Software Ag, Inc.

Verizon Patent and Licensing Inc.

Vijay Anand Chidambaram

Rajesh Kumar Radhakrishnan Varadhan

Balaji Thilagar

Vijayanand Ravivarman

Ulrich Kalex

Gopinath Rajendiran

Aaradhana Sridharan

Vidhyadharan Deivamani

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.

Systems and/or methods for machine-learning based data correction and completion in sparse datasets

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.

Augmented analytics techniques for generating data visualizations and actionable insights

An electronic resource tracking and storage computer system communicates with computing systems operated by different participants. Computing systems store copies of a blockchain and have associated computing devices with sensors. A programmed rule set includes conditions to be met when cooperating to complete, in connection with a resource tracked via the blockchain, a modeled process including modeled tasks. A transceiver receives, from the computing devices, signed electronic data messages including identifiers and values from their respective sensors. Blockchain transactions including identifiers and value(s) in the respective messages are generated. Generated blockchain transactions are published for inclusion in blockchain's copies. Value(s) in the respective electronic data messages are validated against the set of programmed rules. Based on the validations' results, events are emitted to an event bus monitored by a management system. These events selectively trigger the management system to automatically implement modeled tasks in dependence on the validations' results.

Systems and/or methods for securing and automating process management systems using distributed sensors and distributed ledger of digital transactions

A set-top box (STB) determines an occurrence of a first STB error during operation of the STB, and identifies a first error code that corresponds to the first STB error. The STB maps the first error code to a first self-help video of multiple self-help videos stored at the STB, and plays back the first self-video via a display device coupled to the STB.

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Chennai, India

Vijay Anand Chidambaram