Abstract: The invention is generally directed to systems and methods of automatically tuning big data workloads across various cloud platforms, the system being in communication with a cloud platform and a user, the cloud platform including data storage and a data engine. The system may include: a system information module in communication with the cloud platform; a static tuner in communication with the system information module; a cloud tuner in communication with the static tuner and the user; and an automation module in communication with the cloud tuner. Methods may include extracting information impacting or associated with the performance of the big data workload from the cloud platform; determining recommendations based at least in part on the information extracted; iterating through different hardware configurations to determine optimal hardware and data engine configuration; and applying the determined configuration to the data engine.

Abstract: A system and method extends model verification through the creation of composite test objectives. A composite objective includes two or more logically or temporally combined standard or basic test objectives. The basic test objectives selected to form a composite test may be automatically generated by the system or method, or they may be custom defined. A composite test objective represents a new coverage objective that extends model coverage analysis beyond the coverage that is available with the basic test objectives. The system and method also automatically generates one or more test cases for achieving the composite objective. The test cases include input data values for the model, and may cause the specified logical or temporal combination of basic test objectives to evaluate to true at least once during simulation of the model or according to the temporal combination.

Abstract: The invention is generally directed to systems and methods of automatically tuning big data workloads across various cloud platforms, the system being in communication with a cloud platform and a user, the cloud platform including data storage and a data engine. The system may include: a system information module in communication with the cloud platform; a static tuner in communication with the system information module; a cloud tuner in communication with the static tuner and the user; and an automation module in communication with the cloud tuner. Methods may include extracting information impacting or associated with the performance of the big data workload from the cloud platform; determining recommendations based at least in part on the information extracted; iterating through different hardware configurations to determine optimal hardware and data engine configuration; and applying the determined configuration to the data engine.

Abstract: A system and method extends model verification through the creation of composite test objectives. A composite objective includes two or more logically or temporally combined standard or basic test objectives. The basic test objectives selected to form a composite test may be automatically generated by the system or method, or they may be custom defined. A composite test objective represents a new coverage objective that extends model coverage analysis beyond the coverage that is available with the basic test objectives. The system and method also automatically generates one or more test cases for achieving the composite objective. The test cases include input data values for the model, and may cause the specified logical or temporal combination of basic test objectives to evaluate to true at least once during simulation of the model or according to the temporal combination.

Abstract: In general, the present invention is directed to systems and corresponding methods for providing metadata aware background caching amongst various tables in data processing systems, the system configured to process either an original copy of data stored or data stored in derived tables in one or more data stores, the system including: a query optimization module, a catalog module, and a dataset manager. Each of the query optimization module, catalog module, and dataset manager may be communicatively connected to the original copy of data and the derived tables in one or more data stores. The query optimization module configured to conduct queries against data stored in the original copy of data or in the derived tables; the catalog module configured to register tables of data across various types and formats of data stores; and the dataset manager configured to maintain the freshness of the data in the derived tables.