Zeitgeist

Zeitgeist

Zeitgeist – The Element of Awareness

Zeitgeist is a service, which supports acquiring, storing, processing, and providing events. As a computer user, you might want to know, which songs you listened to, while working on your project report last week. Maybe, you also want to know, what you did with Dave last week. Other use cases include answering the questions “What did I do at last year’s GUADEC?” and “Which documents are related to my project report?”. Well, you might wonder, if relating documents isn’t the domain of semantic technology. That’s somewhat true: Semantic technologies relate documents and other items by their rather static properties, such as content. But these properties don’t reflect a user’s personal relationship to the documents in question.

Personal Semantic Technology

While semantic technology helps to model and provide meaning of computer-represented objects independent of specific users, the Zeitgeist approach personalizes semantic technology. Personal semantic technology reflects the personal meaning of computer-represented objects, as emerging and developing from them being involved in a user’s activities and experiences. Therefore, Zeitgeist adds a time-bound layer to the experience of using computers. Users are thereby enabled to relate to and situate themselves within representations of their past, current, and future activities and experiences.

Zeitgeist to the Rescue

The above introduced user questions can best be answered by considering a consolidation of user-related events, as realized by Zeitgeist. Events model time-bound indicators of the user’s doing and of what’s happening or of what the user experiences of what happens. They can be understood as the computer-experienced “percepts” of a person’s past (up to “now”) acting and experiencing. Events can also be placed in the future, but this story will not yet be told.

Features

  • Zeitgeist currently logs file usage, web activity, plus chat and email conversations. More to come.
  • Zeitgeist allows any application to store this information and makes it readily available over a DBus API.
  • Zeitgeist figures out, which are a user’s most used items, not only in general, but also applying time scoping as in “What was most relevant to me, while I was working on project X, for a month last year?”.
  • Using machine-learning algorithms, Zeitgeist can establish relationships between items based on similarity and usage patterns.
  • Zeitgeist is light-weight and supports extensions to enhance its engine’s core feature set.
  • Extensions (or as we call them Smack-ins) reside within the same process as the engine’s core logic. They can be used to include information about activity and experience beyond the desktop, such as geo-logging and geo-tagging.

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