Abstract: A method includes assigning unique symbols to pages of a website, respectively. The method includes obtaining page symbol sequences of browsing sessions, respectively. Each browsing session corresponds to a visitor of the website. For each browsing session, the page symbol sequence of the browsing session is a sequence of symbols that corresponds, respectively, to a sequence of pages of the website visited during the browsing session by the corresponding visitor. The method includes generating a master string including the page symbol sequences, generating a suffix array corresponding to the master string, and generating a longest common prefix (LCP) array corresponding to the suffix array. The method includes, based on the suffix array and LCP array, determining one or more most common n-step subsequences of pages (n is an integer greater than 1).

Abstract: A system, apparatus, method, and non-transitory computer readable medium for performing co-trading changepoint detection may include a server caused to receive a first raw dataset, the first raw dataset including a plurality of transactions for analysis, each transaction of the plurality of transactions associated with a user account of a plurality of user accounts, generate at least one transaction time series based on the first raw dataset, determine changepoints in the first raw dataset by performing changepoint detection analysis on the generated at least one transaction time series, and generate at least one potential fraud alert based on the determined changepoints.

Abstract: A method includes assigning unique symbols to pages of a website, respectively. The method includes obtaining page symbol sequences of browsing sessions, respectively. Each browsing session corresponds to a visitor of the website. For each browsing session, the page symbol sequence of the browsing session is a sequence of symbols that corresponds, respectively, to a sequence of pages of the website visited during the browsing session by the corresponding visitor. The method includes generating a master string including the page symbol sequences, generating a suffix array corresponding to the master string, and generating a longest common prefix (LCP) array corresponding to the suffix array. The method includes, based on the suffix array and LCP array, determining one or more most common n-step subsequences of pages (n is an integer greater than 1).

Abstract: A method includes assigning unique symbols to pages of a website, respectively. The method includes obtaining page symbol sequences of browsing sessions, respectively. Each browsing session corresponds to a visitor of the website. For each browsing session, the page symbol sequence of the browsing session is a sequence of symbols that corresponds, respectively, to a sequence of pages of the website visited during the browsing session by the corresponding visitor. The method includes generating a master string including the page symbol sequences, generating a suffix array corresponding to the master string, and generating a longest common prefix (LCP) array corresponding to the suffix array. The method includes, based on the suffix array and LCP array, determining one or more most common n-step subsequences of pages (n is an integer greater than 1).

Abstract: A recommendation system includes a content similarity analyzer configured to determine a first set of content item identifiers similar to a set of viewed content items based on respective similarity scores and add them to a first list. A similar user content extraction module identifies a set of similar user identifiers from a user similarity database; obtains, based on respective viewing histories of the set, a second set of content item identifiers; and adds them to the first list. The recommendation system includes a content filter configured to select a subset of content item identifiers from the first list based on the corresponding similarity scores between content item identifiers of the first list and a viewing history. The content filter is configured to transmit the subset of content item identifiers for display of the corresponding content items via a web portal on a user interface of a first user device.

Abstract: A recommendation system includes a content similarity analyzer configured to determine a first set of content item identifiers similar to a set of viewed content items based on respective similarity scores and add them to a first list. A similar user content extraction module identifies a set of similar user identifiers from a user similarity database; obtains, based on respective viewing histories of the set, a second set of content item identifiers; and adds them to the first list. The recommendation system includes a content filter configured to select a subset of content item identifiers from the first list based on the corresponding similarity scores between content item identifiers of the first list and a viewing history. The content filter is configured to transmit the subset of content item identifiers for display of the corresponding content items via a web portal on a user interface of a first user device.

Abstract: A method includes assigning unique symbols respectively to potential interactions with a website. The method includes obtaining asserted interaction symbol sequences of multiple browsing sessions, respectively. Each browsing session corresponds to a visitor of the website. For each browsing session, the asserted interaction symbol sequence of the respective browsing session is a sequence of symbols, from among the unique symbols, that corresponds, respectively, to a sequence of asserted interactions with the web site visited during the respective browsing session by the corresponding visitor. The method includes generating a master string including the asserted interaction symbol sequences by concatenating the asserted interaction symbol sequences with sentinel symbols together such that at least one sentinel symbol exists between each consecutive pair of asserted interaction symbol sequences. The method includes generating a suffix array corresponding to the master string.

Abstract: A system includes a memory configured to store instructions and at least one processor configured to execute the instructions. The instructions include accessing time series data, calculating statistical parameters of the time series data, identifying a set of external processing resources, and conveying the time series data and the statistical parameters to the set of external processing resources. The instructions include instructing the set of external processing resources to compute a similarity self-join of the time series data for a window size having a specified length. The instructions include obtaining sets of minimum subsequence distances from the set of external processing resources over a communications network. The full similarity self-join indicates, for each reference subsequence of the specified length within the time series data, a minimum value of distances between the reference subsequence and other subsequences of the specified length within the time series data.

Abstract: A computer system includes memory configured to store a document database and a machine learning model. The document database includes multiple historical documents each having at least one version labeled as compliant and at least one version labeled as non-compliant. The system includes a creator user interface, a compliance user interface, an automated distribution module, and a model building module configured to train the machine learning model to classify a document according to a compliance score indicating a likelihood of document compliance with one or more compliance criteria. The system also includes an orchestrator module configured to receive the compliance score for the submitted document from the machine learning model, determine whether the compliance score is greater than or equal to a compliance score threshold, and supply the submitted document to the compliance user interface for transmission to the compliance team device when the compliance score is above a threshold.

