Abstract: A data-knowledge driven multi-objective optimal control method for municipal wastewater treatment process belongs to the field of wastewater treatment. To balance the energy consumption and effluent quality, a data driven multi-objective optimization model, including energy consumption model and effluent quality model are established to obtain the nonlinear relationship along energy consumption, effluent quality and manipulated variables. Meanwhile, a multi-objective particle swarm optimization algorithm, based on evolutionary knowledge, is proposed to optimize the set-points of nitrate nitrogen and dissolved oxygen. Moreover, the proportional integral differential (PID) controller is designed to track the set-points. Then the effluent quality can be improved and the energy consumption can be reduced.

Abstract: To solve problems of frequent occurrence and great harm of membrane fouling during MBR wastewater treatment process, the invention proposes a membrane fouling intelligent early warning method to realize online and accurate early warning of membrane fouling. This early warning method achieves accurate prediction of water permeability by constructing soft-computing model based on recurrent fuzzy neural network. The intelligent early warning of membrane fouling is achieved by the comprehensive evaluation method, which solves the problem that membrane fouling is difficult to be early warning in the MBR wastewater treatment process, improves the pretreatment ability of membrane fouling, reduces the damage caused by membrane fouling, ensures the safe operation of MBR wastewater treatment process, and promotes efficient and stable operation of MBR wastewater treatment plant.

Abstract: A database system includes database software to receive a query specifying a join of plural base tables, and to determine whether a foreign key and primary key relationship exists between join columns specified by the query and a materialized view definition. Based on the existence of the foreign key and the primary key relationship between join columns specified by the query and the materialized view definition, the database software determines that a result for the received query is to be computed from the materialized view.

Abstract: A system and method include defining a snapshot join index using a sparse condition in a join index definition. A new sparse condition of the snapshot join index is compared with an old sparse condition. Rows in a base table are identified as a function of the comparing, and the join index table is updated using the identified rows.

Abstract: A system and method of rewriting a database query where the query contains an aggregate includes the following. If one or more aggregate materialized views are considered, rewriting the query using an aggregate materialized view. If one or more non-aggregate multi-table materialized views are considered, the query is rewritten using a multi-table materialized view. A join cost is determined for each such non-aggregate multi-table materialized view. If one or more non-aggregate single table materialized views are considered, the query is rewritten using the single table materialized view. A join cost is determined for each such non-aggregate single table materialized view. Finally, a current total cost is determined for use of various materialized views as a function of join cost, aggregation cost and spool cost to select a rewritten query.

Abstract: A database system includes a storage to store a view containing results of a cube-based operation on at least one base table, with the view containing a first result set for a group-by on a first grouping set, and a second result set for a group-by on a second grouping set. In response to a change to the at least one base table, a controller updates the first result set by computing a change to the first result set based on a change in the at least one base table, and updates the second result set by computing a change to the second result set based on the change to the first result set.

Abstract: A system and method include obtaining a query and identifying an aggregate join index (AJI) at a high level of aggregation. The dimension table may be rolled-up with the grouping key being the union of the grouping key in the AJI and the grouping key of the query. The identified AJI is joined with the rolled-up dimension table to obtain columns in the query that are not in the identified AJI. The joined AJI and rolled-up dimension table are then rolled up to answer the query.

Abstract: A computer implemented system and method includes obtaining a query referring to rows in a relational database. A sparse index of the database that has a set of rows that is a subset of the rows referred to in the query is obtained. Rows referred to in the query that are not in the sparse index are then obtained and a union of such rows and the rows of the sparse index is performed to obtain a complete row set for processing the query.

Abstract: A system and method include defining a snapshot join index using a sparse condition in a join index definition. A new sparse condition of the snapshot join index is compared with an old sparse condition. Rows in a base table are identified as a function of the comparing, and the join index table is updated using the identified rows.

Abstract: A system and method include obtaining a query and identifying an aggregate join index (AJI) at a high level of aggregation. The dimension table may be rolled-up with the grouping key being the union of the grouping key in the AJI and the grouping key of the query. The identified AJI is joined with the rolled-up dimension table to obtain columns in the query that are not in the identified AJI. The joined AJI and rolled-up dimension table are then rolled up to answer the query.

Abstract: A system and method of rewriting a database query where the query contains an aggregate includes the following. If one or more aggregate materialized views are considered, rewriting the query using an aggregate materialized view. If one or more non-aggregate multi-table materialized views are considered, the query is rewritten using a multi-table materialized view. A join cost is determined for each such non-aggregate multi-table materialized view. If one or more non-aggregate single table materialized views are considered, the query is rewritten using the single table materialized view. A join cost is determined for each such non-aggregate single table materialized view. Finally, a current total cost is determined for use of various materialized views as a function of join cost, aggregation cost and spool cost to select a rewritten query.

Abstract: A method and apparatus determines (1) if a materialized view can be used to respond to a query for data stored in one or more base tables of a database system; and/or (2) if the materialized view needs to be updated in response to an update query. A query expression tree represents the query, and a materialized view expression tree represents the materialized view. The components of the query expression tree are compared with components of the materialized view expression tree to determine if the materialized view covers the query that is, the tuples in the result of the query expression are a subset of the tuples in the result of the materialized view expression.