyzing the optimization structure to determine a field order for the records contained in the data set. A KStore is provided from the data set in accordance with the field order. The sample of the data set can include the entire data set, a random sample of the data set or a predetermined number of records from the data set. The data set has an initial order having earlier records and later records and the sample can include a plurality of the earlier records. The sample of the data set includes a user determined number of records from the data set. The optimization structure can be a 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: A method for processing KStore sensors for use by a KEngine in a KStore system to process a K includes providing a stream of particles, instantiating a KStore sensor structure, identifying particles to be included in a sensor set and processing said identified particles. The KStore sensor structure can provide a correspondence between a particle and a sensor K node. Furthermore, the KStore sensor structure can be a list of K nodes, an indexed array or a hash table.

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 tree-based datastore comprising a forest of interconnected trees is generated and/or accessed. The tree-based datastore comprises a first tree that depends from a first root node and may include a plurality of branches. Each of the branches of the first tree ends in a leaf node. Each leaf node may represent an end product, as described more fully below. A second root of the same tree-based datastore is linked to each leaf node representing an end product. Hence, the second root is essentially a root to an inverted order of the first tree, but the first tree is not duplicated. Finally, the tree-based datastore comprises a plurality of trees in which the root node of each of these trees is an elemental node, as described more fully below. The root node of each of these trees may be linked to one or more nodes in one or more branches of the first tree. The nodes of the tree-based datastore contain only pointers to other nodes in the tree-based datastore.

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: Aggregation of data in an interlocking trees datastore, especially when the interlocking datastore is a KStore is described. It details consolidating data into a summary or aggregation so that some particular desired analytic type of operation may easily be performed on the data. It uses a set of data constraints across the entire data set. This redefines the data set, which may be for example, individual receipts granular by week or month. When data is learned into a KStore, aggregation parameters may be collected and these parameters may be used to constrain the dataset recorded in K, and direct performance of an analytic on a particular a field value(s). Additional features and details are provided within.

Abstract: A method for providing a display of data from an interlocking trees datastore in a graphical display system having a graphic display device is disclosed, the method including the steps of collecting display requirements for defining a value and at least one display characteristic to be displayed on the graphic display device and querying an interlocking trees datastore in accordance with the display requirements to determine the value to be displayed. At least one graphical display parameter is determined in accordance with the display requirements and the value to be displayed and the graphical display parameter are transmitted to the graphical display device for display of the value by the graphic display device.

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.