Abstract: Techniques for artificial intelligence assisted story generation includes training a neural network with first training data that indicates text for one or more portions of a training story and second training data that indicates text for a subset of text for an immediately following portion and third training data that indicates full text for the same portion. First data is retrieved that indicates text for a first one or more portions of a different new story. Second data is also received that indicates text for a cued subset of a next portion of the new story. Third data is generated that indicates full text for the next portion of the new story based on the first data and the second data and the neural network. The third data is concatenated to the first data to produce output data that is stored.

Abstract: Techniques for artificial intelligence assisted story generation includes training a neural network with first training data that indicates text for one or more portions of a training story and second training data that indicates text for a subset of text for an immediately following portion and third training data that indicates full text for the same portion. First data is retrieved that indicates text for a first one or more portions of a different new story. Second data is also received that indicates text for a cued subset of a next portion of the new story. Third data is generated that indicates full text for the next portion of the new story based on the first data and the second data and the neural network. The third data is concatenated to the first data to produce output data that is stored.

Abstract: Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: According to one embodiment of the present invention, a system controls cleansing of data within a database system, and comprises a computer system including at least one processor. The system receives a data set from the database system, and one or more features of the data set are selected for determining values for one or more characteristics of the selected features. The determined values are applied to a data quality estimation model to determine data quality estimates for the data set. Problematic data within the data set are identified based on the data quality estimates, where the cleansing is adjusted to accommodate the identified problematic data. Embodiments of the present invention further include a method and computer program product for controlling cleansing of data within a database system in substantially the same manner described above.

Abstract: Methods, computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: A method of blocking column selection can include determining a first parameter for each column set of a plurality of column sets, wherein the first parameter indicates distribution of blocks in the column set, and determining a second parameter for each column set. The second parameter can indicate block size for the column set. For each column set, a measure of blockability that is dependent upon at least the first parameter and the second parameter can be calculated using a processor. The plurality of column sets can be ranked according to the measures of blockability.

Abstract: Blocking column selection can include determining a first parameter for each column set of a plurality of column sets, wherein the first parameter indicates distribution of blocks in the column set, and determining a second parameter for each column set. The second parameter can indicate block size for the column set. For each column set, a measure of blockability that is dependent upon at least the first parameter and the second parameter can be calculated using a processor. The plurality of column sets can be ranked according to the measures of blockability.

Abstract: Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: Methods, computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Abstract: A method of blocking column selection can include determining a first parameter for each column set of a plurality of column sets, wherein the first parameter indicates distribution of blocks in the column set, and determining a second parameter for each column set. The second parameter can indicate block size for the column set. For each column set, a measure of blockability that is dependent upon at least the first parameter and the second parameter can be calculated using a processor. The plurality of column sets can be ranked according to the measures of blockability.

Abstract: Blocking column selection can include determining a first parameter for each column set of a plurality of column sets, wherein the first parameter indicates distribution of blocks in the column set, and determining a second parameter for each column set. The second parameter can indicate block size for the column set. For each column set, a measure of blockability that is dependent upon at least the first parameter and the second parameter can be calculated using a processor. The plurality of column sets can be ranked according to the measures of blockability.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: According to one embodiment of the present invention, a system controls cleansing of data within a database system, and comprises a computer system including at least one processor. The system receives a data set from the database system, and one or more features of the data set are selected for determining values for one or more characteristics of the selected features. The determined values are applied to a data quality estimation model to determine data quality estimates for the data set. Problematic data within the data set are identified based on the data quality estimates, where the cleansing is adjusted to accommodate the identified problematic data. Embodiments of the present invention further include a method and computer program product for controlling cleansing of data within a database system in substantially the same manner described above.

Abstract: According to one embodiment of the present invention, a system controls cleansing of data within a database system, and comprises a computer system including at least one processor. The system receives a data set from the database system, and one or more features of the data set are selected for determining values for one or more characteristics of the selected features. The determined values are applied to a data quality estimation model to determine data quality estimates for the data set. Problematic data within the data set are identified based on the data quality estimates, where the cleansing is adjusted to accommodate the identified problematic data. Embodiments of the present invention further include a method and computer program product for controlling cleansing of data within a database system in substantially the same manner described above.