Browsing by Author "Tommasini, Riccardo"

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    Big Data Analytics from the Rich Cloud to the Frugal Edge
    (IEEE, 2023) M. Awaysheh, Feras; Tommasini, Riccardo; Awad, Ahmed
    —Modern systems and applications generate and con sume an enormous amount of data from different sources, including mobile edge computing and IoT systems. Our ability to locate and analyze these massive amounts of data will shape the future, building next-generation Big Data Analytics (BDA) and artificial intelligence systems in critical domains. Traditionally, big data materialize in a centralized repository (e.g., the cloud) for running sophisticated analytics using decent computation. Nevertheless, many modern applications and critical domains require low-latency data analysis with the right decision at the right time standard for building trust. With the advent of edge computing, that traditional deployment model shifted closer to the data sources at the network’s edge. Such a shift was motivated by minimized latency, increased uptime, and enhanced efficiencies. This paper studies the BDA building blocks, analyzes the deployment requirements for edge-based BDA QoS, and drafts future trends. It also discusses critical open issues and further research directions for the next step of edge-based BDA.
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    D 2IA: User-defined interval analytics on distributed streams
    (ProQuest Central, 2022) Awad, Ahmed; Tommasini, Riccardo; Langhi, Samuele; Kamel, Mahmoud; Della Valle, Emanuele; Sakr, Sherif
    Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards being the ultimate framework for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). The former enables stateful computation on infinite sequences of data items while the latter focuses on the detection of events pattern. In most of the cases, data items and events are considered instantaneous, i.e., they are single time points in a discrete temporal domain. Nevertheless, a point-based time semantics does not satisfy the requirements of a number of use-cases. For instance, it is not possible to detect the interval during which the temperature increases until the temperature begins to decrease, nor for all the relations this interval subsumes. To tackle this challenge, we present D 2IA; a set of novel abstract operators to define analytics on user-defined event intervals based on raw events and to efficiently reason about temporal relationships between intervals and/or point events. We realize the implementation of the concepts of D 2IA on top of Flink, a distributed stream processing engine for big data.
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    D2IA: Stream Analytics on User-Defined Event Intervals
    (Springer Nature Switzerland AG, 2019) Awad, Ahmed; Tommasini, Riccardo; Kamel, Mahmoud; Della Valle, Emanuele; Sakr, Sherif
    Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards ultimate frameworks for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). The former enables stateful com putation on infinite sequences of data items while the latter focuses on the detection of events pattern. In most of the cases, data items and events are considered instantaneous, i.e., they are single time points in a discrete temporal domain. Nevertheless, a point-based time semantics does not satisfy the requirements of a number of use-cases. For instance, it is not possible to detect the interval during which the temperature increases until the temperature begins to decrease, nor all the relations this interval subsumes. To tackle this challenge, we present D2IA; a set of novel abstract operators to define analytics on user-defined event inter vals based on raw events and to efficiently reason about temporal rela tionships between intervals and/or point events. We realize the imple mentation of the concepts of D2IA on top of Esper, a centralized stream processing system, and Flink, a distributed stream processing engine for big data.