Abstract: Certain example embodiments herein relate to techniques for automatically correcting and completing data in sparse datasets. Records in the dataset are divided into groups with properties having similar values. For each group, one or more properties of the records therein that is/are to be ignored is/are identified, based on record distances relative to the records in the group, and distances among values for each of the properties of the records in the respective group. The records in the groups are further divided into sub-groups without regard to the one or more properties that is/are to be ignored. The sub-groups include a smaller and more cohesive set of records. For each sub-group: based on the records therein, predicted values to be applied to values identified as being empty but needing to be filled in are determined; and those predicted values are applied. The corrected/completed dataset is provided as output.

Abstract: A data analysis system is provided. Processing resources are configured to at least: identify features within a dataset, identify potential features of interest therefrom, and enable selection of one of the identified potential features of interest. Responsive to an identified potential feature of interest being selected: (a) algorithms are run on the dataset to identify at least one related feature that the selected feature of interest is most likely and/or most heavily influenced by; (b) a display is generated to include a visual representation of each related feature, each including associated data value representations; and (c) a visual representation can be selected. A data value representation is selectable together with the selected visual representation. Responsive selection of the visual representation, (a)-(c) are repeated. Responsive to a data value representation being selected in (c), the dataset is filtered based on it, and the repetition is performed with the filtered dataset.

Abstract: Certain example embodiments herein relate to techniques for automatically correcting and completing data in sparse datasets. Records in the dataset are divided into groups with properties having similar values. For each group, one or more properties of the records therein that is/are to be ignored is/are identified, based on record distances relative to the records in the group, and distances among values for each of the properties of the records in the respective group. The records in the groups are further divided into sub-groups without regard to the one or more properties that is/are to be ignored. The sub-groups include a smaller and more cohesive set of records. For each sub-group: based on the records therein, predicted values to be applied to values identified as being empty but needing to be filled in are determined; and those predicted values are applied. The corrected/completed dataset is provided as output.

Abstract: A data analysis system is provided. Processing resources are configured to at least: identify features within a dataset, identify potential features of interest therefrom, and enable selection of one of the identified potential features of interest. Responsive to an identified potential feature of interest being selected: (a) algorithms are run on the dataset to identify at least one related feature that the selected feature of interest is most likely and/or most heavily influenced by; (b) a display is generated to include a visual representation of each related feature, each including associated data value representations; and (c) a visual representation can be selected. A data value representation is selectable together with the selected visual representation. Responsive selection of the visual representation, (a)-(c) are repeated. Responsive to a data value representation being selected in (c), the dataset is filtered based on it, and the repetition is performed with the filtered dataset.