Abstract: Some embodiments of the present invention include a method for determining a dense subset from a group of records using a graphical representation of the group of records, the graphical representation having nodes and edges, a node associated with a record from the group of records, an edge connecting two nodes associated with two related records, wherein a node is associated with a weight corresponding to a number of edges connected to the node, wherein a record is added to the dense subset based on its associated node having a highest weight and a density that satisfies a density threshold, the density being based on the content of the dense subset, and wherein the content of the dense subset is to be processed as including duplicate records.

Abstract: Some embodiments of the present invention include a method for determining duplicate records in multiple objects and may include combining records associated with a first object with records associated with a second object to generate a third object, wherein the first object is related to the second object; performing de-duplication on the third object to generate a combined group of duplicate sets; and from the combined group of duplicate sets, identifying at least one duplicate set associated with both the first object and the second object based on the duplicate set having at least one record associated with the first object and at least one record associated with the second object.

Abstract: A training set is created via creating adjacent classified substrings by using character classes to replace corresponding characters in adjacent substrings in each training character string, and associating each pair of adjacent classified substrings and each pair of adjacent substrings with corresponding labels indicating whether corresponding pairs include any token boundary. The system splits input character string into beginning and ending parts and creates classified beginning part by replacing beginning part character with corresponding class and classified ending part by replacing ending part character with corresponding class. The machine-learning model determines probability of token identification, based on training set to determine count of instances that classified beginning part is paired with classified ending part and count of corresponding labels that indicate inclusion of any token boundary.

Abstract: A system tokenizes raw values and corresponding standardized values into raw token sequences and corresponding standardized token sequences. A machine-learning model learns standardization from token insertions and token substitutions that modify the raw token sequences to match the corresponding standardized token sequences. The system tokenizes an input value into an input token sequence. The machine-learning model determines a probability of inserting an insertion token after an insertion markable token in the input token sequence. If the probability of inserting the insertion token satisfies a threshold, the system inserts the insertion token after the insertion markable token in the input token sequence. The machine-learning model determines a probability of substituting a substitution token for a substitutable token in the input token sequence.

Abstract: New account recommendations for user account sets are described. A system creates an accounts profile for a set of accounts based on multiple attributes associated with each account of the set of accounts. The system calculates an account score for an account based on comparing multiple attributes associated with the account against the accounts profile, wherein the account is not in the set of accounts. The system determines whether the account score satisfies an account score threshold. The system recommends the account to a user associated with the set of accounts if the account score satisfies the account score threshold.

Abstract: Some embodiments of the present invention include a method for identifying duplicate records from a group of records in a database system.

Abstract: A method and system of matching field values of a field type are described. Blurring operations are applied on a first and second values to obtain blurred values. A first maximum score is determined from first scores for blurred values, where each one of the first scores is indicative of a confidence that a match of the first and the second values occurs with knowledge of a first blurred value. A second maximum score is determined from second scores for the blurred values, where each one of the second scores is indicative of a confidence that a non-match of the first and the second values occurs with knowledge of the first blurred value. Responsive to determining that the first maximum score is greater than the second maximum score, an indication that the first value matches the second value is output.

Abstract: A method and system for estimating a number of distinct entities in a set of records are described. For each one of a subset of records, a set of match rule keys are generated based on a set of match rules. Each match rule from the set of match rules defines a match between records, and each match rule key from the set of match rule keys includes at least a key field value. A high order key for the record is determined based on the match rule keys, and a counter associated with the high order key is incremented. When each record from the subset of records has been processed by determining the match rule keys, and incrementing the counter(s) of the high order keys, a sum of a number of counters that have a non-zero value is performed to estimate the distinct entities in the records.

Abstract: A system determines a name probability based on a first name dataset frequency of a first name value stored by a first name field in a personal record and a last name dataset frequency of a last name value stored by a last name field in a personal record. The system determines at least one other probability based on another dataset frequency of another value stored by another field in the personal record and an additional dataset frequency of an additional value stored by an additional field in the personal record. The system determines a combined probability based on the name probability and the at least one other probability. The system increments a count of identifiable personal records for each personal record that has a corresponding combined probability that satisfies an identifiability threshold. The system outputs a message based on the count of identifiable personal records.

Abstract: A system identifies a first number of distinct values stored in a first field by a dataset of records. The system identifies a second number of distinct values stored in a second field by the dataset of records. The system creates a trie from values stored in a field by multiple records, the field corresponding to the first field or the second field, based on comparing the first number to the second number. The system associates a node in the trie with one of the multiple records, based on a value stored in the field by the record. The system identifies a branch sequence in the trie as a key for a prospective record, based on a prospective value stored in a corresponding field by the prospective record. The system uses the key for the prospective record to identify one of the multiple records that matches the prospective record.

Abstract: System creates three tries based on values stored in first three fields by records. System associates node in third trie with record, based on value stored in third field by record. System associates node with first dispersion measure, based on values stored in first field by records associated with node, and with second dispersion measure, based on values stored in second field by records associated with node. System identifies branch sequence in third trie as key for prospective record, based on value stored in third field by prospective record. System uses key to identify a subset of records that match prospective record. If a count of the subset exceeds threshold, the system identifies other branch sequence in first trie or second trie as other key for prospective record, based on first dispersion measure and second dispersion measure. System uses the key and the other key to identify at least one record that matches prospective record.

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: 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: 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 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.