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Enabling Context-Aware Search using Extracted Insights from IoT Data Streams

Janieko, V., Rezvani, R., Pourshahrokhi, N., Enshaeifar, Shirin, Krogbæk, M., Christophersen, S.H, Elsaleh, Tarek and Barnaghi, Payam (2020) Enabling Context-Aware Search using Extracted Insights from IoT Data Streams In: Global IoT Summit 2020, 3-5 Jun 2020, Virtual Conference.

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The rapid growth in collecting and sharing sensory observation form the urban environments provides opportunities to plan and manage the services in the cities better and allows citizens to also observe and understand the changes in their surrounding in a better way. The new urban sensory data also creates opportunities for further application and service development by creative industries and start-ups. However, as the size and diversity of this data increase, finding and accessing the right set of data in a timely manner is becoming more challenging. This paper describes a search engine designed for indexing, searching and accessing urban sensory data. We present the key feature and architecture of the system and demonstrate some of the functionalities that are provided by searching for raw sensory observations and also pattern search functions that are enabled by a pattern analysis algorithm, supported by monitoring of data streams for changes in quality of information and remediation.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Janieko, V.
Rezvani, R.
Krogbæk, M.
Christophersen, S.H
Date : 15 April 2020
Funders : EU Horizon 2020
Copyright Disclaimer : © 2020 IEEE
Projects : IoT Crawler Project
Uncontrolled Keywords : Time-series data, pattern analysis, Internet of Things, information search and retrieval, stream data monitoring, missing data.
Depositing User : James Marshall
Date Deposited : 04 Jun 2020 10:16
Last Modified : 04 Jun 2020 10:16

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