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: Systems and methods are disclosed for managing a multi-store execution environment by applying opportunistic materialized views to improve workload performance and executing a plan on multiple database engines to increase query processing speed by leveraging unique capabilities of each engine by enabling stages of a query to execute on multiple engines, and by moving materialized views across engines.

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 evolutionary analytics supports three dimensions (analytical workflows, the users, and the data) by rewriting workflows to be more efficient by using answers materialized as part of previous workflow execution runs in the system.

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: There are provided a system and method for predicting query execution time in a database system. A cost model determination device determines a cost model of a database query optimizer for the database system. The cost model models costs of queries applied to the database system. A profiling device determines profiling queries for profiling input/output cost units and processor cost units relating to the database system, and profiles the cost units using the profiling queries to output profiled cost units. A calibrating device calibrates cost units in the cost model responsive to the profiled cost units to output calibrated cost units. A sampling re-estimator samples and re-estimates a cardinality estimation of a final query plan to output an updated cardinality estimation. A predictor applies the calibrated cost units and the updated cardinality estimation in the cost model to generate a prediction of an execution time of a given query.

Abstract: An object of the present invention is to carry out transaction processing on a plurality of records even in the case the transaction processing is executed in an environment that manages records composed of pairs of keys and values.

Abstract: A system to optimize layout of database objects in a relational database management system stored on a plurality of storage classes each characterized by a price and a storage capacity includes a time-based query optimizer and a layout recommender coupled to the time-based query optimizer to estimate a total cost of operation (TCO) for a query workload on each data layout. The layout recommender includes an auxiliary object selection comprising database objects that include auxiliary objects that are optional to place with auxiliary object candidates being given from an auxiliary object recommender component.

Abstract: Systems and methods are disclosed for managing a multi-store execution environment by applying opportunistic materialized views to improve workload performance and executing a plan on multiple database engines to increase query processing speed by leveraging unique capabilities of each engine by enabling stages of a query to execute on multiple engines, and by moving materialized views across engines.

Abstract: An admission control system for a cloud database includes a machine learning prediction module to estimate a predicted probability for a newly arrived query with a deadline, if admitted into the cloud database, to finish its execution before said deadline, wherein the prediction considers query characteristics and current system conditions. The system also includes a decision module applying the predicted probability to admit a query into the cloud database with a target of profit maximization with an expected profit determined using one or more service level agreements (SLAs).

Abstract: A system and method for data sharing are provided. Context parameters associated with a user are gathered from a plurality of applications. A mobile context of the user is determined based on the gathered context parameters. Content is presented to the user in accordance with the mobile context of the user.

Abstract: A method for automatic database design for scalability by receiving a database schema and database workload; applying transaction chopping to split a large transaction into smaller transactions; select one or more transactions using dynamic programming based on transaction weights; deriving a database design that covers the selected transactions; and generating a transaction class design that is scalable.

Abstract: A platform as a service (PaaS) is provided as part of a data sharing scheme. The PaaS is configured to setup a materialized shared space in a cloud. The materialized shared space includes a set of resources invested by the PaaS to permit data sharing between a tenant application and at least one consumer application. A sharing middleware configured to provide the at least one consumer application with access to the data in the materialized shared space.

Abstract: A system for evolutionary analytics supports three dimensions (analytical workflows, the users, and the data) by rewriting workflows to be more efficient by using answers materialized as part of previous workflow execution runs in the system.

Abstract: Systems and method are disclosed for query optimization in a scale-out system with a single query processing machine and a distributed storage engine to store data by receiving a query rewritten for an internal schema; optimizing a query execution plan for the query; and executing the plan and returning result to an application.

Abstract: A system and method are provided for data sharing. A uniform communication framework is provided as part of a sharing service on the cloud platform to facilitate data sharing among a plurality of applications. The uniform communication framework includes an application programming interface which provides a communication gateway to permit a first application to access data of a second application stored in the data store.

Abstract: A system and method are provided for data sharing. A sharing negotiation tool permits at least one consumer application to negotiate a service level agreement on a dataset which is being shared by a tenant application on a cloud. A capacity planner is configured to determine an appropriate allocation of cloud resources to ensure that the service level agreement of the at least one consumer application is satisfied.

Abstract: Systems and methods are disclosed to consolidate workload on cloud-based computers by co-locating one or more high-penalty tenants on one or more cloud-based servers; reducing service level agreement (SLA) violations by over-provisioning the cloud-based server; and maximizing an SLA profit.

Abstract: A system includes one or more application nodes sending SQL queries to one or more query execution nodes. Each query execution node translates the SQL queries into key-based requests to the storage engine, which comprises one or more storage nodes. The system provides an abstraction layer with an external schema and an internal schema, maps the external schema into the internal schema; and generates one or more microshards for the storage node.

Abstract: Systems and methods are disclosed for scheduling one or more queries for execution with one or more cloud computers by applying a service level agreement (SLA) analysis using an SLA-tree data structure to each query in a query queue; using the SLA-tree data structure to determine an overall profit in the query queue; and generating a profit-oriented schedule to select the next query for execution by the cloud computers.