Qatar Foundation

Hasso-plattner-institut Fur Softwaresystemtechnik Gmbh

Qatar Foundation for Education, Science and Community

Paolo Papotti

Ihab Francis Ilyas Kaldas

Mourad Ouzzani

Felix Naumann

Nan Tang

Jorge-Arnulfo Quiane-Ruiz

Sebastian Kruse

Anup Chalmalla

Zuhair Khayyat

El Kindi Rezig

William Lucia

Xu Chu

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 Evican be generated. The evidence set Evican 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 Evi. Valid denial constraints can be identified from the minimal set by inverting the predicates in the minimal set.

Methods for identifying denial constraints

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.

Systems and methods for data integration

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.

System and method for checking data for errors

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.

Optimized inequality join method

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Doha, Qatar

Paolo Papotti