HASLab - Indexed Articles in Conferences
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ItemTotally-Ordered Prefix Parallel Snapshot Isolation( 2021)Distributed data management systems have increasingly been using variants of Snapshot Isolation (SI) as their transactional isolation criteria as it combines strong ACID guarantees with non-blocking reads and scalability. However, most existing proposals are limited by the performance of update propagation and stability detection, in particular, when execution and storage are disaggregated. In this paper, we propose TOPSI, an approach providing a restricted form of Parallel Snapshot Isolation (PSI) that allows partially ordering recent transactions to avoid waiting for remote updates or using a stale snapshot. Moreover, it has the interesting property of making a prefix of history in all sites converge to a common total order. This allows versions to be represented by a single scalar timestamp for certification and storage in a shared store. We demonstrate the impact on throughput and abort rate with a proof-of-concept implementation and the industry-standard TPC-C benchmark. © 2021 ACM.
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ItemAssessment of an IoT platform for data collection and analysis for medical sensors( 2018)Health facilities produce an increasing and vast amount of data that must be efficiently analyzed. New approaches for healthcare monitoring are being developed every day and the Internet of Things (IoT) came to fill the still existing void on real-time monitoring. A new generation of mechanisms and techniques are being used to facilitate the practice of medicine, promoting faster diagnosis and prevention of diseases. We proposed a system that relies on IoT for storing and monitoring medical sensors data with analytic capabilities. To this end, we chose two approaches for storing this data which were thoroughly evaluated. Apache HBase presents a higher rate of data ingestion, when collaborating with the Kaa IoT platform, than Apache Cassandra, exhibiting good performance storing unstructured data, as presented in a healthcare environment. The outcome of this system has shown the possibility of a large number of medical sensors being simultaneously connected to the same platform (6000 records sent by the second or 48 ECG sensors with a frequency of 125Hz). The results presented in this paper are promising and should be further investigated as a comprehensive system would benefit the patient's diagnosis but also the physicians. © 2018 IEEE.
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ItemAssessment of an IoT platform for data collection and analysis for medical sensors( 2018)Health facilities produce an increasing and vast amount of data that must be efficiently analyzed. New approaches for healthcare monitoring are being developed every day and the Internet of Things (IoT) came to fill the still existing void on real-time monitoring. A new generation of mechanisms and techniques are being used to facilitate the practice of medicine, promoting faster diagnosis and prevention of diseases. We proposed a system that relies on IoT for storing and monitoring medical sensors data with analytic capabilities. To this end, we chose two approaches for storing this data which were thoroughly evaluated. Apache HBase presents a higher rate of data ingestion, when collaborating with the Kaa IoT platform, than Apache Cassandra, exhibiting good performance storing unstructured data, as presented in a healthcare environment. The outcome of this system has shown the possibility of a large number of medical sensors being simultaneously connected to the same platform (6000 records sent by the second or 48 ECG sensors with a frequency of 125Hz). The results presented in this paper are promising and should be further investigated as a comprehensive system would benefit the patient's diagnosis but also the physicians. © 2018 IEEE.