Abstract: A method for obtaining data items from an unresponsive database host. The method includes receiving an indication that the database host is unresponsive, receiving, from a management server via a diagnostic connection, a first request for a first organized data item, and sending a first query, using a first interface, to a memory for the first organized data item. The method further includes receiving, from the management server via a normal connection, a second request for a second organized data item, retrieving, from memory on the database host, a first data item in response to the first query, converting the first data item into the first organized data item, and sending the first organized data item to the management server, wherein the first organized data item is analyzed to determine a source causing the database host to be unresponsive.

Abstract: Aspects of the present disclosure describe systems and methods for providing active session history data to users for use in database performance analysis. In various aspects, active session history data obtained from monitoring a database and/or database system over a period of time may be segmented into multiple dimensions. The segmented data may be subsequently provide and/or displayed on one or more interfaces, such as a graphical user interface, to users. The interface may visualize the dimensional ASH data in a variety of ways, such as through icons, graphs, charts, histograms, temporal delineations, treemaps, etc.

Abstract: A method for obtaining data items from an unresponsive database host. The method includes receiving an indication that the database host is unresponsive, receiving, from a management server via a diagnostic connection, a first request for a first organized data item, and sending a first query, using a first interface, to a memory for the first organized data item. The method further includes receiving, from the management server via a normal connection, a second request for a second organized data item, retrieving, from memory on the database host, a first data item in response to the first query, converting the first data item into the first organized data item, and sending the first organized data item to the management server, wherein the first organized data item is analyzed to determine a source causing the database host to be unresponsive.

Abstract: Aspects of the present disclosure describe systems and methods for providing active session history data to users for use in database performance analysis. In various aspects, active session history data obtained from monitoring a database and/or database system over a period of time may be segmented into multiple dimensions. The segmented data may be subsequently provide and/or displayed on one or more interfaces, such as a graphical user interface, to users. The interface may visualize the dimensional ASH data in a variety of ways, such as through icons, graphs, charts, histograms, temporal delineations, treemaps, etc.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: Under the present invention, a data exploration system, a customized model system and an existing model system are provided. The data exploration system analyzes user data to identify statistical information such as data distribution, data relationships, data outliners and invalid or missing data values. The customized model center iteratively generates customized data mining models in parallel based on permutations of the user data, user-provided business parameters and/or a set of model generation algorithms. The existing model system provides users with a library of existing data mining models, assembled based on the business parameters, from which they can choose one or more. In any event, any customized or existing data mining models selected can be run against the user data in parallel.

Abstract: Under the present invention, columns of the user data schema are first matched to corresponding columns of the mining model schema. Once the columns are matched, it will be determined whether data within matching columns of the user data schema has a data type different than data within the corresponding columns of the mining model schema. If so, the data within the matching columns of the user data schema is transformed to match the data type of the data within the corresponding columns of the mining model schema. After any transformation is performed, the user/operator is provided with an opportunity to alter or override the mapping. Once the final mapping is provided, one or more matching resources can be updated to reflect the mapping.