Abstract: A method includes assigning unique symbols respectively to potential interactions with a website. The method includes obtaining asserted interaction symbol sequences of multiple browsing sessions, respectively. Each browsing session corresponds to a visitor of the website. For each browsing session, the asserted interaction symbol sequence of the respective browsing session is a sequence of symbols, from among the unique symbols, that corresponds, respectively, to a sequence of asserted interactions with the web site visited during the respective browsing session by the corresponding visitor. The method includes generating a master string including the asserted interaction symbol sequences by concatenating the asserted interaction symbol sequences with sentinel symbols together such that at least one sentinel symbol exists between each consecutive pair of asserted interaction symbol sequences. The method includes generating a suffix array corresponding to the master string.

Abstract: A method includes assigning unique symbols to pages of a website, respectively. The method includes obtaining page symbol sequences of browsing sessions, respectively. Each browsing session corresponds to a visitor of the website. For each browsing session, the page symbol sequence of the browsing session is a sequence of symbols that corresponds, respectively, to a sequence of pages of the website visited during the browsing session by the corresponding visitor. The method includes generating a master string including the page symbol sequences, generating a suffix array corresponding to the master string, and generating a longest common prefix (LCP) array corresponding to the suffix array. The method includes, based on the suffix array and LCP array, determining one or more most common n-step subsequences of pages (n is an integer greater than 1).

Abstract: A recommendation system includes an input module, an input process in module, a selection module, a recommendation module, a trade module, a trade selection module, and a user return module. The system receives user input form a user interface and determines a requested option and security. A recommendation module compares the requested security to a recommendation database of all user and all securities of the enterprise purchased by all the users of the enterprise using a pair of smaller databases derived from the database. The recommended securities are returned to a trade module that receives the recommended securities and a list of option trades available based on the recommended securities and the option trades. The user interface presents the option trades to the user for selection and execution.

Abstract: A recommendation system includes a content similarity analyzer configured to determine a first set of content item identifiers similar to a set of viewed content items based on respective similarity scores and add them to a first list. A similar user content extraction module identifies a set of similar user identifiers from a user similarity database; obtains, based on respective viewing histories of the set, a second set of content item identifiers; and adds them to the first list. The recommendation system includes a content filter configured to select a subset of content item identifiers from the first list based on the corresponding similarity scores between content item identifiers of the first list and a viewing history. The content filter is configured to transmit the subset of content item identifiers for display of the corresponding content items via a web portal on a user interface of a first user device.

Abstract: A system includes a pattern engine that, in response to selection by a user of a first data set, generates a similarity self-join of the first data set for a specified length. The similarity self-join indicates, for each reference subsequence, a minimum value of distances between the reference subsequence and other subsequences within the first data set. A user portal generates a user interface visually representing the first data set and identifying two subsequences that correspond to the lowest value of the similarity self-join. An alert system receives an alert request specifying a second data set and a pattern specification. An incremental pattern engine generates an initial state of a similarity join of the second data set and the pattern specification. In response to data being received for the first data set, the alert system transmits an alert message if any value of the similarity join meets a threshold.

Abstract: A content recommendation system includes a processor and a memory coupled to the processor. The memory stores instructions that, upon execution, cause the processor to obtain a first viewing history of a first user from the viewing history index. The instructions include determining, based on the user similarity index, a first set of users similar to the first user. The instructions include obtaining a corresponding viewing history from the viewing history index and selecting a set of similar content item identifiers based on similarity scores. The instructions include updating a first recommendation list with (i) the corresponding viewing history for each similar user in the first set of users and (ii) the set of similar content item identifiers. The instructions include selecting and transmitting to a user device a subset of recommended content item identifiers from the first recommendation list.

Abstract: A content recommendation system includes a processor and a memory coupled to the processor. The memory stores instructions that, upon execution, causes the processor to obtain a first viewing history of a first user from the viewing history index. The instructions include determining, based on the user similarity index, a first set of users similar to the first user. The instructions include obtaining a corresponding viewing history from the viewing history index and selecting a set of similar content item identifiers from the plurality of content item identifiers based on similarity scores. The instructions include updating a first recommendation list with (i) the corresponding viewing history for each similar user in the first set of users and (ii) the set of similar content item identifiers. The instructions include selecting and transmitting to a user device a subset of recommended content item identifiers from the first recommendation list.

Abstract: A system includes a pattern engine that, in response to selection by a user of a first data set, generates a similarity self-join of the first data set for a specified length. The similarity self-join indicates, for each reference subsequence, a minimum value of distances between the reference subsequence and other subsequences within the first data set. A user portal generates a user interface visually representing the first data set and identifying two subsequences that correspond to the lowest value of the similarity self-join. An alert system receives an alert request specifying a second data set and a pattern specification. An incremental pattern engine generates an initial state of a similarity join of the second data set and the pattern specification. In response to data being received for the first data set, the alert system transmits an alert message if any value of the similarity join meets a threshold.

Abstract: A system includes a memory configured to store instructions and at least one processor configured to execute the instructions. The instructions include accessing time series data, calculating statistical parameters of the time series data, identifying a set of external processing resources, and conveying the time series data and the statistical parameters to the set of external processing resources. The instructions include instructing the set of external processing resources to compute a similarity self-join of the time series data for a window size having a specified length. The instructions include obtaining sets of minimum subsequence distances from the set of external processing resources over a communications network. The full similarity self-join indicates, for each reference subsequence of the specified length within the time series data, a minimum value of distances between the reference subsequence and other subsequences of the specified length within the time series data.