Abstract: Systems and methods are disclosed for query processing in a big data analytics platform by enumerating plans for a current query using a processor; building a dominance graph for the current query; for each plan, determining a regret value and a score for the plan based on the regret value and cost; and selecting query plans in an online fashion for query processing in big data analytics platforms where intermediate results are materialized and can be reused later.

Abstract: A system includes first and second data stores, each store having a set of materialized views of the base data and the views comprise a multistore physical design; an execution layer coupled to the data stores; a query optimizer coupled to the execution layer; and a tuner coupled to the query optimizer and the execution layer, wherein the tuner determines a placement of the materialized views across the stores to improve workload performance upon considering each store's view storage budget and a transfer budget when moving views across the stores.

Abstract: A system includes first and second data stores, each store having a set of materialized views of the base data and the views comprise a multistore physical design; an execution layer coupled to the data stores; a query optimizer coupled to the execution layer; and a tuner coupled to the query optimizer and the execution layer, wherein the tuner determines a placement of the materialized views across the stores to improve workload performance upon considering each store's view storage budget and a transfer budget when moving views across the stores.

Abstract: A system for fair costing of dynamic data sharing in a cloud market is disclosed. The system uses an online method for sharing plan selection, as well as a set of fair costing criteria and a method that maximizes fairness.

Abstract: Systems and methods are disclosed for query processing in a big data analytics platform by enumerating plans for a current query using a processor; building a dominance graph for the current query; for each plan, determining a regret value and a score for the plan based on the regret value and cost; and selecting query plans in an online fashion for query processing in big data analytics platforms where intermediate results are materialized and can be reused later.

Abstract: Systems and methods are disclosed to run a multistore system by receiving by-products of query processing in the multistore system, wherein the by-products include views or materializations of intermediate data; placing the views or materializations across the stores based on recently observed queries as indicative of a future query workload; determining a benefit score for each view based on a predicted future query workload, wherein each store has an allotted view storage budget, and there is a view transfer budget for transferring views between the stores; and tuning a physical design of the multistore system.

Abstract: A computer implemented method for cloud bursting applied to a database includes employing a database live migration using a database hot backup capability to support database cloud bursting without client service interruption in multitenant databases, ascertaining a critical time to start/redo the database cloud bursting to a public cloud so as to meet a service level agreement SLA while leveraging a private cloud for the database to a maximum level, and determining tenants of the database to burst into the public cloud to responsive to costs and benefits.

Abstract: A method and system for providing user-friendly access to a database are described. More particularly, embodiments of the present invention provide a database server. There is at least one database coupled to the database server, wherein the database server enables access to the at least one database. A user-defined interface configured to have access to the at least one database, wherein the user-defined interface is customized to have functionality to suit specific needs of a user.

Abstract: A system, method and programmed article of manufacture to perform efficient encryption key updates in encrypted database-as-a-service (DAS) environments using a key registry and key locks. A database as a service environment allows organizations to outsource their data management infrastructures to a database service provider. The service provider employs data encryption techniques to ensure the privacy of hosted data. The security of encryption techniques relies on the confidentiality of the encryption keys. The dynamic nature of the encrypted database in the DAS model adds complexity and raises specific requirements on key management techniques. The solution is provided by the key registry and by the key update lock, key management process and log management process to allow data update access to data concurrently with encryption key update for the same data.

Abstract: A system and method for providing an integrated storage appliance is disclosed. The storage appliance includes a front-end processor cluster, a storage subsystem coupled to the processor cluster, and a customizable software stack executed by the processor cluster, wherein the customizable software stack includes an operating system and storage-intensive application software.

Abstract: The present invention provides a computer-readable medium and system for selecting a set of n-grams for indexing string data in a DBMS system. Aspects of the invention include providing a set of candidate in grams, each n-gram comprising a sequence of characters; identifying sample queries having character strings containing the candidate n-grams; and based on the set of candidate n-grams, the sample queries, database records, and an n-gram space constraint, automatically selecting, given the space constraint, a minimal set of an n-grams from the set of candidate n-grams that minimizes the number of false hits for the set of sample queries had the sample queries been executed against the database records.

Abstract: A data warehouse is created using an input file that can contain sub-documents of different formats. A root document model including path names to all nodes among the sub-documents is generated, and a table is generated with columns being derived from the path names of the root model. The sub-documents are shredded to populate the table. Then, the dimensions of the data warehouse are defined by selecting respective columns. A routine such as a DDL may then be generated to populate the data warehouse with data.