Compact Storage of Data Streams in Mobile Devices

Raes, Rémy; Ruas, Olivier; Luxey-Bitri, Adrien; Rouvoy, Romain

Type de document :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

Titre :

Compact Storage of Data Streams in Mobile Devices

Auteur(s) :

Raes, Rémy [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Ruas, Olivier [Auteur]
Luxey-Bitri, Adrien [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]

Self-adaptation for distributed services and large software systems [SPIRALS]

Titre de la manifestation scientifique :

DAIS'24 - 24th International Conference on Distributed Applications and Interoperable Systems

Ville :

Groningen

Pays :

Pays-Bas

Date de début de la manifestation scientifique :

2024-06-17

Éditeur :

LNCS

Mot(s)-clé(s) en anglais :

Mobile
Android
Storage
Time series
Pervasive
Ubiquitous
Data

Discipline(s) HAL :

Informatique [cs]

Résumé en anglais : [en]

Data streams produced by mobile devices, such as smartphones, offer highly valuable sources of information to build ubiquitous services. However, the diversity of embedded sensors and the resulting data deluge makes it ...
Lire la suite >Data streams produced by mobile devices, such as smartphones, offer highly valuable sources of information to build ubiquitous services. However, the diversity of embedded sensors and the resulting data deluge makes it impractical to provision such services directly on mobiles, due to their constrained storage capacity, communication bandwidth and processing power. Unfortunately, the improving hardware capabilities of devices are unlikely to resolve these structural issues. We, therefore, believe that mobile data management systems should, instead, handle data streams efficiently and compactly, to provision services directly at the edge, while accounting for the limits of existing assets and network infrastructures. This paper introduces the FLI framework, which leverages a piece-wise linear approximation technique to capture compact representations of data streams in mobile devices. Our experiments, performed on Android and iOS devices, show that FLI outperforms the state of the art both in memory footprint and I/O throughput. Our Flutter implementation of FLI can store stream datasets in mobile devices, which is a prerequisite to processing big data from ubiquitous devices in situ.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Fichiers

document
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FLI_TSDB.pdf
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Compact Storage of Data Streams in Mobile Devices

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Excel

RIS