Abstract: Systems and methods are provided for self-calibrating a health state of a hardware resource using a Siamese network based on a plurality of feature variables. The feature variables may include hardware failure data, performance degradation data, and power consumption data. The hardware failure data is based on machine operation records and warranty logs. The performance degradation data is based on hourly performance data and a number of client requests for performing functions. The power consumption data uses power telemetry and a processor (e.g., CPU) usage. The present disclosure uses a Siamese network with a plurality of trained neural networks in parallel to determine a correlation between incident data and reference data (e.g., representing a hardware resource in a healthy state). Use of the Siamese network enables self-calibrating a health status of servers in a cloud system without imposing stress tests or complex computations to classify the respective servers.

Abstract: Schema-less databases can make data modeling and data management difficult and can detrimentally affect integration with an RDBMS. Inferring a schema from a schema-less database can improve integration by indicating a structure or organization of data in the schema-less database. A schema analyzer can infer a schema by processing data of the schema-less database to identify statistically significant data fields. The schema analyzer then creates a schema that comprises the statistically significant data fields. A data modeler can use the resulting schema along with a schema for a RDBMS to generate a unified data model. A user may submit a query based on the unified data model to obtain results from both databases. The data modeler translates the query from the unified model to be compatible with each of the schemas so that data may be written to or retrieved from each of the schema-less database and the RDBMS.

Abstract: Schema-less databases can make data modeling and data management difficult and can detrimentally affect integration with an RDBMS. Inferring a schema from a schema-less database can improve integration by indicating a structure or organization of data in the schema-less database. A schema analyzer can infer a schema by processing data of the schema-less database to identify statistically significant data fields. The schema analyzer then creates a schema that comprises the statistically significant data fields. A data modeler can use the resulting schema along with a schema for a RDBMS to generate a unified data model. A user may submit a query based on the unified data model to obtain results from both databases. The data modeler translates the query from the unified model to be compatible with each of the schemas so that data may be written to or retrieved from each of the schema-less database and the RDBMS.