Semantic Modeling and Inference with Episodic Organization for Managing Personal Digital Traces

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

doi:10.1007/978-3-319-69459-7_19

Cited by 6 publications

(9 citation statements)

References 9 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%

“…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: 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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“…Notice that the why dimension is not explored in this paper and only included for completeness, The why dimension is the topic of related work [11,12] that use inference to connect different fragments of data that derive from a common real-life task, or episode (e.g., all traces that stemmed from a restaurant outing).…”

Section: Frequency-based Featuresmentioning

confidence: 99%

A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces

Vianna¹,

Marian²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Personal digital traces are constantly produced by connected devices, internet services and interactions. These digital traces are typically small, heterogeneous and stored in various locations in the cloud or on local devices, making it a challenge for users to interact with and search their own data. By adopting a multidimensional data model based on the six natural questions -what, when, where, who, why and howto represent and unify heterogeneous personal digital traces, we propose a learning-to-rank approach using the state of the art LambdaMART algorithm and frequency-based features that leverage the correlation between content (what), users (who), time (when), location (where) and data source (how) to improve the accuracy of search results. Due to the lack of publicly available personal training data, a combination of known-item query generation techniques and an unsupervised ranking model (field-based BM25) is used to build our own training sets. Experiments performed over a publicly available email collection and a personal digital data trace collection from a real user show that the frequency-based learning approach improves search accuracy when compared with traditional search tools.

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“…Notice that the why dimension is not explored in this paper, but is the topic of related work [17,18]. This dimension can be derived by inference and could be used to connect different fragments of data that derive from a common real-life task, or episode.…”

Section: Frequency-based Featuresmentioning

confidence: 99%

A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces

Vianna¹,

Marian²

2020

Preprint

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Personal digital traces are constantly produced by connected devices, internet services and interactions. These digital traces are typically small, heterogeneous and stored in various locations in the cloud or on local devices, making it a challenge for users to interact with and search their own data. By adopting a multidimensional data model based on the six natural questions -what, when, where, who, why and how -to represent and unify heterogeneous personal digital traces, we can propose a learning-to-rank approach using the state of the art LambdaMART algorithm and frequencybased features that leverage the correlation between content (what), users (who), time (when), location (where) and data source (how) to improve the accuracy of search results. Due to the lack of publicly available personal training data, a combination of known-item query generation techniques and an unsupervised ranking model (field-based BM25) is used to build our own training sets. Experiments performed over a publicly available email collection and a personal digital data trace collection from a real user show that the frequency-based learning approach improves search accuracy when compared with traditional search tools.

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Semantic Modeling and Inference with Episodic Organization for Managing Personal Digital Traces

Cited by 6 publications

References 9 publications

Searching heterogeneous personal digital traces

Searching heterogeneous personal digital traces

A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces

A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces

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