Abstract: A cognitive cartography system receives a set of legacy documents, such as maps, in the form of physical hardcopy or as simple graphic images. The system, using rules derived from prior training and experience, revises the documents to resolve formal inconsistences like differences in resolution, orientation, or scale. The system assembles the adjusted documents into a seamless composite document represented as a computerized model. Applying learned rules and logic to contextual information received from extrinsic sources, the system infers semantic meaning from features represented by the composite, such as geographical features of a map. These inferences allow the system to derive new knowledge about the represented features, which is added to the model. When additional documents or contextual information are received, the system further refines the model by repeating this procedure. When the model has been sufficiently refined, the system makes the knowledge available to downstream systems.

Abstract: A continuously updated database is enhanced with virtual dynamic representations of taxonomic groups of records stored in the database. Each record of the database contains a vertical field that identifies a unique entity associated with that record and a horizontal attribute upon which a business decision, related to the corresponding unique entity, may be based. A taxonomic group of database records are defined for each distinct value of the vertical field, and all records in a particular taxonomic group contain a most desirable value of the horizontal field associated with that group's corresponding vertical-field value. The system creates and maintains a virtual representative of each taxonomic group that is automatically updated as the database is updated.

Abstract: A cognitive cartography system receives a set of legacy documents, such as maps, in the form of physical hardcopy or as simple graphic images. The system, using rules derived from prior training and experience, revises the documents to resolve formal inconsistences like differences in resolution, orientation, or scale. The system assembles the adjusted documents into a seamless composite document represented as a computerized model. Applying learned rules and logic to contextual information received from extrinsic sources, the system infers semantic meaning from features represented by the composite, such as geographical features of a map. These inferences allow the system to derive new knowledge about the represented features, which is added to the model. When additional documents or contextual information are received, the system further refines the model by repeating this procedure. When the model has been sufficiently refined, the system makes the knowledge available to downstream systems.

Abstract: A first interaction indication is intercepted from a user interface to interact with a first portion of the user interface. The user interface currently displaying a thumbnail representative of an application window. It is determined from the user interface that the first interaction indication is directed towards the thumbnail representative of the application window. Transmission of the first interaction indication to the use interface is canceled. A thumbnail position of the first interaction indication is calculated. The calculation is based on a position of the first interaction indication before cancelation. The calculated thumbnail position of the first interaction indication is translated to an application position of the application window. A second interaction indication is generated. The second interaction indication is directed towards the application position of the application window.

Abstract: A first interaction indication is intercepted from a user interface to interact with a first portion of the user interface. The user interface currently displaying a thumbnail representative of an application window. It is determined from the user interface that the first interaction indication is directed towards the thumbnail representative of the application window. Transmission of the first interaction indication to the use interface is canceled. A thumbnail position of the first interaction indication is calculated. The calculation is based on a position of the first interaction indication before cancelation. The calculated thumbnail position of the first interaction indication is translated to an application position of the application window. A second interaction indication is generated. The second interaction indication is directed towards the application position of the application window.

Abstract: In an approach for identifying a location of a mobile device a processor receives a first set of Global Positioning System (GPS) data at a time, wherein the time is specified by the mobile device. A processor synchronizes the time with Coordinated Universal Time (UTC). A processor retrieves offset values, wherein the offset values comprise one or more values to adjust one or more location coordinates based on the synchronized time, and wherein the offset values are determined by comparing a known set of location coordinates to a set of location coordinates calculated using another set of GPS data. A processor calculates location coordinates of the mobile device using the first set of GPS data and the offset values. A processor stores at least the location coordinates of the mobile device and the synchronized time.

Abstract: An approach is provided for rationalizing operational data. Persisted knowledge of previous data transactions is determined to not include a previous data profile matching a current data profile. Stochastic data sampling generates a sample of a current dataset. Data size and shape corrections are determined and updated by iterations of a statistical inference technique. Data in the sample is corrected by using the data size and shape corrections to provide a confidence level exceeding a threshold. Persisted knowledge configurations are updated with parameters of the statistical inference technique, the data size and shape corrections, and filtering rule(s). A recommended modification of a query of a current data transaction is determined to provide data in the current dataset with the level of confidence exceeding the threshold. The recommended modification is applied to the query.

Abstract: An approach is provided for rationalizing operational data. Persisted knowledge of previous data transactions is determined to not include a previous data profile matching a current data profile. Stochastic data sampling generates a sample of a current dataset. Data size and shape corrections are determined and updated by iterations of a statistical inference technique. Data in the sample is corrected by using the data size and shape corrections to provide a confidence level exceeding a threshold. Persisted knowledge configurations are updated with parameters of the statistical inference technique, the data size and shape corrections, and filtering rule(s). A recommended modification of a query of a current data transaction is determined to provide data in the current dataset with the level of confidence exceeding the threshold. The recommended modification is applied to the query.

Abstract: An approach is provided for rationalizing operational data. A current data profile of a current dataset utilized by a current data transaction is determined. Persisted knowledge of previous data transactions is determined to include a previous data profile of a previous dataset that matches the current data profile. If the persisted knowledge indicates that data size and shape corrections were applied to the previous dataset, filtering corrections of the current dataset based on the data size and shape corrections are determined, the persisted knowledge is rationalized based on the filtering corrections, and queries of the current transaction are modified based on the filtering corrections, or if the data size and shape corrections were not applied to the previous dataset, the persisted knowledge is rationalized based on the data profile match and the queries of the current transaction are modified based on the rationalized persisted knowledge.

