ICITR - 2020

Permanent URI for this collectionhttp://192.248.9.226/handle/123/16316

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Browsing ICITR - 2020 by Author "Bandara, HMND"

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    An automated decision-making framework for precipitation-related workflows
    (Faculty of Information Technology, University of Moratuwa., 2020-12) Adikari, AMH; Bandara, HMND; Herath, S; Chitraranjan, C; Karunananda, AS; Talagala, PD
    Due to weather’s chaotic nature, static workflow managers are ineffective in integrating multiple Numerical Weather Models (NWMs) with cascading relationships. Unexpected events like flash floods and breakdown in canal water control systems or reservoirs make decision-making in workflow management further complicated. To enable dynamic decision-making, we need to update part or entire workflow, terminate unfitting NWM executions, and trigger parallel NWM workflows based on recent results from NWMs and observed conditions. Most of the existing weather-related decision support systems cannot trigger or create workflows dynamically. They are also designed for specific geography or functionality, making it challenging to customize for regions with different weather patterns. In this paper, we present an automated decision-making framework for precipitation-related workflows. The proposed framework can manage complex weather-related workflows dynamically in response to varying weather conditions, automatically control and monitor those workflows, and update workflow paths in response to unexpected weather events. Using significant flood-related datasets from the Colombo catchment area, we demonstrate that the proposed framework can achieve 100% accuracy in dynamic workflow generation and path updates compared to manual workflow controlling. Also, we demonstrate that unexpected event identification and pumping station controlling workflow triggers could be improved with advance rule sets.
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    Evaluation of re-identification risks in data anonymization techniques based on population uniqueness
    (Faculty of Information Technology, University of Moratuwa., 2020-12) Bandara, PLMK; Bandara, HMND; Fernando, S; Karunananda, AS; Talagala, PD
    With the increasing appetite for publicly available personal data for various analytics and decision making, due care must be taken to preserve the privacy of data subjects before any disclosure of data. Though many data anonymization techniques are available, there is no holistic understanding of their risk of re-identification and the conditions under which they could be applied. Therefore, it is imperative to study the risk of re-identification of anonymization techniques across different types of datasets. In this paper, we assess the re-identification risk of four popular anonymization techniques against four different datasets. We use population uniqueness to evaluate the risk of re-identification. As per the analysis, k-anonymity shows the lowest re-identification risk for unbiased samples of the population datasets. Moreover, our findings also emphasize that the risk assessment methodology should depend on the chosen dataset. Furthermore, for the datasets with higher linkability, the risk of re-identification measured using the uniqueness is much lower than the real risk of re-identification.