Please use this identifier to cite or link to this item: http://13.232.72.61:8080/jspui/handle/123456789/2377
Title: G-SPAMINE : An Approach to Discover Temporal Association Patterns and Trends in Internet of Things
Authors: Aljawarneh, Shadi
Vangipuram, Radhakrishna
Puligadda, Veeresh Kumar
Vinjamuri, Janaki
Keywords: Computer Science
Temporal Data
Temporal Trend
Web of Things
Issue Date: 2017
Publisher: ElsevIer.
Citation: Aljawarneh, Shadi., Vangipuram, Radhakrishna., Puligadda, Veeresh Kumar., & Vinjamuri, Janaki. (2017). G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things. Future generation computer systems, 74, 430-443.
Abstract: Temporal data is one of the most common form of data in internet of things. Data from various sources such as sensors, smart phones, smart homes and smart vehicles in near future shall be of temporal nature with generated information recorded at different timestamps. We call all such data as time stamped temporal data. Discovery of temporal patterns and temporal trends from such temporal data requires new algorithms and methodologies as most of the existing algorithms do not reveal emerging, seasonal and diminishing patterns. In this paper, the objective is to find temporal patterns whose true prevalence values vary similar to a reference support time sequence satisfying subset constraints through estimating temporal pattern support bounds and using a novel fuzzy dissimilarity measure. We name our approach as G-SPAMINE. Experiment results show that G-SPAMINE out performs naive and sequential approaches and comparatively better to or atleast same as SPAMINE. In addition, the stamped temporal data adds extra level of privacy for temporal patterns in the IoT.
URI: http://13.232.72.61:8080/jspui/handle/123456789/2377
Appears in Collections:Faculty Publications



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