Abstract: System determines first and second scores based on applying function to features of first and second values in fields in first and second records, respectively. System determines first priority based on first score and second priority based on second score for displaying first and second values in fields in first profile. System revises, based on feedback associated with first value and second value, parameter associated with function and determines third score based on applying function, associated with revised parameter, to feature of third value in field in third record. System determines fourth score based on applying function, associated with revised parameter, to feature of fourth value in field in fourth record and determines third priority, based on third score, for displaying third value in field in second profile and fourth priority, based on fourth score, for displaying fourth value in field in second profile.

Abstract: A system determines factored score by multiplying factor and match score for values of field in two records, offset score by adding offset to factored score, and weighted score by applying weight to offset score. The system determines status for two records based on combining weighted score with other weighted score corresponding to other field of two records. The system revises factor, offset, and weight based on feedback associated with two records. The system determines revised factored score by multiplying revised factor and match score for other values of field in two other records, revised offset score by adding revised offset to revised factored score, and revised weighted score by applying revised weight to revised offset score. The system determines learned status for two other records based on combining revised weighted score with additional weighted score corresponding to other field for two other records.

Abstract: A system creates graph of nodes connected by edges. Each node represents corresponding value of corresponding attribute and is associated with count of corresponding value. Each edge is associated with count of instances that values represented by corresponding connected nodes are associated with each other. The system identifies each node associated with first count as first set of keys, and deletes each node associated with first count. The system identifies each edge associated with second count as second set of keys, and deletes each edge associated with second count. The system identifies each node associated with third count as third set of keys, and deletes each node associated with third count. The system identifies each edge associated with fourth count as fourth set of keys, and deletes each edge associated with fourth count. The system uses each set of keys to search and match records.

Abstract: New account routing to user account sets is described. A system creates multiple accounts profiles corresponding to multiple sets of accounts, based on multiple attributes associated with each account of the multiple sets of accounts. The system calculates multiple account scores for an account based on comparing multiple attributes associated with the account against the corresponding multiple accounts profiles, wherein the account is not in the multiple sets of accounts. The system identifies a highest account score of the multiple account scores. The system routes the account to a user associated with a set of accounts corresponding to the highest account score.

Abstract: Computing systems, database systems, and related methods are provided for guiding a user defining a quote for a product. One method involves a server obtaining one or more values for one or more attributes of a quote from a client device coupled to the server over a network, obtaining an expected pricing model for the quote from a database, determining expected pricing information for the quote based on the one or more values for the one or more attributes using the model, and providing a graphical indication of the expected pricing information on the client device. The expected pricing model is determined based on historical relationships between quote attributes and price for previously-closed quotes.

Abstract: The system tokenizes values stored by records' fields, creates trie from tokenized values, each branch labeled with tokenized value, each node storing count indicating number of records associated with tokenized value sequence beginning from trie root. The system tokenizes value stored by record field, identifies nodes, beginning from trie root, corresponding to token value sequence associated with tokenized value, until node is identified that stores count that is less than node threshold. The system identifies branch sequence comprising each identified node as record's key, and associates key with node storing count less than node threshold, and record with key. The system tokenizes prospective value stored by prospective record's field, identifies nodes, beginning from trie root, corresponding to another token value sequence associated with tokenized prospective value, until another node is identified that stores another count that is less than node threshold.

Abstract: Associating job responsibilities with job titles is described. A database system identifies a job level and a job department associated with a job title stored by an object. The database system identifies level-based job responsibilities associated with the job level. The database system identifies department-based job responsibilities associated with the job department. The database system identifies at least one job responsibility associated with the job title based on the level-based job responsibilities and the department-based job responsibilities. The database system stores each identified job responsibility in metadata and/or a field associated with the object. The database system outputs a message based on the object to a user device, in response to a search that specifies any identified job responsibility.

Abstract: A personalized recommendation model scores each object in an interaction set of objects with which a user interacted and in a ransom set of objects with which the user lacks known interaction. A system sorts each scored object based on a decreasing order of each corresponding score, and identifies a high scoring set of the sorted objects with a number (equal to the number of objects in the interaction set of objects) of highest corresponding scores. The system aggregates a corresponding order value for each object in the high scoring set that is also in the interaction set of objects (the corresponding order value for an object is based on a corresponding order for the object in the high scoring set). The system evaluates the model for the user by dividing the aggregated order value by an aggregation of a corresponding order value for each object in the high scoring set.

Abstract: System receives inputs, each input associated with a label and having features, creates a rule for each feature, each rule including a feature and a label, each rule stored in a hierarchy, and distributes each rule into a partition associated with a label or another partition associated with another label. System identifies a number of inputs that include a feature for a rule in the rule partition, and identifies another number of inputs that include both the feature for the rule and another feature for another rule in the rule partition. System deletes the rule from the hierarchy if the ratio of the other number of inputs to the number of inputs satisfies a threshold and an additional number of inputs that includes the other antecedent feature is at least as much as the number. System predicts a label for an input including features by applying each remaining rule to the input.

Abstract: A system determines a first volume of out-calls of a first out-call type made by a software container that is executing an application during a time period. The system determines a second volume of out-calls of a second out-call type made by the software container. The system determines a first ratio of the first volume to a combined volume of out-calls of all out-call types made by the software container. The system determines a second ratio of the second volume to the combined volume of out-calls of all out-call types made by the software container. The system determines a measure by comparing the first ratio to a third ratio associated with the first out-call type, and by comparing the second ratio to a fourth ratio associated with the second out-call type. The system identifies any behavior or any application type associated with the application, based on the measure.

