Inter-application Workflow Performance Analytics Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computing environments lack a mechanism to automatically measure the performance of workflows that involve interactions across multiple applications, leading to inefficiencies and burdens on users in highly collaborative environments.
Innovation Solution
The implementation of inter-application workflow performance analytics techniques, which link content object interactions across multiple applications to generate performance measurements, utilizing an inter-application analytics engine and data management framework to record, analyze, and present workflow performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual logging of inter-application interactions is implemented, then workflow performance measurement capability is improved, but user burden and operational complexity increase
Solution Approach 1:
The system automatically monitors and records inter-application interactions without requiring user intervention. The analytics engine self-services by collecting data from application events, processing metrics, and generating reports autonomously, eliminating the manual logging burden on users while maintaining comprehensive measurement capability
Solution Approach 2:
An analytics engine is introduced as an intermediary component between applications and users. This mediator automatically captures interaction data, processes workflow metrics, and presents performance measurements, shielding users from the complexity of data collection and analysis while enabling precise workflow performance measurement
2Productivity
If automatic monitoring of inter-application interactions is implemented, then productivity is improved by reducing manual logging, but device complexity increases
Solution Approach 1:
The analytics engine serves multiple functions within a single system component: it collects interaction data from various applications, processes multiple types of workflow metrics, generates performance reports, and provides data for analysis. This multi-functionality improves productivity through automatic monitoring while containing system complexity by consolidating capabilities in one universal component
Solution Approach 2:
The monitoring system is segmented into distinct functional modules: data collection from application events, metric processing, report generation, and analysis components. This segmentation allows each module to handle specific tasks independently, improving overall productivity through automated workflow while managing complexity by dividing the system into manageable, specialized segments
Data Source
AI summary
Methods, systems and computer program products for shared content management systems that provide performance analytics pertaining to a project. Embodiments include establishing one or more network communication links between a content management system that manages a plurality of shared content objects and a plurality of applications that cause modifications to the shared content objects in accordance with workflows of the project. Iteraction events that correspond to modifications over the shared content objects are recorded such that interaction events associated with the plurality of applications are selected based at least in part on attributes associated with the interaction events. Relationships between the recorded interaction events such as time durations between certain of the interaction events are calculated. Project performance measurements are generated based on the calculations and/or based on other relationships between the interaction events. The calculations may span across many different applications and/or many different departments and/or many different enterprises.