Abstract: One or more embodiments include operations associated with semantic classification of data columns. The operations may include receiving a set of data elements corresponding to a data column to be semantically classified, applying a machine learning model to the set of data elements to predict a set of candidate semantic types for the set of data elements, selecting a particular semantic type from the set of candidate semantic types based at least in part on a semantic fit score corresponding to the particular semantic type predicted by the machine learning model, and presenting the particular semantic type as a recommended semantic classification for the data column.

Abstract: Techniques are described herein for automatically discovering undeclared PK-FK relationships between dimension tables and a fact table in situations where: the primary keys of the dimension tables (a) are not known, and (b) are composite keys, and the foreign keys of the fact table (a) are not known, and (b) are composite keys. The techniques involve removing from consideration all fact table columns that satisfy first removal criteria, and all dimension table columns that satisfy second removal criteria. Fingerprints are generated for the fact and dimension table columns that remain. Matched pairs (e.g. two fact table columns and two dimension table columns) are generated based on the columns that have not been filtered. The matched pairs are further filtered based on third removal criteria. The matched pairs that remain are then graded, and a PK-FK relationship is established based on the matched pair with the highest grade.

Abstract: A system is configured for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store.

Abstract: Techniques are provided for determining the semantic-type of a target column based on “fingerprints” that are created based on the values in the target column. The fingerprint set for the target column is only generated once, not once per semantic-type. Thus, the target column only needs to be scanned once, and resource usage is minimized. Once generated, the fingerprint set of the column is compared against fingerprint set that corresponds to each semantic-type to generate a “similarity measure”. The semantic-type whose fingerprint set produces the highest similarity measure relative to the target column's fingerprint set is determined to be the semantic-type of the target column.

Abstract: A system is configured for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store.

Abstract: In accordance with an embodiment, described herein is a system and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment. A cube schema inference component can successively perform column-wise splits to calculate information gain ratios between each pair of a plurality of columns in the tabular data. A cross correlation matrix can be constructed from the information gain ratios between each pair of columns. The system can determine relationships among the plurality of columns based on the cross correlation matrix; create a hierarchy directed graph to represent the relationships; and further transform the hierarchy directed graph into a cube schema that can be used to create a cube for loading the tabular data, or to map the tabular data into an existing cube in the multidimensional database environment.

Abstract: 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.

Abstract: Techniques are provided for determining the semantic-type of a target column based on “fingerprints” that are created based on the values in the target column. The fingerprint set for the target column is only generated once, not once per semantic-type. Thus, the target column only needs to be scanned once, and resource usage is minimized. Once generated, the fingerprint set of the column is compared against fingerprint set that corresponds to each semantic-type to generate a “similarity measure”. The semantic-type whose fingerprint set produces the highest similarity measure relative to the target column's fingerprint set is determined to be the semantic-type of the target column.

Abstract: A system is configured for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store.

Abstract: The application describes a system and method for extracting a star schema from tabular data for use in a multidimensional database. The system can receive a tabular data including a plurality of columns, and determine a relationship between each pair of the plurality of columns by analyzing actual values in a plurality of rows for each pair of columns. Based on the determined relationships among the plurality of columns and a type of each column, the system can use a heuristic process to identify a plurality of cube elements from the plurality of columns to construct a star schema. A user interface can be provided to display potential problems of the star schema, and one or more alternative approaches for a user to select to extract a star schema from the tabular data.

Abstract: A system is configured for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system ef is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store.

Abstract: The application describes a system and method for extracting a star schema from tabular data for use in a multidimensional database. The system can receive a tabular data including a plurality of columns, and determine a relationship between each pair of the plurality of columns by analyzing actual values in a plurality of rows for each pair of columns. Based on the determined relationships among the plurality of columns and a type of each column, the system can use a heuristic process to identify a plurality of cube elements from the plurality of columns to construct a star schema. A user interface can be provided to display potential problems of the star schema, and one or more alternative approaches for a user to select to extract a star schema from the tabular data.

Abstract: In accordance with an embodiment, described herein is a system and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment. A cube schema inference component can successively perform column-wise splits to calculate information gain ratios between each pair of a plurality of columns in the tabular data. A cross correlation matrix can be constructed from the information gain ratios between each pair of columns. The system can determine relationships among the plurality of columns based on the cross correlation matrix; create a hierarchy directed graph to represent the relationships; and further transform the hierarchy directed graph into a cube schema that can be used to create a cube for loading the tabular data, or to map the tabular data into an existing cube in the multidimensional database environment.

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: In accordance with an embodiment, described herein is a system and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment. A cube schema inference component can successively perform column-wise splits to calculate information gain ratios between each pair of a plurality of columns in the tabular data. A cross correlation matrix can be constructed from the information gain ratios between each pair of columns. The system can determine relationships among the plurality of columns based on the cross correlation matrix; create a hierarchy directed graph to represent the relationships; and further transform the hierarchy directed graph into a cube schema that can be used to create a cube for loading the tabular data, or to map the tabular data into an existing cube in the multidimensional database environment.

Abstract: In accordance with an embodiment, described herein is a system and method for extracting a star schema from tabular data for use in a multidimensional database. The system can receive a tabular data including a plurality of columns, and determine a relationship between each pair of the plurality of columns by analyzing actual values in a plurality of rows for each pair of columns. Based on the determined relationships among the plurality of columns and a type of each column, the system can use a heuristic process to identify a plurality of cube elements from the plurality of columns to construct a star schema. A user interface can be provided to display potential problems of the star schema, and one or more alternative approaches for a user to select to extract a star schema from the tabular data.

Abstract: The present invention is a system for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system of the invention is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store. Methods for using the system of the invention are also described.

Abstract: 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.

Abstract: The present invention is a system for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system of the invention is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store. Methods for using the system of the invention are also described.

Abstract: The present invention is a system for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system of the invention is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store. Methods for using the system of the invention are also described.