Abstract: Embodiments of the present invention provide a system and method for collaborative data intelligence. Embodiments of the present invention can include one or more data stores and one or more data warehouses (DWHs), each individually configured to communicate with a physical storage management system. Embodiments of the present invention can be used to manage the chaotic disorder of social data in real time, using a collaborative DWH model and big data store, in order to assess the potential of data integration to reporting and decision management.

Abstract: A continuously updated database is enhanced with virtual dynamic representations of taxonomic groups of records stored in the database. Each record of the database contains a vertical field that identifies a unique entity associated with that record and a horizontal attribute upon which a business decision, related to the corresponding unique entity, may be based. A taxonomic group of database records are defined for each distinct value of the vertical field, and all records in a particular taxonomic group contain a most desirable value of the horizontal field associated with that group's corresponding vertical-field value. The system creates and maintains a virtual representative of each taxonomic group that is automatically updated as the database is updated.

Abstract: The present specification relates to data warehousing, and, more specifically, to systems and methods for optimizing the preparation and use of data cubes (a/k/a quasi-cubes) based on inputs such as particular rules/decisions designed to appropriately size the cubes by effectively removing the static piling of unwarranted dimensions from the each instance/cycle of aggregated schema which are used for neither analysis nor by business intelligence reporting.

Abstract: A method and associated systems for generating a virtual dynamic representative of a taxonomic group with unique inheritance of attributes. Each record of a continuously updated database identifies a “vertical” attribute of an entity associated with that record, and further identifies a “horizontal” attribute upon which a business decision may be based. An account-management apparatus selects a subset of these records that are associated with a particular value of the vertical attribute and identifies the most desirable value of the horizontal attribute that is associated with any of the selected records. The apparatus then creates a virtual representative of the subset that represents the subset as a taxonomic group associated with the particular value of the vertical attribute and associates all records of the group with the most desirable value of the horizontal attribute.

Abstract: A mobility database is updated with new data relating to a location of a user as the user enters a new operating environment upon leaving a prior operating environment. The new data includes mobility and environmental data relating to a third-party. The third-party data is relevant to the user and to operation of a mobile device. The mobility and environmental data are based on a relationship that the third party has with the user's mobility environment. The mobile device is configured for operation, based on previously collected real-time mobility data relating to the user and devices associated with the user. The devices include the mobile device. The mobile device is activated for subsequent operation, based on the collected real-time mobility data. Operation of the activated mobile device is controlled, based on the new data in the mobility database and other data being acquired in real time.

Abstract: A mobility database is updated with new data relating to a location of a user as the user enters a new operating environment upon leaving a prior operating environment. The new data includes mobility and environmental data relating to a third-party. The third-party data is relevant to the user and to operation of a mobile device. The mobility and environmental data are based on a relationship that the third party has with the user's mobility environment. The mobile device is configured for operation, based on previously collected real-time mobility data relating to the user and devices associated with the user. The devices include the mobile device. The mobile device is activated for subsequent operation, based on the collected real-time mobility data. Operation of the activated mobile device is controlled, based on the new data in the mobility database and other data being acquired in real time.

Abstract: Real-time mobility data relating to a user and devices associated with the user are collected. The collected mobility data to a device being used in a respective environment is applied. The operation of the device based on the applied data is activated and controlled. The mobility database is updated with new data relating to the location of the user as the user enters a new operating environment. New data includes mobility and environmental data relating to a third-party. The third-party data is relevant to the user and to operation of a mobile device.

Abstract: In an approach for identifying a location of a mobile device a processor receives a first set of Global Positioning System (GPS) data at a time, wherein the time is specified by the mobile device. A processor synchronizes the time with Coordinated Universal Time (UTC). A processor retrieves offset values, wherein the offset values comprise one or more values to adjust one or more location coordinates based on the synchronized time, and wherein the offset values are determined by comparing a known set of location coordinates to a set of location coordinates calculated using another set of GPS data. A processor calculates location coordinates of the mobile device using the first set of GPS data and the offset values. A processor stores at least the location coordinates of the mobile device and the synchronized time.

Abstract: Embodiments of the present invention provide a system and method for collaborative data intelligence. Embodiments of the present invention can include one or more data stores and one or more data warehouses (DWHs), each individually configured to communicate with a physical storage management system. Embodiments of the present invention can be used to manage the chaotic disorder of social data in real time, using a collaborative DWH model and big data store, in order to assess the potential of data integration to reporting and decision management.

Abstract: A method and associated systems for generating a virtual dynamic representative of a taxonomic group with unique inheritance of attributes. Each record of a continuously updated database identifies a “vertical” attribute of an entity associated with that record, and further identifies a “horizontal” attribute upon which a business decision may be based. An account-management apparatus selects a subset of these records that are associated with a particular value of the vertical attribute and identifies the most desirable value of the horizontal attribute that is associated with any of the selected records. The apparatus then creates a virtual representative of the subset that represents the subset as a taxonomic group associated with the particular value of the vertical attribute and associates all records of the group with the most desirable value of the horizontal attribute.

Abstract: An approach is provided for rationalizing operational data. A current data profile of a current dataset utilized by a current data transaction is determined. Persisted knowledge of previous data transactions is determined to include a previous data profile of a previous dataset that matches the current data profile. If the persisted knowledge indicates that data size and shape corrections were applied to the previous dataset, filtering corrections of the current dataset based on the data size and shape corrections are determined, the persisted knowledge is rationalized based on the filtering corrections, and queries of the current transaction are modified based on the filtering corrections, or if the data size and shape corrections were not applied to the previous dataset, the persisted knowledge is rationalized based on the data profile match and the queries of the current transaction are modified based on the rationalized persisted knowledge.