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But does the enormous size of our temporal footprint mean that we can speak for human beings who are not yet alive?
The time series of events associated to a group (e.g., photo uploads) is the temporal footprint we aim to characterize.
Specifically, we describe groups with respect of the geographic scattering of their members, the temporal footprint of the members' activity in terms of dispersion, skeweness, and burstiness, and the tendency of the group to aggregate members on a topical or social basis.
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Regular customers are more predictable than changing customers since they can adopt a smaller range of temporal footprints.
Some of these applications are concerned with real-time data streams, while others are more focused on acquiring temporal footprints from historical data.
In particular, we define a new metric to account for geographic dispersion, we use a clustering approach on activity traces to extract classes of different temporal footprints, and we transpose the "common identity and common bond" theory into metrics to identify the skew of a group towards sociality or topicality.
We start by specifying how to compare the footprints by defining the collective temporal purchasing footprint (collective footprint in short) as: (Collective temporal purchasing footprint).
We name the new sequence temporal purchasing footprint sequence (footprint sequence in short, see Fig. 1- d)).
Thus, given a group G of similar units we define a temporal purchasing footprint F (footprints in short) as the representative of the group G.
We define the temporal purchasing footprint as follow: (Temporal purchasing footprint).
We define the temporal purchasing footprint sequence as: (Temporal purchasing footprint sequence).
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