Microsoft Technology Licensing, LLC

Ownbackup Ltd.

Salesforce, Inc.

Own Data Company Ltd.

Shai Rubin

Yair Helman

Efim Hudis

Arie Friedman

Yosef Dinerstein

Uri Barash

Ron Jack Karidi

Lior Arzi

Bar Jakubovitz

David Anaton

Roy Emek

Tomer Weisberg

Yehonatan Mazar

Moshe Sapir

Adar Greenshpon

Liron Sarel

Eyal Zangi

Shahar Yalov

Shahar Meir

Yoav Alroy

Eliyahu Shua

A method, including accessing a source database including records stored in tables organized in a hierarchy defining respective parent and dependent tables of the tables in the source database and containing respective parent and dependent records of the records, each record including one or more attributes having respective values. A selection is received for at least a first table and one or more second tables that are related to the first table by being parent tables or dependent tables of the first table. A filtering request is received that includes, for a given attribute having one or more distinct values, a limiting number of records to select having each of the distinct values. The request is applied to select a subset of the records in the one of the tables. A target database is generated including the subset and the records that are related to the records in the subset.

Target environment data seeding

In one embodiment, a system includes a processor configured to search a datastore to find records including a given field with a given value, the found records defining a dataset for consideration for archive having an aggregate data size, render to a display an indication of the aggregate data size of the dataset for consideration for archive, receive a user selection to archive at least part of the dataset, and purge the at least part of the dataset from the datastore and archive the at least part of the dataset into archive storage responsively to receiving the user selection, and a memory configured to store data used by the processor.

Data archiving system

A method, including accessing a source database including records stored in tables, which are organized in a hierarchy defining respective parents and dependents of the tables, containing respective parent and dependent records, the records including attribute values. Upon presenting a first list of the tables, an input is received selecting a first table. Upon presenting a second list of the tables related to the first table, an input is received selecting a second table in the second list. Upon presenting filtering criteria for the selected tables, a filtering request is received for a given selected table. The request is applied to the given selected table to select a subset of the records in the given selected table and to generate a target database including the subset of the records and the records in the set that are the respective directly related records of the records in the subset.

An embodiment features a method of generating test data. An application-level schema corresponding to a source relational database is received. The schema defines constraints comprising one or more of inter-field, inter-record, and inter-object constraints between related data in the source relational database. A random walk is performed on a graph of nodes representing data in the source relational database. At respective ones of the nodes, corresponding ones of the data in the source relational database are selected along a path ordered in accordance with the constraints defined in the schema. Synthetic test data is generated based on one or more statistical models of the data selected from the source relational database. Data values are generated for respective fields of an object defined in the schema, and data values are generated for records related to the object based on one or more of the constraints defined in the schema.

Test data generation for automatic software testing

Information management systems and methods archive and back up data from one or more primary storage devices to one or more secondary storage devices are described. More specifically, systems, methods, and computer program instructions are provided for efficiently transferring relational database data from one or more primary storage devices to one or more secondary storage devices for archival and backup purposes.

Reducing number of queries on a relational database

Systems, methods, and computer program products are described for controlling malicious activity detection with respect to information technology assets based on behavioral models associated with the respective information technology assets. Protection rules and corresponding sensitivities associated with the behavioral models are applied by protection services to detect malicious activity with respect to the information technology assets.

Controlling malicious activity detection using behavioral models

Aspects of the subject matter described herein relate to a mechanism for assessing security. In aspects, an analytics engine is provided that manages execution, information storage, and data passing between various components of a security system. When data is available for analysis, the analytics engine determines which security components to execute and the order in which to execute the security components, where in some instances two or more components may be executed in parallel. The analytics engine then executes the components in the order determined and passes output from component to component as dictated by dependencies between the components. This is repeated until a security assessment is generated or updated. The analytics engine simplifies the work of creating and integrating various security components.

Analytics engine

Aspects of the subject matter described herein relate to tuning detection components of a security system. In aspects, a history of alerts is collected. This history is then used together with knowledge about tunable objects of the system to determine parameters of the tunable objects that can be changed to improve detection of the system. Parameters of tunable objects are adjusted in a simulator that determines an effect on alerts in the history based on the adjusted parameters. A recommendation of one or more tuning actions may be provided together with information regarding the effect of each tuning action.

Detection accuracy tuning for security

Security risk for a single IT asset and/or a set of IT assets in a network such as an enterprise or corporate network may be estimated and represented in a visual form by categorizing risk into different discrete levels. The IT assets may include both computing devices and users. The risk categorization uses a security assessment of an IT asset that is generated to indicate the type of security problem encountered, the severity of the problem, and the fidelity of the assessment. The asset value of an IT asset to the enterprise is also assigned. Security risk is then categorized (and a numeric risk value provided) for each IT asset for different problem types by considering the IT asset value along with the severity and fidelity of the security assessment. The security risk for the enterprise is estimated using the numeric risk value and then displayed in visual form.

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Binyamina, Israel

Shai Rubin