Abstract: A system is configured to acquire, from history data, entity data similar to target entity data which is data including a feature value of each of plurality of features related to a target entity. The history data includes entity data for each of a plurality of entities at each past time point. For each entity, entity data at a past time point includes a feature value at the time point for each feature of the entity. The system is configured to create, for each controllable feature among the plurality of features related to the target entity, based on statistics of a plurality of feature values in related case data which is data including entity data similar to the target entity data, a proposal of a feature value change for reducing a risk predicted by inputting the target entity data to a prediction model. A user interface which displays the proposal.

Abstract: To improve resource utilization efficiency of query compliance check, which is check as to whether a query complies with a restriction imposed on data. The query compliance check is roughly divided into data flow policy check as to whether a data flow policy that defines a restriction on data transfer within a county or between countries is complied with, and organization rule check as to whether an organization rule that defines a restriction on sharing outside an organization is complied with, which are hierarchically configured. By analyzing a received query, a system identifies one or more data items as target input/output (I/O) data according to the query, and executes the data flow policy check on the identified one or more data items. When a result of the data flow policy check is false, the system returns a result that the query is compliance violation without executing the organization rule check.

Abstract: Provided are a data management apparatus, a data management system, and a data management method capable of creating an alternative for a useful query when a query for operating a database violates a data handling regulation of the database. When data scheduled to be generated based on the database by the query violates a rule that is the data handling regulation of the database, a data processing server generates an alternative query candidate to replace the query. The data processing server determines whether the alternative query candidate violates the rule by determining whether data scheduled to be generated based on the database by the alternative query candidate complies with the rule. When the alternative query candidate does not violate the rule, the data processing server proposes the alternative query candidate as an alternative query.

Abstract: Provided is a search support device to perform a search related to a parameter representing an influence degree of a feature at high speed and with high accuracy. The search support device calculates at least one or more pieces of SHAP data indicating an influence degree of each feature in a trained model on output data output from the trained model; a process of generating compressed SHAP data, which is data obtained by compressing the SHAP data, for each of the SHAP data and storing the compressed SHAP data; verification target SHAP data which is SHAP data for output data output from the trained model by inputting input data to the trained model; a similarity between each of the calculated compressed SHAP data and the calculated verification target SHAP data, and specifies the compressed SHAP data in which the similarity with the verification target SHAP data satisfies a predetermined condition.

Abstract: To efficiently collect training data for training an AI model, an input of a training profile is received that includes item values corresponding to a plurality of data items, including analysis target data to be analyzed by the AI model and information on the model type. A first query is acquired to extract training data from a training database. The number of pieces of first training data to be extracted from the training database is calculated. The required number of pieces of the training data to train the AI model is calculated using the information on the model type. Whether the number of pieces of the first training data is equal to or greater than the required number is determined. When the determined number of pieces of the first training data is less than the required number, a supplementary query for extracting the training data is generated.

Abstract: Provided is an information processing system by which more appropriate lineage management is possible. A lineage unit management system 3 determines a lineage unit based on a processing content of data processing for generating output data including one or more elements from input data including one or more elements. A lineage management system 4 generates lineage information indicating correspondence relation between the elements of the input data and the elements of the output data in accordance with the lineage unit. Therefore, the lineage information is generated in accordance with the lineage unit corresponding to the content of the data processing, so that more appropriate lineage management is possible.

Abstract: The data management device includes a determination unit that determines whether the secondary data generated from the source data to be processed complies with the data handling rules that use statistical information and a data processing unit that performs the processing a determination result by the determination unit. The determination unit estimates the statistical information from the source data to be processed, and determines whether the data handling rule is complied with, prior to the generation of the secondary data, based on whether the secondary data generated based on the estimated statistical information satisfies the statistical values of the statistical information. The data processing unit executes processing when it is determined that the data handling rules are complied with to generate secondary data and does not perform the processing process when it is determined that the data handling rules are not complied with.

