Abstract: Computer implemented methods for identifying denial constraints are provided herein. The denial constraints can be used with a database schema R. A predicate space P can be generated for an instance I in the schema R. An evidence set EviI can be generated. The evidence set EviI can include sets of satisfied predicates in the predicate space P for each instance I. A minimal set of predicates can be identified for the evidence set EviI. Valid denial constraints can be identified from the minimal set by inverting the predicates in the minimal set.

Abstract: A computer implemented method for integrating data into a target database may include: providing a plurality of source databases which each may include a relational schema and data for integration into the target database; generating at least one complexity model based on the relational schema and data of each source database, each complexity model indicating at least one inconsistency between two or more of the data sources which may be require to be resolved to integrate the data from the data sources into the target database; and generating an effort model that may include an effort value for each inconsistency indicated by each complexity model, each effort value indicating at least one of a time period and a financial cost to resolve the inconsistency to integrate data from the data sources into the target database.

Abstract: A system for checking data for errors, the system comprising a checking module operable to check tuples of data stored in a target database for errors, the tuples in the target database originating from the output of at least one query transformation module which applies a query transformation to tuples of data from at least one data source an identification module operable to identify a problematic tuple from a data source that produces an error in the target database, the identification module being operable to quantify the contribution of the problematic tuple in producing the error in the target database, and a description generation module operable to generate a descriptive query which represents at least one of errors identified by the checking module in the target database which are produced by the at least one query transformation module, and problematic tuples identified in a data source by the identification module.

Abstract: The optimized inequality join method is a method for joining relational tables on input inequality conditions. The optimized inequality join method is a relatively fast inequality join method using permutation arrays to store positional information for sorted attributed values. Additionally, space efficient bit arrays are used to enable optimization, such as Bloom filter indices, thus providing faster computation of the join results. The method may be used, for example, for joining various inequalities associated with a variety of measured environmental conditions for raising an alarm when certain conditions are met.

Abstract: The optimized inequality join method is a method for joining relational tables on input inequality conditions. The optimized inequality join method is a relatively fast inequality join method using permutation arrays to store positional information for sorted attributed values. Additionally, space efficient bit arrays are used to enable optimization, such as Bloom filter indices, thus providing faster computation of the join results. The method may be used, for example, for joining various inequalities associated with a variety of measured environmental conditions for raising an alarm when certain conditions are met.

Abstract: A system for checking data for errors, the system comprising a checking module operable to check tuples of data stored in a target database for errors, the tuples in the target database originating from the output of at least one query transformation module which applies a query transformation to tuples of data from at least one data source an identification module operable to identify a problematic tuple from a data source that produces an error in the target database, the identification module being operable to quantify the contribution of the problematic tuple in producing the error in the target database, and a description generation module operable to generate a descriptive query which represents at least one of errors identified by the checking module in the target database which are produced by the at least one query transformation module, and problematic tuples identified in a data source by the identification module.

Abstract: A computer implemented method for integrating data into a target database may include: providing a plurality of source databases which each may include a relational schema and data for integration into the target database; generating at least one complexity model based on the relational schema and data of each source database, each complexity model indicating at least one inconsistency between two or more of the data sources which may be require to be resolved to integrate the data from the data sources into the target database; and generating an effort model that may include an effort value for each inconsistency indicated by each complexity model, each effort value indicating at least one of a time period and a financial cost to resolve the inconsistency to integrate data from the data sources into the target database.

Abstract: Computer implemented methods for identifying denial constraints are provided herein. The denial constraints can be used with a database schema R. A predicate space P can be generated for an instance I in the schema R. An evidence set EviI can be generated. The evidence set EviI can include sets of satisfied predicates in the predicate space P for each instance I. A minimal set of predicates can be identified for the evidence set EviI. Valid denial constraints can be identified from the minimal set by inverting the predicates in the minimal set.

Abstract: A method for generating nested mapping specifications and transformation queries based thereon. Basic mappings are generated based on source and target schemas and correspondences between elements of the schemas. A directed acyclic graph (DAG) is constructed whose edges represent ways in which each basic mapping is nestable under any of the other basic mappings. Any transitively implied edges are removed from the DAG. Root mappings of the DAG are identified. Trees of mappings are automatically extracted from the DAG, where each tree of mappings is rooted at a root mapping and expresses a nested mapping specification.

Abstract: A method and system for generating nested mapping specifications and transformation queries based thereon. Basic mappings are generated based on source and target schemas and correspondences between elements of the schemas. A directed acyclic graph (DAG) is constructed whose edges represent ways in which each basic mapping is nestable under any of the other basic mappings. Any transitively implied edges are removed from the DAG. Root mappings of the DAG are identified. Trees of mappings are automatically extracted from the DAG, where each tree of mappings is rooted at a root mapping and expresses a nested mapping specification. A transformation query is generated from the nested mapping specification by generating a first query for transforming source data into flat views of the target and a second query for nesting flat view data according to the target format. Generating the first query includes applying default Skolemization to the specification.