Abstract: The present invention refers to the field of transactional data management. The invention provides a transactional semantic concurrency control system for managing a database, wherein the system comprises a data store engine which is adapted to manage the database comprising value rows, and each value row comprising a primary key uniquely identifying each row, at least one semantic column and a commit timestamp; wherein the data store engine is further adapted to manage operation rows, which are rows comprising a primary key, at least one operation to be applied over at least one semantic column of a value row identified by the primary key, and a commit timestamp; store the value rows and the operation rows; generate an operation row when it receives an operation that inserts, updates, upserts, and/or deletes a row of the database with the commit timestamp of the operation; and apply the operations of at least one operation row when a trigger condition is satisfied.

Abstract: The present invention refers to the field of transactional data management. The invention provides a transactional semantic concurrency control system for managing a database, wherein the system comprises a data store engine which is adapted to manage the database comprising value rows, and each value row comprising a primary key uniquely identifying each row, at least one semantic column and a commit timestamp; wherein the data store engine is further adapted to manage operation rows, which are rows comprising a primary key, at least one operation to be applied over at least one semantic column of a value row identified by the primary key, and a commit timestamp; store the value rows and the operation rows; generate an operation row when it receives an operation that inserts, updates, upserts, and/or deletes a row of the database with the commit timestamp of the operation; and apply the operations of at least one operation row when a trigger condition is satisfied.

Abstract: A continuous query parallel engine on data streams provides scalability and increases the throughput by the addition of new nodes. The parallel processing can be applied to data stream processing and complex events processing. The continuous query parallel engine receives the query to be deployed and splits the original query into subqueries, obtaining at least one subquery; each subquery is executed in at least in one node. Tuples produced by each operator of each subquery are labeled with timestamps. A load balancer is interposed at the output of each node that executes each one of the instances of the source subquery and an input merger is interposed in each node that executes each one of the instances of a destination subquery. After checks are performed, further load balancers or input managers may be added.

Abstract: A system and method for decentralized transaction processing that reduce contention by a number of techniques. First, the transactional management system is decomposed in a number of components that can be scaled out and/or scaled up independently and in a composable way. Second, transactions are committed in parallel without blocking each other. Third, applications can progress when the transactions updates are durable even if the writes of the transaction have not yet completed. Fourth, transactional consistency is guaranteed by making readable only gap-free prefixes of committed update transactions, without blocking the commit of new transactions. Fifth, session consistency is guaranteed by delaying the start of a new transaction till the snapshot of any previous update transaction in the same session is readable.

Abstract: A system and method for decentralized transaction processing that reduce contention by a number of techniques. First, the transactional management system is decomposed in a number of components that can be scaled out and/or scaled up independently and in a composable way. Second, transactions are committed in parallel without blocking each other. Third, applications can progress when the transactions updates are durable even if the writes of the transaction have not yet completed. Fourth, transactional consistency is guaranteed by making readable only gap-free prefixes of committed update transactions, without blocking the commit of new transactions. Fifth, session consistency is guaranteed by delaying the start of a new transaction till the snapshot of any previous update transaction in the same session is readable.

Abstract: A continuous query parallel engine on data streams provides scalability and increases the throughput by the addition of new nodes. The parallel processing can be applied to data stream processing and complex events processing. The continuous query parallel engine receives the query to be deployed and splits the original query into subqueries, obtaining at least one subquery; each subquery is executed in at least in one node. Tuples produced by each operator of each subquery are labeled with timestamps. A load balancer is interposed at the output of each node that executes each one of the instances of the source subquery and an input merger is interposed in each node that executes each one of the instances of a destination subquery. After checks are performed, further load balancers or input managers may be added.

Abstract: A continuous query parallel engine on data streams provides scalability and increases the throughput by the addition of new nodes. The parallel processing can be applied to data stream processing and complex events processing. The continuous query parallel engine receives the query to be deployed and splits the original query into subqueries, obtaining at least one subquery; each subquery is executed in at least in one node. Tuples produced by each operator of each subquery are labeled with timestamps. A load balancer is interposed at the output of each node that executes each one of the instances of the source subquery and an input merger is interposed in each node that executes each one of the instances of a destination subquery. After checks are performed, further load balancers or input managers may be added.

Abstract: A continuous query parallel engine on data streams provides scalability and increases the throughput by the addition of new nodes. The parallel processing can be applied to data stream processing and complex events processing. The continuous query parallel engine receives the query to be deployed and splits the original query into subqueries, obtaining at least one subquery; each subquery is executed in at least in one node. Tuples produced by each operator of each subquery are labeled with timestamps. A load balancer is interposed at the output of each node that executes each one of the instances of the source subquery and an input merger is interposed in each node that executes each one of the instances of a destination subquery. After checks are performed, further load balancers or input managers may be added.