Atomate it! End-user context-sensitive automation using heterogeneous information sources on the web
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Karger_Atomate it.pdf
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Author(s)
Van Kleek, Max G.
Moore, Brennan
Karger, David R.
Andre, Paul
schraefel, m. c.
Date Issued
April 2010
Journal
Proceedings of the 19th international conference on World wide web
Publisher
Association for Computing Machinery
Citation
Kleek, Max Van et al. “Atomate it! end-user context-sensitive automation using heterogeneous information sources on the web.” Proceedings of the 19th international conference on World wide web. Raleigh, North Carolina, USA: ACM, 2010. 951-960.
Version
Author's final manuscript
Abstract
The transition of personal information management (PIM) tools off the desktop to the Web presents an opportunity to augment these tools with capabilities provided by the wealth of real-time information readily available. In this paper, we describe a next-generation personal information assistance engine that lets end-users delegate to it various simple context- and activity-reactive tasks and reminders. Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their timevarying states and activities. Combined with other information sources on the web, including the user's online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions. Atomate's open architecture and world model easily accommodate new information sources and actions via the addition of feeds and web services. To make routine use of the system easy for non-programmers, Atomate provides a constrained-input natural language interface (CNLI) for behavior specification, and a direct-manipulation interface for inspecting and updating its world model.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1145/1772690.1772787
