Abstract: The present disclosure relates to a method for centrally processing data records using a record linkage algorithm. The method comprises providing a centralized master repository for storing data records in a predefined data structure having a set of attributes. At least one clustering metric is provided. Clusters of records may be determined using a clustering function that is based on the at least one clustering metric. For each particular cluster, a set of configuration data for the record linkage algorithm may be defined based on a value of the clustering metric within that particular cluster. The individual data records may be assigned to one or more clusters of the clusters using the clustering metric values and the record linkage algorithm may be applied to a set of two or more individual data records assigned to at least one common cluster using the set of configuration data for the common cluster.

Abstract: An improved method provides fault handling in a distributed IT environment. The distributed IT environment executes a workflow application interacting with at least one application by using interface information about the at least one application. In response to receiving a first instance of a fault response, a fault handler performing a first lookup of a fault handling policy corresponding to the fault response within a fault handling descriptions catalog. The fault handler loads a first one or more fault handling descriptions that are pointed to by the fault handling policy in order to continue execution of the workflow application. After a second instance of the fault response, the fault handler performs a second lookup of the fault handling policy which now points to a second one or more fault handling descriptions which are loaded in order to continue execution of the workflow application.

Abstract: The present disclosure relates to a method for centrally processing data records using a record linkage algorithm. The method comprises providing a centralized master repository for storing data records in a predefined data structure having a set of attributes. At least one clustering metric is provided. Clusters of records may be determined using a clustering function that is based on the at least one clustering metric. For each particular cluster, a set of configuration data for the record linkage algorithm may be defined based on a value of the clustering metric within that particular cluster. The individual data records may be assigned to one or more clusters of the clusters using the clustering metric values and the record linkage algorithm may be applied to a set of two or more individual data records assigned to at least one common cluster using the set of configuration data for the common cluster.

Abstract: Embodiments of the present invention disclose generating a data profiling jobs for source data in a data processing system, the source data being described by at least one source functional data model. A target functional data model is provided, for describing target data that can be generated from the source data. One or more source functional data models are identified that correspond to the target functional data model. At least one functional source-to-target model mapping is associated to at least one source-target pair based on the target functional data model and identified source functional data models. A physical source-to-target model mapping for at least one source-target pair based on the logical source-to-target model mapping is calculated. For all physical source attributes, the needed data profiling jobs are generated based on the target attribute for analyzing the physical source attributes.

Abstract: The invention relates to a computer implemented method for creating a backup of data by a computer system. The creation of the backup comprises: assigning a first memory space in a main memory to data and loading the data into the first memory space; allocating a second memory space in the main memory for acting as container of one or more images of the data in the first memory space; creating the one or more images of the data in the first memory space; and storing the created one or more images in the second memory space and storing a respective copy of at least one of the one or more images in the one or more non-volatile storage media.

Abstract: According to an embodiment of the present invention, a computer-implemented method of cleansing data is provided that comprises determining a criticality score and a complexity score for identified attributes of an enterprise, wherein the criticality score represents a relevance of an attribute to one or more enterprise dimensions and the complexity score represents complexity of cleansing data for an attribute. The identified attributes for data cleansing based on the criticality and complexity scores are prioritized, and data of the identified attributes is cleansed in accordance with priority of the identified attributes. Embodiments further include a system, apparatus and computer readable media to cleanse data in substantially the same manner as described above.

Abstract: An in-memory application has a state is associated with data (CA0, CB0, CC0) stored in a memory and accessed by the application. A first restore point of the application is determined to represent a first time point (T0) in the execution time associated with a first state at which the application accesses the data being stored in memory locations (CA0) using first addresses (S1) and first pointers (A0) which are stored in a first data structure. A first restore point identifier is assigned to the first restore point, whose value is indicative of (T0). The first restore point identifier is stored in association with (A0) and (S1) in a first entry of a second data structure. In the first data structure, the first addresses (S1) are associated to second pointers (A1) to contents of memory locations (CA1) in the memory, and redirecting writing operations.

Abstract: Embodiments of the present invention disclose generating a data profiling jobs for source data in a data processing system, the source data being described by at least one source functional data model. A target functional data model is provided, for describing target data that can be generated from the source data. One or more source functional data models are identified that correspond to the target functional data model. At least one functional source-to-target model mapping is associated to at least one source-target pair based on the target functional data model and identified source functional data models. A physical source-to-target model mapping for at least one source-target pair based on the logical source-to-target model mapping is calculated. For all physical source attributes, the needed data profiling jobs are generated based on the target attribute for analyzing the physical source attributes.

Abstract: Embodiments provide a system and method for integrating a cognitive system into a call center.

