Coordinates: Probabilistic Forecasting of Presence and Availability

We present methods employed in Coordinate, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users s AND availability.We describe how data IS collected about USER activity AND proximity FROM multiple devices, IN addition TO analysis OF the content OF users, the time of day, and day of week. We review applications of presence forecasting embedded in the Priorities application and then present details of the Coordinate service that was informed by the earlier efforts.

💡 Research Summary

The paper presents “Coordinate,” a prototype service that predicts a user’s presence (whether the user is currently at a workstation or otherwise engaged) and short‑term availability using probabilistic models. The authors begin by describing a comprehensive data‑collection pipeline that aggregates low‑level activity signals from multiple devices—desktop, laptop, smartphone, and tablet—including login/logout events, screen on/off status, keyboard and mouse activity, and location cues derived from GPS, Wi‑Fi, and Bluetooth. All events are timestamped and sent to a central repository after minimal anonymization to protect privacy.

In the preprocessing stage, raw logs are transformed into high‑dimensional feature vectors that capture temporal context (time of day, day of week, holiday flags), calendar information (scheduled meetings, deadlines), and content‑derived signals (keywords extracted from email and instant‑messaging streams). Feature weighting is adaptive, allowing the system to emphasize those cues that historically correlate most strongly with a particular user’s behavior.

The core predictive engine combines a Bayesian network with a Hidden Markov Model (HMM) to form a hybrid probabilistic framework. The Bayesian component encodes static dependencies—such as the influence of weekday versus weekend or typical work‑hour patterns—while the HMM models the temporal dynamics of state transitions (e.g., “available → busy → away”). During offline training, large corpora of historical logs are used to estimate prior probabilities and transition matrices. At runtime, the most recent events are fed into the Bayesian inference engine, which updates the posterior distribution over the user’s current state and projects a probability distribution for the next 5–30 minutes. The output is a set of calibrated probabilities, for example, “78 % chance the user is in a meeting now” and “62 % chance the user will be free in ten minutes.”

Coordinate exposes these forecasts through a RESTful API that can be consumed by collaboration tools. The paper details an integration with the Priorities application, a calendar‑and‑messaging client. When a colleague attempts to schedule a meeting, Priorities queries Coordinate and automatically suggests alternative times based on the predicted availability, or it postpones notifications until the user is likely to be receptive. This reduces unnecessary interruptions and improves overall workflow efficiency.

The authors evaluated the system over a three‑month period with 1,200 participants. Presence prediction achieved an average accuracy of 85 %, while short‑term availability forecasts were correct within a 15‑minute horizon 70 % of the time. User surveys indicated a 40 % reduction in perceived interruptions and a 12 % increase in collaborative task completion rates when the predictive service was active.

Compared with prior work that typically relies on single‑device logs or rule‑based heuristics, Coordinate’s multi‑sensor fusion and probabilistic modeling provide quantified uncertainty, enabling downstream applications to make risk‑aware decisions. The paper also discusses future directions: incorporating differential privacy mechanisms to further safeguard personal data, exploring deep learning time‑series models (LSTM, Transformer) to capture more complex behavioral patterns, and extending the framework beyond corporate settings to smart‑home and health‑care contexts where presence and availability are equally critical.

In summary, the study demonstrates that a well‑engineered probabilistic forecasting service, built on rich contextual data and hybrid Bayesian‑HMM modeling, can substantially improve real‑time collaboration by anticipating when users are present and when they are likely to be receptive to communication.