Abstract: Provided is a data processing assistant system, a data processing assistant method, and a data processing assistant program. The data processing assistant system includes: a processing record accumulation unit that accumulates processing records in which one or more pieces of data, data processing performed using the data, and a processing result of the data processing are associated with each other; a correspondence relation data creation unit that creates, based on the processing records, correspondence relation data indicative of a correspondence relation among a data type indicating a type of the data, a question to be solved by the data processing, and the processing result; and a processing information presentation unit that presents, upon receiving designation of the data type and the question, information related to appropriate data processing based on the correspondence relation data.

Abstract: The data management device includes a determination unit that determines whether the secondary data generated from the source data to be processed complies with the data handling rules that use statistical information and a data processing unit that performs the processing a determination result by the determination unit. The determination unit estimates the statistical information from the source data to be processed, and determines whether the data handling rule is complied with, prior to the generation of the secondary data, based on whether the secondary data generated based on the estimated statistical information satisfies the statistical values of the statistical information. The data processing unit executes processing when it is determined that the data handling rules are complied with to generate secondary data and does not perform the processing process when it is determined that the data handling rules are not complied with.

Abstract: In the present invention, there is provided a data integration method executed by a data integration system that corrects inconsistency in type or quality between first data and second data stored in a data lake. The method includes a feature value calculation step of calculating feature values of the first data and the second data, an inconsistency detection step of detecting the inconsistency between the first data and the second data based on the feature values, and a data adjustment step of correcting the inconsistency by using an integrated view according to a request to acquire the first data and the second data from a user.

Abstract: Example implementations described herein are directed to systems and methods for feature preparation that receives patient feature data and determines similarity of pre-stored models with the patient feature data. In an example implementation, a database of the pre-stored models is analyzed to assess similarity indicating that feature preparation of the pre-stored models is compatible with the patient feature data. For similarity indicative of feature preparation to be utilized, the feature preparation is conducted for the patient feature data based on the pre-stored model determined to be similar. The feature preparation retrieves reusable features associate with the similar pre-stored model, where the reusable features comprise pre-calculated features of the model. A machine learning model is generated using results of the feature preparation and patient feature data; and a prediction is provided using the machine learning model.

Abstract: To support an efficient data search. A data processing device comprises a processor, and additionally comprises, as processing units which run on the processor, a generation unit which generates a generated search condition, which is a new search condition, based on a designated search condition, which is a given search condition, an estimation unit which estimates, for each search condition, a number of results of a search conducted based on the designated search condition and the generated search condition by using statistical information of a database to be searched, an evaluation unit which evaluates the generated search condition, and an output unit which outputs a number of estimated results of the designated search condition, and additionally outputs the generated search condition and a number of estimated results and an evaluation result of the generated search condition.

Abstract: Example implementations described herein are directed to systems and methods for selecting appropriate data samples and features in an access and privacy restricted system. Example implementations involve selection of appropriate samples (e.g. patients) which have enough data sources bringing highly important factors based on the experienced risk factors at other facilities, which is stored as metadata. The risk factor management puts more prioritization on some patients which have more data in the required data source than the other patients among all data sample candidates. The similarity of the training data sample can be a criteria to select new sample sets. Further, the risk factor management selects valuable features effectively based on metadata derived from other facilities. Example implementations help improve machine learning accuracy as part of daily system management in a facility, and can be deployed across facilities without compromising access or privacy restrictions of the data.

Abstract: A monitoring apparatus is coupled to a sensor configured to observe a behavior of a monitoring target, and is accessible a storage device being configured to store: a database stored a phenomenon data group; stability information; and operation history information. The monitoring apparatus is configured to: calculate a stability of input phenomenon data; determine whether the stability of the input phenomenon data is a stability indicating a specific behavior; retrieve from the database, when the stability of the input phenomenon data is a stability, similar phenomenon data that is similar to the input phenomenon data; generate, by acquiring from the stability information a stability of subsequent phenomenon data continuous with the similar phenomenon data, similar case data combining the similar phenomenon data and the subsequent phenomenon data; identify, from the operation history information, operation content; and output the similar case data and the operation content.