Abstract: Embodiments provide a system and method for integrating a cognitive system into a call center. The system and method include ingesting, in real-time through an instant messaging application, one or more questions from one or more call center agents; ingesting, in real-time through the instant messaging application, one or more answers associated with the one or more questions; storing one or more question and answer pairs in a corpus; analyzing, through a cognitive system, the corpus of the one or more question and answer pairs; receiving, through the instant messaging system, one or more additional questions; determining a proposed answer to each additional question based on the analysis of the corpus; analyzing, through the cognitive system, the proposed answer; and incorporating the analysis of the proposed answer into the analysis of the one or more question and answer pairs. The answers can be provided by subject matter experts or call center managers.

Abstract: In an embodiment of the invention, a method for data profiling incorporating an enterprise service bus (ESB) coupling the target and source systems following an extraction, transformation, and loading (ETL) process for a target system and a source system is provided. The method includes receiving baseline data profiling results obtained during ETL from a source application to a target application, caching the updates, determining current data profiling results within the ESB for cached updates, and triggering an action if a threshold disparity is detected upon the current data profiling results and the baseline data profiling results.

Abstract: In an embodiment of the invention, a method for data profiling incorporating an enterprise service bus (ESB) coupling the target and source systems following an extraction, transformation, and loading (ETL) process for a target system and a source system is provided. The method includes receiving baseline data profiling results obtained during ETL from a source application to a target application, caching the updates, determining current data profiling results within the ESB for cached updates, and triggering an action if a threshold disparity is detected upon the current data profiling results and the baseline data profiling results.

Abstract: In a method for determining appropriate runtime environments for execution of a process model, a computer receives a process model. The process model includes a plurality of activities, wherein two activities are linked by a relationship. The computer determines that the two activities linked by a relationship match a process pattern. The computer determines one or more runtime environments for execution of the process model, wherein each of the one or more runtime environments is capable of executing the process pattern.

Abstract: According to an embodiment of the present invention, a computer-implemented method of cleansing data is provided that comprises determining a criticality score and a complexity score for identified attributes of an enterprise, wherein the criticality score represents a relevance of an attribute to one or more enterprise dimensions and the complexity score represents complexity of cleansing data for an attribute. The identified attributes for data cleansing based on the criticality and complexity scores are prioritized, and data of the identified attributes is cleansed in accordance with priority of the identified attributes. Embodiments further include a system, apparatus and computer readable media to cleanse data in substantially the same manner as described above.

Abstract: The invention provides for a data processing system comprising an application server comprising at least one processor. Execution of the instructions cause the processor to: receive an analysis request, the analysis request comprising multiple data analysis commands for generating an analysis report descriptive of a structured data file; divide the commands into private analysis commands and public analysis commands; send the private analysis commands to a trusted distributed file system; send a portion of the public analysis commands to a public distributed file system; send a remainder of the public analysis commands to the trusted distributed file system; and generate the analysis report using public analysis results from the public distributed file system and trusted analysis results from the trusted distributed file system.

Abstract: A processor receives statistical information about a data set included in a column of a data table. The processor receives additional information about the data set that indicates a data format utilized by the data set and a type of information represented by the data set. The processor generates a data dictionary for compression of the data set based, at least in part, on the statistical information and the additional information. The data dictionary is created such that the data dictionary is capable of compressing data that is statistically predicted to be received at a future point.

Abstract: A computer implemented method, computer program product and system for data quality monitoring includes measuring a data quality of loaded data relative to a predefined data quality metric. The measuring the data quality includes identifying delta changes in at least one of the loaded data and the data quality rules relative to a previous measurement of the data quality of the loaded data. Logical calculus defined in the data quality rules is applied to the identified delta changes.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: In an approach for providing data privacy in information integration systems, a method performed during compilation of an information integration job receives information regarding a data flow structure of the job to be executed, said data flow structure comprising at least one source system, one or more target entities, and at least one operator for modifying output data provided by the source system. The method determines data exit points at which output data are provided to the target entities and determines at least one non-trusted target entity. The method determines, for each non-trusted target entity, if at least one data field included in the output data provided to the non-trusted target entity is classified as sensitive information, and, if so, modifies the information integration job by including a masking operator directly before a data exit point associated with the non-trusted target entity in order to mask said sensitive information.

Abstract: In an approach for providing data privacy in information integration systems, a method performed during compilation of an information integration job receives information regarding a data flow structure of the job to be executed, said data flow structure comprising at least one source system, one or more target entities, and at least one operator for modifying output data provided by the source system. The method determines data exit points at which output data are provided to the target entities and determines at least one non-trusted target entity. The method determines, for each non-trusted target entity, if at least one data field included in the output data provided to the non-trusted target entity is classified as sensitive information, and, if so, modifies the information integration job by including a masking operator directly before a data exit point associated with the non-trusted target entity in order to mask said sensitive information.