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 for securing files through a persistent signature includes: selecting a file of any file type; selecting a private key, a certificate identifier and a hash algorithm identifier for the file; creating an encrypted hash of the file using the hash algorithm and private key for the file; and creating a persistent, independent signature for each selected file that identifies the file, certificate identifier, hash algorithm identifier, and the encrypted hash value for the file.

Abstract: A processor-based method for securing directories through a persistent signature, comprising: selecting a directory; selecting a private key, a certificate identifier and a hash algorithm identifier for each selected directory; creating an encrypted hash of the directory using the hash algorithm and private key; and creating a persistent, independent signature for the directory that identifies the directory, certificate identifier, hash algorithm identifier, and the encrypted hash value for that directory.

Abstract: A processor-based method for verifying a secured file, directory, or meta-data, comprising: extracting a persistent, independent signature for a secured file, directory, or meta-data from a directory signature file, the signature identifying a certificate identifier, a hash algorithm identifier, and an encrypted hash value for that secured file, directory, or meta-data; retrieving a public key corresponding to the certificate identifier; decrypting the encrypted hash using the public key and a decryption tool, resulting in a clear text hash value; creating a new hash value for the secured file, directory, or meta-data, the hash creation corresponding to the hash algorithm identifier; and verifying the signature when the new hash value for the secured file, directory, or meta-data matches the unencrypted hash value from the persistent, independent signature for the secured file, directory, or meta-data,

Abstract: A processor-based method for securing meta-data through a persistent signature, comprising: identifying meta-data for a file; selecting a private key, a certificate identifier and a hash algorithm identifier for the meta-data; creating an encrypted hash of the meta-data using the hash algorithm and private key; and creating a persistent, independent signature for the meta-data that identifies the meta-data, certificate identifier, hash algorithm identifier, and the encrypted hash value for the meta-data.

Abstract: A processor-based method for creating a persistent directory signature file, comprising: selecting a private key, a certificate identifier and a hash algorithm identifier for the directory signature file; concatenating one or more persistent, independent signatures wherein a persistent independent signature includes a file name, sub-directory indicator, or meta-data indicator, and a certificate identifier, and a hash identifier, and an encrypted hash; creating an encrypted hash of the one or more concatenated signatures using the hash algorithm and private key chosen for the directory signature file; and concatenating the encrypted hash of the one or more concatenated signatures with the concatenated signatures and the certificate identifier, and hash algorithm identifier chosen for the directory signature file to create a persistent, independent signature for the directory signature file.

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 creating a secondary index for a B-tree in a database includes creating an empty secondary index; traversing the B-tree to find the left most data page to begin processing; capturing the current system time; placing a read-lock on the data page; retrieving a first primary key value for the data page; writing the first primary key value to a control information for the B-tree; creating a secondary index entry and writing it into the secondary index; 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 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 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 method of decomposing a lecture into events includes: processing a recorded video and segmenting the video into video, chat, audio and audio transcript; processing the video and dividing it into discreet events including lecture, green board text, white board text, and interaction with users; and storing the discreet events as a storyboard that identifies break points in the lecture. The storyboard and events can be used to create a composed lecture.

Abstract: A method of presenting a virtual class in a presentation having a start time, normal speed, and end time includes determining if the presentation is being started after the start time and if so, determining a delay tine; determining if the presentation is to be presented at the normal speed, and if so, presenting the presentation at the normal speed and finishing the presentation at the end time plus the delay time; if the presentation is not to be presented at the normal speed, presenting the presentation at an accelerated speed such that the presentation ends at the end time.

Abstract: A method of creating action-trigger phrase sets includes receiving a document from a corpus of documents; processing text from the document; and creating an action-trigger phrase set from the text.

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 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 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.