Integration and Exploration of Connected Personal Digital Traces

Kalokyri, Varvara; Borgida, Alexander; Marian, Amélie; Vianna, Daniela

doi:10.1145/3077331.3077337

Cited by 6 publications

(8 citation statements)

References 8 publications

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“…• Design an aggregate query model where groups of objects (traces) can be returned together as a query answer (e.g., all the social media messages and pictures relating to a party). For this we plan to integrate our work on the why dimension, which connects digital traces together [27,28] into our scoring framework.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, if a value could be inferred to the why dimension for the Facebook post in Figure 1 it could be used to connect this data to a possible message thread. In [27,28] we explored connections, in the form of plans, between events involving personal data traces; the plans, or tasks, connecting these events giving a contextual link as to why the corresponding digital traces were created/produced.…”

Section: W5h Data Modelmentioning

confidence: 99%

“…For now, we consider how as the type of information recorded, e.g., a Facebook comment. The why dimension is not explored in this paper; it is derived from inference and can be used to connect events [27,28].…”

Section: Classificationmentioning

confidence: 99%

“…Personal data is highly sensitive; consequently, privacy and ethical issues have to be considered while dealing with this type of information. The work discussed in this paper is developed as part of a series of tools to let user retrieve, store and organize their digital traces on their own devices (Kalokyri, Borgida, Marian, & Vianna, , ; Vianna, Yong, Xia, Marian, & Nguyen, ), guaranteeing some clear privacy and security benefits.…”

Section: Introductionmentioning

confidence: 99%

“…More elaborate scheme for preserving privacy in personal information management is discussed in [3]. The work discussed in this paper is developed as part of a series of tools to let user retrieve, store and organize their digital traces on their own devices [27,28,40], guaranteeing some clear privacy and security benefits.…”

Section: Introductionmentioning

confidence: 99%

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Searching heterogeneous personal digital traces

Vianna

Kalokyri

Borgida

et al. 2019

Proceedings of the Association for Information Science and Tech

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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.

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Section: Discussionmentioning

confidence: 99%

Section: W5h Data Modelmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%