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Which are the deployment options available for UiPath Orchestrator Standalone?
UiPath Orchestrator offers several deployment options to cater to different organizational needs for scalability, reliability, and disaster recovery. These options include single-node deployments for smaller or less critical environments, multi-node deployments for increased throughput and redundancy, and High Availability (HA) deployments that ensure the Orchestrator remains operational even in the event of failures. For organizations requiring robust disaster recovery solutions, UiPath provides options for High Availability with Disaster Recovery in both Active/Passive configurations, where one data center is on standby, and Two Active Data Centers, where both data centers are operational, providing seamless failover capabilities.
UiPath Orchestrator Guide: About Deployment Options
UiPath Orchestrator Guide: High Availability and Disaster Recovery
What a true fact regarding test cases in UiPath test manager?
What is the purpose of the "Resumed" process status in UiPath Action Center?
UiPath Action Center is a feature that enables human intervention in long-running unattended workflows. The ''Resumed'' process status indicates that the workflow execution has been resumed after a human input was provided through a task in Action Center. For example, a document understanding process may require a human to validate the extracted data from a document and then resume the automation. The ''Resumed'' status shows that the human validation action has been completed and the workflow is continuing with the next steps.Reference:
Action Center - Wait for Document Validation Action and Resume
What is the difference between rule-based and model-based data extraction methodologies?
Rule-based extraction is a technique that applies a set of predefined rules and patterns to extract data from a document. For example, you can use document templates, data position, occurrence patterns, or regular expressions to define the rules. Rule-based extraction is suitable for structured documents, such as forms, that have a fixed format and layout. However, rule-based extraction can be limited by the complexity and variability of the rules, and it may not be able to handle semi-structured or unstructured documents, such as invoices, contracts, or emails, that have different formats, layouts, or data types.
Model-based extraction is a technique that uses machine learning algorithms to automatically learn and extract data from a document. For example, you can use classification, clustering, or regression algorithms to train a model based on a set of labeled or unlabeled data. Model-based extraction is effective for semi-structured or unstructured documents, such as invoices, contracts, or emails, that have similar types of information but different formats, layouts, or data types. However, model-based extraction can require more computational resources and data preparation, and it may not be as accurate or consistent as rule-based extraction.
Document Processing with Improved Data Extraction | UiPath
Data Extraction Types & Techniques: A Complete Guide
NLP Methods' Information Extraction for Textual Data: An Analytical ...
What is the involvement of an RPA Solution Architect throughout the stages of an RPA project?
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