Abstract: A processor-based method of building and appending data structures in a multi-host environment includes creating and storing data into a first data structure having a first root node, a first right leaf node and a plurality of other leaf nodes and non-leaf nodes defining multiple hierarchical levels; allowing a search of the first data structure to proceed while creating a second data structure that includes a second root node, a second left leaf node and a plurality of leaf nodes and non-leaf nodes defining multiple hierarchical levels; receiving a command to append the second data structure to the first data structure during searching the first data structure; creating a global lock for all hosts in the multi-host environment to the first root node and the first right leaf node of the first data structure while allowing access to the plurality of other leaf nodes and non-leaf nodes of the first data structure; updating the first root node and the first right leaf node with information about the second data

Abstract: A processor-based method of building and appending data structures in a multi-host environment includes creating and storing data into a first data structure having a first root node, a first right leaf node and a plurality of other leaf nodes and non-leaf nodes defining multiple hierarchical levels; allowing a search of the first data structure to proceed while creating a second data structure that includes a second root node, a second left leaf node and a plurality of leaf nodes and non-leaf nodes defining multiple hierarchical levels; receiving a command to append the second data structure to the first data structure during searching the first data structure; creating a global lock for all hosts in the multi-host environment to the first root node and the first right leaf node of the first data structure while allowing access to the plurality of other leaf nodes and non-leaf nodes of the first data structure; updating the first root node and the first right leaf node with information about the second data

Abstract: A processor-based method of making a file containing a secondary index recoverable during processing of the secondary index includes: capturing a current system time as a captured system time and writing the captured system time to control information; finding writes to the file containing the secondary index; also writing the writes to an audit trail; using the captured system time in control information for the secondary index as a start time for recovery; determining if there is a primary key value in the control information; using a primary key value in the control information to traverse to a data page continuing the primary key value; placing a read-lock on the data page; creating a corresponding secondary index entry; and releasing the read-lock.

Abstract: A processor-based method of updating a secondary index for a B-tree in a database in a DBMS to include subsequent changes that occurred during creation of the secondary index includes opening an audit trail and moving back in the audit trail to a captured system time; making the secondary index visible to the DBMS for INSERT, UPDATE and DELETE processing; reading a next committed set of database updates from the audit trail; creating a secondary index modification for each row on each audit trail update and applying it to the secondary index; clearing the captured system time from control information for the B-tree to indicate completion; and making the secondary index visible to the DBMS for all processing.

Abstract: A processor-based method of creating a secondary index includes creating an empty secondary index; capturing the current system time; cloning the database to create a cloned database; writing to a file a secondary index column value and a primary key column value from rows in the cloned database that meet search criteria; populate the secondary index using sorted data; writing, periodically, a row number being processed from the file to control information for the B-tree; and clearing the row number.

Abstract: A processor-based method of grafting a scion data structure having a scion root page and scion left page to an understock data structure having an understock root page and an understock right page in a multi-host environment includes receiving a command to graft the scion data structure onto the understock data structure; preventing prefetching data belonging to the scion data structure into cache memory; locking the understock root page and the understock right page; grafting the scion data structure onto the right side of the understock data structure and not in the middle of the understock data structure; updating the understock root page to point to the scion root page and the understock right page to point to the scion left page; committing the update of the understock root page and the understock right page; releasing the lock to the understock root page and the understock right page; and sending updated copies of the understock root page and the understock right page to other hosts in a multi-host envi

Abstract: A processor-based method of building and appending data structures in a multi-host environment includes creating and storing data into a first data structure having a first root node, a first right leaf node and a plurality of other leaf nodes and non-leaf nodes defining multiple hierarchical levels; allowing a search of the first data structure to proceed while creating a second data structure that includes a second root node, a second left leaf node and a plurality of leaf nodes and non-leaf nodes defining multiple hierarchical levels; receiving a command to append the second data structure to the first data structure during searching the first data structure; creating a global lock for all hosts in the multi-host environment to the first root node and the first right leaf node of the first data structure while allowing access to the plurality of other leaf nodes and non-leaf nodes of the first data structure; updating the first root node and the first right leaf node with information about the second data

Abstract: A method of creating an instruction database includes searching various information sources for instruction information related to a user task; searching various information sources for safety information related to the user task; extracting the instruction information and safety information and saving the instruction information and safety information in the instruction database; and receiving user comments and saving the comments in the instruction database. A system for creating an instruction database includes a computer device for searching various information sources for instruction information related to a user task, searching various information sources for safety information related to the user task, extracting the instruction information and safety information and saving the instruction information and safety information in the instruction database and receiving user comments and saving the comments in the instruction database.

