Sparkbeyond Ltd.

Sagie Davidovich

Ron Karidi

Meir Maor

Amir Ronen

Denis Wainshtein

Avishay Livne

Yuval Peleg

Guy Shaked

A method, apparatus and product for automatic hypothesis generation using geospatial data. A labeled dataset and an auxiliary dataset are obtained. Instances comprise geospatial attributes. Hypothesis generation is performed automatically based on the labeled dataset. For each labeled instance, one or more auxiliary instances are selected from the auxiliary dataset based on a geospatial relation between the geospatial attribute of the labeled instance and the geospatial attribute of the auxiliary instance. Based on the selected auxiliary instances, one or more new attributes are computed and added to the labeled instance.

Automatic hypothesis generation using geospatial data

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.

Systems and methods for secondary knowledge utilization in machine learning

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.

Hypotheses generation using searchable unstructured data corpus

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.

Systems and methods for searching an unstructured dataset with a query

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.

System and method for automatic generation of features from datasets for use in an automated machine learning process

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.

Systems and methods for detection of anomalous entities

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.

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Zikhron-Yaakov, Israel

Sagie Davidovich