Abstract: The disclosure generally describes methods, software, and systems, including a method for machine learning anomaly detection for a set of assets. Assets are analyzed using anomaly-detection analysis and a set of anomaly-detection rules. Each asset is associated with correlated records comprising characteristics of the particular asset and characteristic of non-asset-specific signals. Each anomaly-detection rule is associated with conditions determined to be indicative of a potential anomaly. At least a subset of the assets are provided for presentation in a user interface. Each asset is identified as being in a potential anomalous or non-anomalous state based on the anomaly-detection analysis. Input is received from a user identifying at least one asset as anomalous as a non-anomalous asset. Based on the received input, at least one anomaly-detection rule is modified that was applied to identify the asset as anomalous. The modified rule is stored for future analyses.

Abstract: The disclosure generally describes methods, software, and systems, including a method for providing a suggested view of asset information for presentation. A set of correlated records is identified for a plurality of assets. The set of correlated records includes a correlated set of at least one characteristic of a particular asset and a characteristic of the non-asset-specific signals. The set of correlated records is analyzed to identify a set of anomaly-detection rules. In a presentation of at least a subset of the assets, an indication of assets associated with a potential anomaly identified. A suggested view is identified based on the potential anomaly and at least one characteristic/signal associated with the determination that the potential anomaly exists. The suggested view is provided for presentation in a user interface.

Abstract: A system includes determination of a first measure value associated with a first physical space and a first time period within the analytical data, dynamic determination of a time-dependent association between a first entity or event and the first physical, dynamic mapping of the first measure value to the first entity or event based on the time-dependent association, and presentation of the first measure value in association with the first entity or event.

Abstract: The disclosure generally describes methods, software, and systems, including a method for data collection and correlation of information for a set of assets. A set of asset-specific signals is obtained for a plurality of assets. Each asset has a set of characteristics defining the asset. A set of non-asset-specific signals is obtained. At least a portion of the set of non-asset-specific signals is correlated to at least some of the plurality of assets based on a determined correlation between at least one characteristic of a particular asset and a characteristic of the non-asset-specific signals. For each asset, a correlated record of the particular asset comprising the correlated characteristics is stored.

Abstract: The disclosure generally describes methods, software, and systems, including a method for providing a suggested view of asset information for presentation. A set of correlated records is identified for a plurality of assets. The set of correlated records includes a correlated set of at least one characteristic of a particular asset and a characteristic of the non-asset-specific signals. The set of correlated records is analyzed to identify a set of anomaly-detection rules. In a presentation of at least a subset of the assets, an indication of assets associated with a potential anomaly identified. A suggested view is identified based on the potential anomaly and at least one characteristic/signal associated with the determination that the potential anomaly exists. The suggested view is provided for presentation in a user interface.

Abstract: The disclosure generally describes methods, software, and systems, including a method for machine learning anomaly detection for a set of assets. Assets are analyzed using anomaly-detection analysis and a set of anomaly-detection rules. Each asset is associated with correlated records comprising characteristics of the particular asset and characteristic of non-asset-specific signals. Each anomaly-detection rule is associated with conditions determined to be indicative of a potential anomaly. At least a subset of the assets are provided for presentation in a user interface. Each asset is identified as being in a potential anomalous or non-anomalous state based on the anomaly-detection analysis. Input is received from a user identifying at least one asset as anomalous as a non-anomalous asset. Based on the received input, at least one anomaly-detection rule is modified that was applied to identify the asset as anomalous. The modified rule is stored for future analyses.

Abstract: A system includes determination of a first measure value associated with a first physical space and a first time period within the analytical data, dynamic determination of a time-dependent association between a first entity or event and the first physical, dynamic mapping of the first measure value to the first entity or event based on the time-dependent association, and presentation of the first measure value in association with the first entity or event.

Abstract: Various implementations for session coupling in a distributed computing system are provided. In one implementation, a first server entity receives a transmission having an identifiable user context from a client entity. In response, the first server entity creates a first session that is related to the identifiable user context. A second server entity receives a transmission related to the first session from the first server entity. In response, the second server entity creates a second session that relates to the identifiable user context. The first and second sessions are coupled to create a common virtual session. The common virtual session is then bound to the identifiable user context so that the first and second server entities recognize that subsequent transmissions from the client entity relate to the common virtual session.

Abstract: Various implementations for session coupling in a distributed computing system are provided. In one implementation, a first server entity receives a transmission having an identifiable user context from a client entity. In response, the first server entity creates a first session that is related to the identifiable user context. A second server entity receives a transmission related to the first session from the first server entity. In response, the second server entity creates a second session that relates to the identifiable user context. The first and second sessions are coupled to create a common virtual session. The common virtual session is then bound to the identifiable user context so that the first and second server entities recognize that subsequent transmissions from the client entity relate to the common virtual session.