Abstract: A method of improving a visual instruction during running of a visual instruction sequence includes playing a visual instruction sequence to a user from a point-of-view of the user; monitoring the user for user data related to the visual instruction sequence; using the user data to improve the visual instruction sequence; generating an improved visual instruction sequence; and playing the improved visual instruction sequence to the user from a point-of-view of the user. A system for improving a visual instruction during running of a visual instruction sequence includes a computer device for playing a visual instruction sequence to a user from a point-of-view of the user, monitoring the user for user data related to the visual instruction sequence, using the user data to improve the visual instruction sequence, generating an improved visual instruction sequence; and playing the improved visual instruction sequence to the user from a point-of-view of the user.

Abstract: A computer it implemented method of training a user to perform a task includes receiving task data from a user device; identifying a task associated with the task data; querying a knowledgebase for data associated with the task; generating an AR pattern for training the user to perform the task; and transmitting the AR pattern to the user device. An augmented reality training system includes a computer device connected to a user device having a video camera. The computer device receives the video data from the video camera and identifies a task from the digital video data. A knowledgebase is connected to the computer device. The knowledgebase contains resources related to the task. The system identifies a task to be performed, queries the knowledgebase for resources and creates an augmented reality pattern with an avatar from the resources for training a user.

Abstract: A method for creating a learning model that evaluates risks of applying commits in a third-party product to a dependent product is disclosed. The method includes collecting data on past commits; training the learning model using the collected data; and using the learning model to determine if future commits are problematic.

Abstract: A method of determining if a commit is problematic includes determining a complexity of the commit; determining an author of the commit; determining an experience of the author; determining a component affected by the commit; and assigning a risk value to the commit based on the complexity, the author, the experience of the author and the component affected.

Abstract: A method for alerting a community to potential problematic commits includes receiving a commit submitted by an author in a project; testing the commit using a pre-trained learning model; and determining if the commit is problematic, and if the commit is problematic, sending a report in the project outlining the level of risk of implementing the commit and a reason for the level or risk. Users of the project will be alerted to potential risk of particular commits.

Abstract: A method for testing commits from a third-party product into a dependent product includes receiving a first commit from a third-party product; waiting for additional commits from the third-party product; receiving a second commit from the third-party product; testing the first and second commit using a pre-trained learning model; determining if the first commit is problematic, and if the first commit is problematic, sending the first commit for review before implementation; and determining if the second commit is problematic, and if the second commit is problematic, sending the second commit for review before implementation. Accumulating the first and second commits for testing at once reduces system resources.

Abstract: A method for creating a learning model that evaluates risks of applying commits in a third-party product to a dependent product is disclosed. The method includes collecting data on past commits; training the learning model using the collected data; and using the learning model to determine if future commits are problematic.

Abstract: A computer implemented method of training a user to perform a task includes receiving task data from a user device; identifying a task associated with the task data; querying a knowledgebase for data associated with the task; generating an AR pattern for training the user to perform the task; and transmitting the AR pattern to the user device. An augmented reality training system includes a computer device connected to a user device having a video camera. The computer device receives the video data from the video camera and identifies a task from the digital video data. A knowledgebase is connected to the computer device. The knowledgebase contains resources related to the task. The system identifies a task to be performed, queries the knowledgebase for resources and creates an augmented reality pattern with an avatar from the resources for training a user.

Abstract: An artificially intelligent method includes the steps of monitoring, by a processor, information sources to identify primary semantic information; capturing, by the processor, the primary semantic information; reformatting, by the processor, the primary semantic information according to a predetermined format; analyzing, by the processor, the primary semantic information to establish secondary semantic information; and establishing, by the processor, ontologies from the primary, secondary, and additional secondary semantic information.

Abstract: An artificially intelligent method includes the steps of monitoring, by a processor, information sources to identify primary semantic information; capturing, by the processor, the primary semantic information; reformatting, by the processor, the primary semantic information according to a predetermined format; analyzing, by the processor, the primary semantic information to establish secondary semantic information; and establishing, by the processor, ontologies from the primary, secondary, and additional secondary semantic information.

Abstract: Systems and methods for evaluating analytic queries comprising disjunctive Boolean expressions are described. A method may include receiving an analytic query comprising a first disjunctive Boolean expression. The method may further include transforming the analytic query to obtain a transformed analytic query comprising at least one nondisjunctive Boolean expression and at least a second disjunctive Boolean expression. The method may also include evaluating the transformed analytic query, wherein complete evaluation of the at least one nondisjunctive Boolean expressions and the at least a second disjunctive Boolean expressions yields the same results as evaluation of the first disjunctive Boolean expression.

Abstract: Systems and methods for processing tables for query operations referencing the tables are described. A method may include determining whether a table is referenced one time or more than one time in a query that includes at least one operation referencing the table. The method may further include creating a single materialized view of the table when the table is determined to be referenced more than one time in the query. The method may also include creating two or more hash tables based, at least in part, on the single materialized view of the table by creating a hash table for each operator in the query that references the table, and evaluating the query using the two or more hash tables.