Taskexplorer software#

In our research, we are working to demonstrate that our system can help people find their task-related information faster, recover faster and better from interruptions, and maintain better awareness of personal and workgroup tasks. We believe that a database of past user actions on the desktop, segmented by user task, can enable substantial productivity- enhancing features when combined with machine learning.

Taskexplorer software#

By building software that is aware of those tasks, we can greatly decrease costs, errors, and frustration. The core TaskTracer hypothesis is that people naturally want to organize their information, regardless of data type or storage location, into units that they think of as projects or activities or tasks. TaskTracer is a research project at Oregon State University to identify innovative technological solutions to information overload and task management at the personal and workgroup levels. Two analyses carried out on data collected during experiments enabled us to work out methods that can be used for teaching by automatic personalization of human-computer interaction. Algorithmic methods of artificial intelligence are also classified by a typology of data processing. This has enabled us to develop a platform which allows the implementation of methods for machine learning and describes ways for adapting the interaction automatically to provide personalized help. As this work must be applicable in a scholastic context, we have studied various teaching theories elaborated by cognitivists. Two algorithms are introduced to facilitate the reading of this representation. Application of the data-visualization tool permits us to replace the global interaction of the user in context as well as comparing the activity of several users on one or more interfaces. We also present tools for analysis which allow automatic recognition of behavior characteristics of users, as well as a tool for data visualization. This formalization allows all modeling, and all applications of all the methods previously exposed. We propose a new generic formalization of data harvesting, designed for data derived from human-computer interaction. After having studied the properties necessary to design a generic system to gather data from human-computer interaction, we present methods of data visualization for the purpose of identifying useful properties, as well as examples of automatic analyses by algorithmic methods. Human-computer interactions can be studied under several angles: some analyses attempt to describe human behavior using the computer as a tool, others seek to optimize the ergonomics of the interface, and finally, others seek to improve the interactivity between the user and the immense quantity of information conveyed by the numerical device. Results from each study also shed more light on the nature of mixed artifact task contexts. We analyzed task contexts from hundreds of days of work from three users and found similar trends of information decaying from contexts. The second study is a case study of the use of contexts to support the operations of a software development company. We found that these knowledge workers need information to decay from their context and that our model is adequate at automatically trimming contexts. The first study we describe is a field study of eight knowledge workers who used the model in their daily work for up to 25 days on tasks involving both file and web documents. In this paper, we describe the results of two studies of the use of captured contexts for a broad range of knowledge workers. Captured contexts facilitate task switches and reduce information overload by enabling the display of only the information relevant to the task-at-hand. In earlier work, we have shown the benefits of automatically capturing contexts for tasks for a specific category of knowledge worker, software programmers.

Knowledge workers work on many different tasks and must often switch between those tasks.