Yanicklo Technology Limited Liability Company

Oracle International Corporation

Hyperroll, Israel Limited

Tamiras Per Pte. Ltd., LLC

Yanicklo Technology Limited Liability

Guy Shaked

Reuven Bakalash

Joseph Caspi

Marina Sadetsky

Vladimir Braverman

Victor Belyaev

Gabby Rubin

Dima Alberg

Yossi Matias

Valdimir Braverman

Shahar Golan

David Greenfield

Ilya Tebelev

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.

Techniques for automatically identifying undeclared composite key relationships in a database schema

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.

Method for identifying a sematic type of data contained in a column of a table

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.

Automatic data store architecture detection

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.

System and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment

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.

System and method for extracting a star schema from tabular data for use in a multidimensional database environment

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.

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.

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.

A method of effectively representing and processing data sets with time series is disclosed. The method may comprise representing time series as a virtual part of data in a data store layer of a user system, thereby allowing processing of time-series related queries in said data store layer of said user system.

Methods for effective processing of time series

Improved method of and apparatus for aggregating data elements in multidimensional databases (MDDB). In the preferred embodiment, the apparatus is realized in the form of a high-performance stand-alone (i.e. external) aggregation server which can be plugged-into conventional MOLAP systems to achieve significant improvements in system performance. In accordance with the principles of the present invention, the stand-alone aggregation server contains a scalable MDDB and a high-performance aggregation engine that are integrated into the modular architecture of the aggregation server. The stand-alone aggregation server of the present invention can uniformly distribute data elements among a plurality of processors, for balanced loading and processing, and therefore is highly scalable.

Data aggregation module supporting dynamic query responsive aggregation during the servicing of database query requests provided by one or more client machines

A relational database management system (RDBMS) for servicing query statements through one or more client machines. The RDBMS comprises a query interface adapted to receive query statements from the client machines. The query handling mechanism (i) receives each request from the query interface, (ii) extracts a set of dimensions associated with the request, (iii) uses the dimensions to retrieve aggregated fact data from a multi-dimensional database (MDDB), and (iv) forwards retrieved aggregated fact data to the query processing mechanism for subsequent processing. When the query processing mechanism determines that servicing of one or more query requests require data stored in the relational tables, then the query processing mechanism automatically routes the requests to the relational data tables, so that data can be accessed from the relational tables and forwarded to the query processing mechanism for use in servicing the requests, in a manner transparent to the client machine.

Relational database management system (RDBMS) employing multi-dimensional database (MDDB) for servicing query statements through one or more client machines

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Shdema, Israel

Guy Shaked