Abstract: There is provided a method of dynamic adaptation of a graphical user interface for exploring sensitive data, comprising: dynamically creating a hidden data presentation by applying permissions to a dataset, for hiding records, obtaining a selection of a target variable via the GUI presenting the hidden data presentation, feeding the dataset and the target variable into a hypothesis engine that extracts hypothesis features from the dataset, tests correlations between the hypothesis features and the target variable, and selects a set of insight features from the hypothesis features according to the correlations, dynamically creating a hidden result presentation by propagating the permission to the portions of the dataset used to compute the insight features, and presenting within the GUI the hidden result presentation that presents the insight features and hides the portions of the dataset used to compute the insight features according to the permissions.

Abstract: A classifier is computed as follows. For a first set of values of primary field(s) of primary data instances of a labeled primary training dataset, a second set(s) of secondary fields of unclassified second data instances of secondary dataset(s) is identified. First set of values are matched to corresponding values in respective secondary field(s), and linked to other secondary fields of respective secondary data instance(s) of the respective matched secondary field. A set of classification features is generated. Each including: (i) condition(s), and (ii) a value selected from the linked other secondary fields of the respective secondary data instance(s) of the respective matched secondary field(s). The respective classification feature outputs a binary value computed by the condition(s) that compares between the value selected from the other linked secondary fields and a new received data instance. A classifier is computed according to a selected subset of the classification features.

Abstract: Methods, products and apparatus are provided for hypotheses generation using searchable unstructured data corpus. In one method, a query is generated based on at least one attribute of at least one instance in a dataset. The query is provided to a search engine searching in an unstructured data corpus. An hypothesis for the database is based on a new attribute whose value is defined based on the one or more results. Another method comprises obtaining a set of keywords from a plurality of hypotheses extracted from a database. A query is generated based on an attribute of an instance in the dataset, where the attribute corresponds to an hypothesis. A search engine executes the query to provide results which are used to augment an instance with a new attribute, where a value of the new attribute is computed based on the one or more results.

Abstract: A classifier is computed as follows. For a first set of values of primary field(s) of primary data instances of a labeled primary training dataset, a second set(s) of secondary fields of unclassified second data instances of secondary dataset(s) is identified. First set of values are matched to corresponding values in respective secondary field(s), and linked to other secondary fields of respective secondary data instance(s) of the respective matched secondary field. A set of classification features is generated. Each including: (i) condition(s), and (ii) a value selected from the linked other secondary fields of the respective secondary data instance(s) of the respective matched secondary field(s). The respective classification feature outputs a binary value computed by the condition(s) that compares between the value selected from the other linked secondary fields and a new received data instance. A classifier is computed according to a selected subset of the classification features.

Abstract: There is provided a method of selecting subpopulations of users mapped to subpopulations of entities, comprising: receiving latent factors of a mapping between users and entities and a predicted correlation value for each undefined mapping, computed by a recommender process, for each respective latent factor: identifying, by a user semantic model, user features of the users correlated to the respective latent factor, identifying, by an entity semantic model, entity features of the entities correlated to the respective latent factor, generate combinations of pairs each including one user feature and one entity feature, for each pair, compute statistical metric(s) indicative of a change relative to the predicted correlation value for the users and the entities, select pair(s) according to a requirement of the statistical metric(s), and provide the user feature and the entity feature for each selected pair.

Abstract: A classifier is computed as follows. For a first set of values of primary field(s) of primary data instances of a labeled primary training dataset, a second set(s) of secondary fields of unclassified second data instances of secondary dataset(s) is identified. First set of values are matched to corresponding values in respective secondary field(s), and linked to other secondary fields of respective secondary data instance(s) of the respective matched secondary field. A set of classification features is generated. Each including: (i) condition(s), and (ii) a value selected from the linked other secondary fields of the respective secondary data instance(s) of the respective matched secondary field(s). The respective classification feature outputs a binary value computed by the condition(s) that compares between the value selected from the other linked secondary fields and a new received data instance. A classifier is computed according to a selected subset of the classification features.

Abstract: There is provided a method for searching an unstructured dataset with a query, comprising: receiving a query comprising a value for a first token of a triplet, and a value for a relation term defining a relationship between the first token and a second token of the triplet, wherein the second token is defined as a variable element set with an undefined value, creating a plurality of enhanced queries for the query, each one of the plurality of enhanced queries including variations of the relation term, providing the plurality of enhanced queries for search by a search engine on at least one dataset of unstructured text-based data, receiving a plurality of documents in response to the search, analyzing the plurality of documents for extracting at least one value for the variable element of the triplet, and providing the at least one value for the variable element.

Abstract: There is provided a method for generating features for use in an automated machine learning process, comprising: receiving a first training dataset comprising unclassified raw data instances each including a set of objects of arbitrary types; applying a function to each data instance to calculate a set of first results; generating a set of classification features each including the function for application to a newly received data instance to calculate a second result, and a condition defined by a respective member of the set of first results applied to the second result; applying each classification feature to each instance of an unclassified second training dataset to generate a set of extracted features; selecting a subset of pivotal classification features from the set of classification features according to a correlation requirement between classification variable(s) and each respective member of the set of extracted features; and documenting the subset of pivotal features.

Abstract: There is provided a computer-implemented method of identifying anomalous entities in a dataset, comprising: selecting a subset of training entities from entities of at least one dataset; determining dummy tuplets of entities in the subset by applying a permutation function on real tuplets, wherein the real tuplets represent original and normal data of the at least one dataset, wherein the dummy tuplets represent anomalous data based on artificially created data not found in the original and normal at least one dataset, each one of the real tuplets and dummy tuplets comprises at least two of the training entities; analyzing the dummy tuplets and the real tuplets to identify at least one predefined characteristic relation that statistically differentiates between the real tuplets and the dummy tuplets according to a distinguishing requirement; and outputting the identified at least one predefined characteristic relation to identify a normal entity and/or an anomalous entity.

Abstract: A method of identifying an element. The method comprises setting a training set comprising a plurality of data units, selecting a function group of building block functions adapted for processing said plurality of data units, combining members of said function group to create a stream of a plurality of combination functions each complied from at least two members of said function group, applying each member of said stream on each of said plurality of data units to create a set of results, analyzing said set of results to identify a correlation between at least one member of said stream and a target variable for an analysis of said plurality of data units, and outputting said at least one member or an indication thereof.