Patent application EP21174979A (EP), filed 2021-05-20
Publication number: EP3913552A1
Title: DATA CORRECTNESS OPTIMIZATION
Abstract: The present invention relates to providing a ground truth dataset and optimizing data correctness using the ground truth dataset. A plurality of datasets is received from different data sources (610A, 610B, 610C). The datasets comprise a plurality of data elements. Each data element includes an identifier and at least one attribute value associated with the identifier. Data correctness values are determined for the attribute values. A data correctness value is associated with a probability that an attribute value is correct. A data element with a single data correctness value is added to the ground truth dataset for each attribute value for each identifier with which a respective attribute value is associated based on the determined data correctness values for the attribute values such that the data correctness values in the ground truth dataset define probability distributions of data correctness for the attribute values. A new dataset can be received from a data source (610A, 610B, 610C) with the new dataset comprising a plurality of data elements and each data element including an identifier and at least one attribute value associated with the identifier. Data correctness values for the attribute values of the data elements of the new dataset can be determined based on the data correctness values for the attribute values of the data elements with known identifiers of the ground truth dataset. A known identifier is an identifier which is included in the ground truth dataset and in the new dataset.
IPC codes: G06F  16/   215            A I
G06N  20/    00            A I
Claims: number: 1
text: A computer implemented method for providing a ground truth dataset, comprising:
using at least one processor (604) for:
number: 2
text: The computer implemented method of claim 1, wherein the data correctness values for the attribute values are determined by a method for determining a data correctness value known in the prior art.
number: 3
text: The computer implemented method of claim 1 or 2, wherein the at least one processor (604) is further configured to determine a data correctness value for an attribute value received from a respective data source (610A, 610B, 610C) for a subset of data elements received from the respective data source (610A, 610B, 610C) and to assign the data correctness value for the attribute value determined for the subset of the data elements to the attribute values of the same attribute of the other data elements received from the respective data source (610A, 610B, 610C).
number: 4
text: The computer implemented method of at least one of the claims 1 to 3, wherein the at least one processor (604) is configured to determine the single data correctness value for the respective attribute value for a respective identifier based on data correctness values for the attribute value received from different data sources (610A, 610B, 610C), when two or more data elements with the respective identifier and an identical attribute are included in the plurality of datasets received from the different data sources (610A, 610B, 610C).
number: 5
text: The computer implemented method of claim 4, wherein the at least one processor (604) is configured to determine the single data correctness value for the respective attribute value for the respective identifier by calculating  p = ∏ i n p i ∏ i n p i + ∏ i n 1 − p i
number: 6
text: The computer implemented method of at least one of the claims 1 to 5, wherein the at least one processor (604) is configured to determine the single data correctness value for the respective attribute value for a respective identifier based on calculating  p i = ∑ j k p ij k
number: 7
text: A computer implemented method for optimizing data correctness using a ground truth dataset, comprising:
using at least one processor (604) for:
number: 8
text: The computer implemented method of claim 7, wherein the at least one processor (604) is further configured to determine a data correctness value for at least one attribute value of data elements of the new dataset with unknown identifier based on a probability distribution of data correctness for the respective attribute value, when the new dataset comprises a threshold level of data elements including a known identifier, wherein an unknown identifier is an identifier which is not included in the ground truth dataset.
number: 9
text: The computer implemented method of claim 8, wherein the at least one processor (604) is configured for:
number: 10
text: The computer implemented method of at least one of the claims 7 to 9, wherein the at least one processor (604) is configured to add, to the ground truth dataset, the data elements of the new dataset with unknown identifier for which the data correctness values for the attribute values were determined.
number: 11
text: The computer implemented method of at least one of the claims 1 to 10, wherein the at least one processor (604) is configured to compare the data correctness values for a respective attribute value of respective data elements of the ground truth dataset to data correctness values for the respective attribute value of the respective data elements determined by a method for determining a data correctness value known in the prior art in dependence of a triggering event.
number: 12
text: The computer implemented method of claim 11, wherein the at least one processor (604) is configured to adjust the data correctness values for the respective attribute value of the respective data elements of the ground truth dataset based on a difference between the data correctness values for the respective attribute value of the respective data elements of the ground truth dataset and the data correctness values for the respective attribute value of the respective data elements determined by the method for determining a data correctness value known in the prior art.
number: 13
text: The computer implemented method of claim 11 or 12, wherein the at least one processor (604) is configured for:
number: 14
text: A data correctness management system (600) for optimizing data correctness using a ground truth dataset, comprising:
number: 15
text: The data correctness management system (600) of claim 14, the code comprising program instructions to determine a data correctness value for attribute values of data elements of the new dataset with unknown identifier based on a probability distribution of data correctness for a respective attribute value, when the new dataset comprises a threshold level of data elements including a known identifier, wherein an unknown identifier is an identifier which is not included in the ground truth dataset.
Office action: search_report, dated 2021-11-24
Prior art cited: ref_id: - SAHIN GEYIK ET AL.: "Towards Data Quality Assessment in Online Advertising", ARXIV:1711.11175
ref_type: npl
severity: background
ref_id: - MELINDA HAN WILLIAMS ET AL.: "Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth", ADKDD'14: PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORKSHOP ON DATA MINING FOR ONLINE ADVERTISING, August 2014 (2014-08-01), pages 1 - 9
ref_type: npl
severity: background
ref_id: - CHOUDHURY AMBIKA: "Zeotap Is Using Deep Learning To Drive Growth In Customer Intelligence", 24 April 2020 (2020-04-24), pages 1 - 6, XP055843873, Retrieved from the Internet <URL:https://analyticsindiamag.com/how-this-berlin-based-startup-is-using-deep-learning-to-drive-growth-in-customer-intelligence/> [retrieved on 20210922]
ref_type: npl
severity: background
ref_id: - Choudhury Ambika, "Zeotap Is Using Deep Learning To Drive Growth In Customer Intelligence", (20200424), pages 1 - 6, URL: https://analyticsindiamag.com/how-this-berlin-based-startup-is-using-deep-learning-to-drive-growth-in-customer-intelligence/, (20210922), XP055843873 [I] 1-15 * page  2 *
ref_type: npl
severity: background
Final disposition: pending (2021-11-24)