Architecture and challenges of maintaining a large-scale, context-aware human digital memory

Gurrin, Cathal; Byrne, Daragh; O’Connor, Noel E.; Jones, Gareth J. F.; Smeaton, Alan F.

doi:10.1049/cp:20080301

Cited by 18 publications

(13 citation statements)

References 20 publications

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“…Hence, in collaboration with cognitive neuropsychologists, a core set of baseline principles has been identified for a useful lifelog, based on the Cohen and Conway (2008) model of episodic memory. These principles, as described in Doherty et al (2012), in combination with our practical lifelogging experiences, Gurrin et al (2008a); Doherty et al (2013b), and can be summarised thus:…”

Section: Storage Models For Lifelog Datamentioning

confidence: 99%

LifeLogging: Personal Big Data

Gurrin

Smeaton

Doherty

2014

FNT in Information Retrieval

338

137

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Section: Storage Models For Lifelog Datamentioning

confidence: 99%

LifeLogging: Personal Big Data

Gurrin

Smeaton

Doherty

2014

FNT in Information Retrieval

338

137

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“…These logs are a digital representation of ourselves that evolve and grow alongside us and are a form of pervasive computing that consists of -a unified digital record of the totality of an individual's experiences, captured multimodally through digital sensors and stored permanently as a personal multimedia archive‖ [12]. In other words, they are a combination of many types of media, audio, video and images that have been recorded using a range of devices and sensors [13], [14]. As the IoT develops, the range of information that we have access to and can incorporate into our lifelogs is growing daily.…”

Section: B Digital Remembrancementioning

confidence: 99%

Digital Remembrance Based User Validation for Internet of Things [IoT]

Kowser¹

2016

IJECS

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Abstract-The increasing number of devices within the IoT is raising concerns over the efficiency and exploitability of existing authentication methods. The weaknesses of such methods, in particular passwords, are well documented. Although alternative methods have been proposed, they often rely on users being able to accurately recall complex and often unmemorable information. With the profusion of separate online accounts, this can often be a difficult task. The emerging digital remembrance concept involves the creation of a repository of remembrance specific to individuals. We believe this abundance of personal data can be utilized as a form of authentication. In this paper, we propose our digital remembrance based two-factor authentication mechanism, and also present our promising evaluation results.

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“…The 'alwayson' and pervasive nature of lifelogs leads to a high volume of data which presents several research challenges, such as efficient data storage, retrieval, annotation, visualisation, etc. Gurrin et al [13] proposed an architecture for maintaining, browsing and retrieving content from lifelog databases. Moreover, it has been recognised that the segmentation of lifelogs is essential for semantic extractions [9,10].…”

Section: Related Workmentioning

confidence: 99%

“…To date, much of the research has focused on harnessing lifelogs to enable people visualise, monitor and review their personal experiences [8,11,12,13,14]. When the personal lifelogs of users are available, a new category of real-time recommender systems, which are capable of generating recommendations at the right time and in the right way for a given user and context, are facilitated.…”

Section: Introductionmentioning

confidence: 99%

Towards Activity Recommendation from Lifelogs

Kumar

Jerbi

Gurrin

et al. 2014

Proceedings of the 16th International Conference on Information Integration and Web-Based Applications &Amp; Services

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With the increasing availability of passive, wearable sensor devices, digital lifelogs can now be captured for individuals. Lifelogs contain a digital trace of a person's life, and are characterised by large quantities of rich contextual data. In this paper, we propose a content-based recommender system to leverage such lifelogs to suggest activities to users. We model lifelogs as timelines of chronological sequences of activity objects, and describe a recommendation framework in which a two-level distance metric is proposed to measure the similarity between current and past timelines. An initial evaluation of our activity recommender performed using a real-world lifelog dataset demonstrates the utility of our approach.

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Architecture and challenges of maintaining a large-scale, context-aware human digital memory

Cited by 18 publications

References 20 publications

LifeLogging: Personal Big Data

LifeLogging: Personal Big Data

Digital Remembrance Based User Validation for Internet of Things [IoT]

Towards Activity Recommendation from Lifelogs

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