Abstract: Example implementations described herein involve systems and methods for efficient learning for mixture of domains which can include applying a clustering technique to a set of data comprised of multiple domains to obtain an initial domain separation of the set of data into one or more clusters; training one or more experts associated with each of the one or more clusters based on the initial domain separation where each expert corresponds with one domain of the multiple domains; inputting all data points to the one or more experts for refining each of the one or more clusters using expert output probabilities; retraining the one or more experts based on the refined one or more clusters; and training a gating mechanism to route an input to an appropriate expert of the one or more experts based on the refined one or more clusters.

Abstract: A method for generating simulations based on data. The method may include receiving internal data and external data which affect output data; differencing the internal data, the external data, and the output data to create first differenced data; training a machine learning model using the first differenced data: receiving scenario input data as input to the machine learning model, wherein the scenario input data comprises conditional data input from a user and/or input data obtained from a separately simulated machine learning data change model, the conditional data input including at least one feature dimension associated with at least one of the internal data or the external data, and the input data including at least one feature dimension associated with at least one of the internal data or the external data; and generating predicted output based on the scenario input data.

Abstract: Easy and correct estimation of a relationship between entities is made possible. An entity relationship estimation apparatus that estimates a relationship between a plurality of entities each executing a process is configured to include: a window-based correlation and traffic volume estimation unit that acquires traffic data of a plurality of entities, and calculates correlation values in one or more windows of a predetermined time width of each traffic data of at least one entity pair; and a relationship type estimation unit that estimates a type of relationship between entities of the entity pair based on the correlation values of the windows of the entity pair.

Abstract: An outlier detection apparatus creates a first processing window having a designated window length and a second processing window having a designated window length, and performs sliding alignment for sliding the second processing window relative to the first processing window by a designated sliding alignment length. The apparatus performs one or more types of outlier sub-detections. The outlier sub-detection includes comparing, by a method corresponding to the type of outlier sub-detection, a real time series dataset which is a data portion corresponding to the first processing window from among real time series data which is a time series of real values, with a forecasted time series dataset which is a data portion corresponding to the second processing window after the sliding alignment from among forecasted time series data which is a time series of forecasted values. The apparatus decides whether an outlier candidate is an outlier.

Abstract: Aspects relate to determining the presence or absence of concept drift in seasonal time series data. A concept drift detection device including a data input unit for receiving a time series data set that includes a set of past time series data and a set of current time series data, a baseline model generation unit for generating a baseline model based on a subset of the set of past time series data, a feature extraction unit for extracting a set of past data features, a set of baseline data features, and a set of current data features, a distance calculation unit for calculating a baseline distance and a current distance, and a concept drift detection unit for determining, based on a baseline statistic and the current distance, presence or absence of concept drift between the set of current time series data and the set of past time series data.

Abstract: Easy and correct estimation of a relationship between entities is made possible. An entity relationship estimation apparatus that estimates a relationship between a plurality of entities each executing a process is configured to include: a window-based correlation and traffic volume estimation unit that acquires traffic data of a plurality of entities, and calculates correlation values in one or more windows of a predetermined time width of each traffic data of at least one entity pair; and a relationship type estimation unit that estimates a type of relationship between entities of the entity pair based on the correlation values of the windows of the entity pair.

Abstract: A log labeling apparatus is configured to include a label importance DB and a similarity DB configured to store importance information between a plurality of labels and an action set and action set information identifying a first action set for calculating a second similarity with each label of a first log unit, a similarity calculation unit configured to calculate the second similarity with each label of the first log unit on the basis of the importance information, an action set of the first log unit, and the action set of the action set information, a post processor configured to detect label candidates, and an accumulation determination unit configured to determine a second action set for calculating a second similarity of a second log unit and to store action set information on the second action set in the similarity DB.

Abstract: A log labeling apparatus is configured to include a label importance DB and a similarity DB configured to store importance information between a plurality of labels and an action set and action set information identifying a first action set for calculating a second similarity with each label of a first log unit, a similarity calculation unit configured to calculate the second similarity with each label of the first log unit on the basis of the importance information, an action set of the first log unit, and the action set of the action set information, a post processor configured to detect label candidates, and an accumulation determination unit configured to determine a second action set for calculating a second similarity of a second log unit and to store action set information on the second action set in the similarity DB.