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An Enhanced Predictive and Analytics Model for Collaboration

IP.com Disclosure Number: IPCOM000244015D
Publication Date: 2015-Nov-05
Document File: 3 page(s) / 98K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method to accurately project a project’s resource workload based on the data collected from different social networking channels used by team members in various project roles. The novel idea is to apply social media collaboration experiences to the project’s time and resource management.

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An Enhanced Predictive and Analytics Model for Collaboration

A successful project, from development through execution, depends on good collaboration and communication practices, especially for key aspects of the project. For example, time allocation is an important metric for resource planning, and time tracking is important to project planning and execution. This information needs to be tracked and shared. Thus, developers and team members spend a lot of time in collaborative activities such as meetings, conference calls, presentations (and preparing for said presentations), training, etc. A project manager or team can

manually collect and aggregate information about the time and resources contributed to the project goals and then use that information for tracking the project schedule and resources as well as for further project planning.

An efficient method is needed for tracking the timing of various project-related activities across all collaboration space and channels and with consideration of the users' profiles. The goal is to improve project planning efficiency and both team and personal time management.

Proposed herein is a method for collecting timing related information from social and collaboration platforms and tools in relation to the person's role in the project. The novel idea is to apply social media collaboration experiences to the project's time and resource management. The novel method focuses on accurately projecting the resource workload based on the data collected from different social networking channels used by team members in various project roles.

The key difference between this approach and available solutions is that this approach uses different data sources; the user does not need to go to the Enterprise Resource Planning (ERP) tools to enter the static data, the method learns from users' experience feedback and project targets. The proposed method is not a deterministic and rule-based method, but is a cognitive time tracking collaboration project planning tool. It is based on a time management model that is defined as time vectors across various project resources and dimensions.

The method relies on data mining and social media properties extraction. Collected timing, such as time spent on meetings, unified communications sessions (e.g., web

conferencing, instant messaging, etc.), presentations, social collaboration and networking sites, visits to customer sites, number of times this person updated the project WIKI page (and the size of the documentation delivered), etc. is mapped to the person's role in the project and within the project context.

The proposed model can be applied to predict an outcome based on social activities and collaboration from various domains, especially the crowdsourcing based projects for which communication between crowdsourcers is the key feature.

This solution is based on building a time management model that trains on available project data and metrics and then learns...