Abstract: Example implementations described herein are directed to systems and methods for selecting appropriate data samples and features in an access and privacy restricted system. Example implementations involve selection of appropriate samples (e.g. patients) which have enough data sources bringing highly important factors based on the experienced risk factors at other facilities, which is stored as metadata. The risk factor management puts more prioritization on some patients which have more data in the required data source than the other patients among all data sample candidates. The similarity of the training data sample can be a criteria to select new sample sets. Further, the risk factor management selects valuable features effectively based on metadata derived from other facilities. Example implementations help improve machine learning accuracy as part of daily system management in a facility, and can be deployed across facilities without compromising access or privacy restrictions of the data.

Abstract: Example implementations described herein are directed to detection of historical anomalous events that are similar to currently occurring events in a transmission power system based on phasor management unit (PMU) data to provide information to grid operators with online decision support. From the high-resolution time synchronized PMU data, the historical events can be quickly retrieved and compared to the currently occurring event so that operators can be provided with remedy actions that were attempted in response to the historical events. Utilization of PMU information for such decision support may compliment operation practices relying on supervisory control and data acquisition (SCADA) measurements by allowing a much fast response to the currently occurring event. Accurate identification of similar, historical events can advise grid operators of the cause of disturbances and provide ideas for response.

Abstract: Example implementations described herein are directed to systems and methods for feature preparation that receives patient feature data and determines similarity of pre-stored models with the patient feature data. In an example implementation, a database of the pre-stored models is analyzed to assess similarity indicating that feature preparation of the pre-stored models is compatible with the patient feature data. For similarity indicative of feature preparation to be utilized, the feature preparation is conducted for the patient feature data based on the pre-stored model determined to be similar. The feature preparation retrieves reusable features associate with the similar pre-stored model, where the reusable features comprise pre-calculated features of the model. A machine learning model is generated using results of the feature preparation and patient feature data; and a prediction is provided using the machine learning model.

Abstract: Example implementations described herein are directed to detection of historical anomalous events that are similar to currently occurring events in a transmission power system based on phasor management unit (PMU) data to provide information to grid operators with online decision support. From the high-resolution time synchronized PMU data, the historical events can be quickly retrieved and compared to the currently occurring event so that operators can be provided with remedy actions that were attempted in response to the historical events. Utilization of PMU information for such decision support may compliment operation practices relying on supervisory control and data acquisition (SCADA) measurements by allowing a much fast response to the currently occurring event. Accurate identification of similar, historical events can advise grid operators of the cause of disturbances and provide ideas for response.

Abstract: Example implementations described herein are directed to detecting similarity between anomalous events that are currently occurring or have previously occurred transmission power system based on phasor management unit (PMU) data to provide information to grid operators with online decision support. From the high-resolution time synchronized PMU data, the events can be quickly retrieved and compared so that operators can be provided with remedy actions that were attempted in response to the previous events. Utilization of PMU information for such decision support may compliment operation practices relying on supervisory control and data acquisition (SCADA) measurements by allowing a much fast response to the currently occurring event. Accurate identification of similar, historical events can advise grid operators of the cause of disturbances and provide ideas for response. Implementations of the proposed technology may improve the resilience and reliability of the transmission power systems.

Abstract: A monitoring apparatus is coupled to a sensor configured to observe a behavior of a monitoring target, and is accessible a storage device being configured to store: a database stored a phenomenon data group; stability information; and operation history information. The monitoring apparatus is configured to: calculate a stability of input phenomenon data; determine whether the stability of the input phenomenon data is a stability indicating a specific behavior; retrieve from the database, when the stability of the input phenomenon data is a stability, similar phenomenon data that is similar to the input phenomenon data; generate, by acquiring from the stability information a stability of subsequent phenomenon data continuous with the similar phenomenon data, similar case data combining the similar phenomenon data and the subsequent phenomenon data; identify, from the operation history information, operation content; and output the similar case data and the operation content.