Abstract: A new ecosystem monitoring solution provides novel features including a dashboard service, a dashboard notifier, and a near real time query processor. The dashboard service can continuously aggregate, through the dashboard notifier and optionally dashboard agents, metadata from disparate ecosystem components of a complex computing platform or ecosystem. The metadata include metrics of crawling, data ingestion, and content enrichment activities and health information of the disparate ecosystem components. The metrics are processed with respect to a time window utilizing an expression tree dynamically constructed by the query processor. The query processor can navigate the expression tree to form collection models. Each collection has aggregation functions for aggregating a set of metrics specified in a view model.

Abstract: A new ecosystem monitoring solution provides novel features including a dashboard service, a dashboard notifier, and a near real time query processor. The dashboard service can continuously aggregate, through the dashboard notifier and optionally dashboard agents, metadata from disparate ecosystem components of a complex computing platform or ecosystem. The metadata include metrics of crawling, data ingestion, and content enrichment activities and health information of the disparate ecosystem components. The metrics are processed with respect to a time window utilizing an expression tree dynamically constructed by the query processor. The query processor can navigate the expression tree to form collection models. Each collection has aggregation functions for aggregating a set of metrics specified in a view model.

Abstract: A classification system is provided that separates unclassified pages into unclassified, separated documents and classifies the separated documents. The classification system applies a page-level recognition model to the unclassified pages to recognize the logical boundaries between documents and, based on the logical boundaries, separates the pages into unclassified, separated documents. The classification system further applies a document-level recognition model to classify the separated documents.

Abstract: A classification system is provided that separates unclassified pages into unclassified, separated documents and classifies the separated documents. The classification system applies a page-level recognition model to the unclassified pages to recognize the logical boundaries between documents and, based on the logical boundaries, separates the pages into unclassified, separated documents. The classification system further applies a document-level recognition model to classify the separated documents.

Abstract: A classification system is provided that separates unclassified pages into unclassified, separated documents and classifies the separated documents. The classification system applies a page-level recognition model to the unclassified pages to recognize the logical boundaries between documents and, based on the logical boundaries, separates the pages into unclassified, separated documents. The classification system further applies a document-level recognition model to classify the separated documents.

Abstract: Provided herein are systems and methods for user-defined automated document feature modeling, extraction and optimization. In the present disclosure, an end user of an automated document review system can customize and create new data models applicable to a set of focus documents. In addition, an end user of the automated document review system can customize and create new extraction rules applicable to text extraction from the set of focus documents. The user-defined edits to the data model and extraction rules can be further tested in a staging environment, and tested against a ground truth set of documents, before being widely applied to other relevant documents.

Abstract: A classification system is provided that separates unclassified pages into unclassified, separated documents and classifies the separated documents. The classification system applies a page-level recognition model to the unclassified pages to recognize the logical boundaries between documents and, based on the logical boundaries, separates the pages into unclassified, separated documents. The classification system further applies a document-level recognition model to classify the separated documents.

Abstract: A classification system is provided that separates unclassified pages into unclassified, separated documents and classifies the separated documents. The classification system applies a page-level recognition model to the unclassified pages to recognize the logical boundaries between documents and, based on the logical boundaries, separates the pages into unclassified, separated documents. The classification system further applies a document-level recognition model to classify the separated documents.

Abstract: Provided herein are systems and methods for user-defined automated document feature modeling, extraction and optimization. In the present disclosure, an end user of an automated document review system can customize and create new extractor taggers within data models applicable to a set of focus documents. The user-defined edits to the extractor taggers can be further tested in a staging environment, and tested against a ground truth set of documents, before being widely applied to other relevant documents.

Abstract: A new ecosystem monitoring solution provides novel features including a dashboard service, a dashboard notifier, a near real time query processor. The dashboard service can continuously aggregate, through the dashboard notifier and optionally dashboard agents, metadata from disparate ecosystem components of a complex computing platform or ecosystem. The metadata include metrics of crawling, data ingestion, and content enrichment activities and health information of the disparate ecosystem components. The metrics are processed with respect to a time window utilizing an expression tree dynamically constructed by the query processor. The query processor can navigate the expression tree to form collection models. Each collection has aggregation functions for aggregating a set of metrics specified in a view model.

Abstract: Provided herein are systems and methods for user-defined automated document feature modeling, extraction and optimization. In the present disclosure, an end user of an automated document review system can customize and create new data models applicable to a set of focus documents. In addition, an end user of the automated document review system can customize and create new extraction rules applicable to text extraction from the set of focus documents. The user-defined edits to the data model and extraction rules can be further tested in a staging environment, and tested against a ground truth set of documents, before being widely applied to other relevant documents.

Abstract: Provided herein are systems and methods for user-defined automated document feature modeling, extraction and optimization. In the present disclosure, an end user of an automated document review system can customize and create new extractor taggers within data models applicable to a set of focus documents. The user-defined edits to the extractor taggers can be further tested in a staging environment, and tested against a ground truth set of documents, before being widely applied to other relevant documents.