Searching heterogeneous personal digital traces

Daniela Vianna, Varvara Kalokyri, Alexander Borgida, Amélie Marian, Thu Nguyen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or on local devices. Users have very few tools to organize, understand, and search the digital traces they produce. We propose a simple but flexible data model to aggregate, organize, and find personal information within a collection of a user's personal digital traces. Our model uses as basic dimensions the six questions: what, when, where, who, why, and how. These natural questions model universal aspects of a personal data collection and serve as unifying features of each personal data object, regardless of its source. We propose indexing and search techniques to aid users in searching for their past information in their unified personal digital data sets using our model. Experiments performed over real user data from a variety of data sources such as Facebook, Dropbox, and Gmail show that our approach significantly improves search accuracy when compared with traditional search tools.

Original languageEnglish (US)
Pages (from-to)276-285
Number of pages10
JournalProceedings of the Association for Information Science and Technology
Issue number1
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Library and Information Sciences


  • Data Model
  • Personal Digital Traces
  • Personal Search


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