Abstract: A request is received to perform an exploration in a predictive and maintenance service application. A sequence of explorations is added in an evidence package. The evidence package includes the list of micro services. The sequence of explorations in the evidence package are analyzed. Based on the analysis, a user behavior corresponding to the performed exploration is identified. The user behavior is provided as input to the machine learning algorithm. Configuration data corresponding to the exploration and the evidence package is stored in a configuration database. The machine learning algorithm is executed in an execution engine. The execution engine is a micro service. Based on execution of the machine learning algorithm, the list of micro services is automatically identified as recommendations. The list of micro services is displayed as recommendations in the predictive and maintenance service application.

Abstract: Structured exploration of available data provides insight allowing early detection/analysis of emerging issues. An in-memory database engine applies specialized information sources and filter criteria to an original data set to successively produce various exploration levels. Evidence relating to a particular exploration level (e.g., resulting data subset, metadata indicating chains of information sources/filter criteria) are stored at the user's instruction within an evidence package of the in-memory database. Information sources may be licensed from third parties, and may be present in the in-memory database. To improve computer performance, embodiments may delete results of previous exploration levels outside of the evidence package. Evidence from the evidence package may be displayed to afford insight into relationships between data subsets and an emerging issue. One structured exploration references a source of geographic information (e.g.

Abstract: Structured exploration of available data provides insight allowing early detection/analysis of emerging issues. An in-memory database engine applies specialized information sources and filter criteria to an original data set to successively produce various exploration levels. Evidence relating to a particular exploration level (e.g., resulting data subset, metadata indicating chains of information sources/filter criteria) are stored at the user's instruction within an evidence package of the in-memory database. Information sources may be licensed from third parties, and may be present in the in-memory database. To improve computer performance, embodiments may delete results of previous exploration levels outside of the evidence package. Evidence from the evidence package may be displayed to afford insight into relationships between data subsets and an emerging issue. One structured exploration references a source of geographic information (e.g.

Abstract: A request is received to perform an exploration in a predictive and maintenance service application. A sequence of explorations is added in an evidence package. The evidence package includes the list of micro services. The sequence of explorations in the evidence package are analyzed. Based on the analysis, a user behavior corresponding to the performed exploration is identified. The user behavior is provided as input to the machine learning algorithm. Configuration data corresponding to the exploration and the evidence package is stored in a configuration database. The machine learning algorithm is executed in an execution engine. The execution engine is a micro service. Based on execution of the machine learning algorithm, the list of micro services is automatically identified as recommendations. The list of micro services is displayed as recommendations in the predictive and maintenance service application.