Abstract: An attempt by a user to login to a destination server is identified from a source server. A destination score is determined based on the count of attempts by the user to login to the destination server and the count of attempts by the user to login to all destination servers. A source given destination score is determined based on the count of attempts by the user to login from the source server to the destination server, and the count of attempts by the user to login to the destination server. An outlier score is determined based on values associated with the destination score and the source given destination score. An alert is output if the outlier score satisfies a threshold.

Abstract: Client-server hybrid A.I. scores for customized actions are described. A client generates client scores corresponding to client customized actions by applying a user-specific model to an action received from a user, the user-specific model based on at least one historical action received from the user. The client requests a server to provide server scores corresponding to server customized actions by applying a cross-user model to the action received from the user, the cross-user model based on historical actions associated with server users. The client generates hybrid scores corresponding to hybrid customized actions by combining the client scores with the server scores, in response to receiving the server scores from the server. The client causes the hybrid customized actions to be outputted based on the corresponding hybrid scores.

Abstract: Search query result set count estimation is described. A system parses data set query that includes first query attribute and second query attribute. The system identifies first hierarchy of connected nodes including a first node representing a first query attribute, and a second hierarchy of other connected nodes including a second node representing a second query attribute. The system identifies a directed arc connecting first correlated node in first hierarchy to second correlated node in second hierarchy. The system identifies cross-hierarchy probabilities of correlations between values of a first attribute represented by the first correlated node and values of a second attribute represented by the second correlated node.

Abstract: A training dataset having training instances is determined. Each training instance comprises first and second records and a second record and a label indicate whether there is a match between the first and second records. A matching score vector is determined for each such training instance, and comprises components storing match scores for extracted features from field values in the first and second records. Based on matching score vectors and a match objective function, match score thresholds are determined for the extracted features. Match rule(s) each of which comprises predicate(s) are generated. Each predicate makes a predication on whether two records match by comparing a match score derived from the two records against a match score threshold.

Abstract: A system tokenizes values stored in a field by multiple records. The system creates a trie from the tokenized values, each branch in the trie labeled with one of the tokenized values, each node storing a count indicating the number of the multiple records associated with a tokenized value sequence beginning from a root of the trie. The system tokenizes a value stored in the field by a prospective record. Beginning from the root of the trie, the system identifies each node corresponding to a token value sequence for the prospective record's tokenized value. Beginning from the most recently identified node for the prospective record's token value sequence, the system identifies each extending node which stores a count that satisfies a threshold, each identified extending node corresponding to another token value sequence. The system uses the other token value sequence to identify one of the multiple records that matches the prospective record.

Abstract: A system determines a first volume of out-calls of a first out-call type made by a software container that is executing an application during a time period. The system determines a second volume of out-calls of a second out-call type made by the software container. The system determines a first ratio of the first volume to a combined volume of out-calls of all out-call types made by the software container. The system determines a second ratio of the second volume to the combined volume of out-calls of all out-call types made by the software container. The system determines a measure by comparing the first ratio to a third ratio associated with the first out-call type, and by comparing the second ratio to a fourth ratio associated with the second out-call type. The system identifies any behavior or any application type associated with the application, based on the measure.

Abstract: Recommending data providers' datasets based on database value densities is described. A database system determines a provider dataset density for a value by identifying a frequency of the value in a dataset that is provided by a data provider. The database system determines a user database density for the value by identifying a frequency of the value in a database used by a data user. The database system determines a relative density based on a relationship between the provider dataset density and the user database density. The database system determines an evaluation metric for the value, based on a combination of the relative density and the user database density. The database system causes a recommendation to be outputted, based on a relationship of the evaluation metric relative to other evaluation metrics for other values, which recommends that the data user acquire at least a part of the dataset.

Abstract: Some embodiments of the present invention include a system and method for removing duplicate records from a group of records in a database system. The method includes generating a first cluster of records from the group of records, generating a second cluster of records from the group of records, identifying sets of duplicate records in the first cluster of records, and identifying sets of duplicate records in the second cluster of records. The method also includes merging at least two sets of duplicate records associated with both the first cluster and the second cluster of records to form a merged set of duplicate records. The merging is performed based on the at least two sets of duplicate records having a common record. Duplicate records in the group of records may then be removed by removing duplicate records from the merged set of duplicate records.

Abstract: An attempt by a user to login to a destination server is identified from a source server. A destination score is determined based on the count of attempts by the user to login to the destination server and the count of attempts by the user to login to all destination servers. A source given destination score is determined based on the count of attempts by the user to login from the source server to the destination server, and the count of attempts by the user to login to the destination server. An outlier score is determined based on values associated with the destination score and the source given destination score. An alert is output if the outlier score satisfies a threshold.

Abstract: Client-server hybrid A.I. scores for customized actions are described. A client generates client scores corresponding to client customized actions by applying a user-specific model to an action received from a user, the user-specific model based on at least one historical action received from the user. The client requests a server to provide server scores corresponding to server customized actions by applying a cross-user model to the action received from the user, the cross-user model based on historical actions associated with server users. The client generates hybrid scores corresponding to hybrid customized actions by combining the client scores with the server scores, in response to receiving the server scores from the server. The client causes the hybrid customized actions to be outputted based on the corresponding hybrid scores.