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Small Data: Applications and Architecture

Authors:
Cheng-Kang Hsieh
Faisal Alquaddoomi
Fabian Okeke
John P. Pollak
Lucky Gunasekara
Deborah Estrin

Keywords: small data; linked data; knowledge representation

Abstract:
Small data are the digital traces that individuals generate as a byproduct of their daily activities, such as: communicating through email or text; buying groceries or ordering delivery; or going to work on foot or by car. These traces can empower individuals to gain insights into their behavior, personalize their care, improve their relationships, motivate achievement of goals, and broadly improve their quality of life. As such small data are both byproducts of today's and drivers of tomorrow's ubiquitous computing applications. The contributions of this paper are twofold: we motivate the requirements for a small data ecosystem and supporting architecture, and present a critical component -- Lifestreams Database (DB) -- which is evaluated using three exemplar apps. Lifestreams DB extracts, processes, and models diverse traces from data silos and enables various small data applications through simple SPARQL queries. Its soft-state design provides storage-efficiency, robustness, and query performance for processing small data.

Pages: 1 to 10

Copyright: Copyright (c) IARIA, 2018

Publication date: April 22, 2018

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-631-6

Location: Athens, Greece

Dates: from April 22, 2018 to April 26, 2018