Managing Heterogeneous Data on a Big Data Platform: A Multi-criteria Decision Making Model for Data-Intensive Science

Pal, Gautam; Atkinson, Katie; Li, Gangmin

doi:10.1109/bigcomp48618.2020.00-69

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Similarly, (12) used another tool called Apache Flume for data collection, representing the data ingestion phase. Another innovative framework presented by (13) uses two tools, Apache Kafka and Apache Flume, as an ingestion data layer to address the challenges of data heterogeneity and velocity.…”

Section: Types and Classificationmentioning

confidence: 99%

Overview on Data Ingestion and Schema Matching

El Haddadi,

Chevalier,

Dousset

et al. 2024

Data and Metadata

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This overview traced the evolution of data management, transitioning from traditional ETL processes to addressing contemporary challenges in Big Data, with a particular emphasis on data ingestion and schema matching. It explored the classification of data ingestion into batch, real-time, and hybrid processing, underscoring the challenges associated with data quality and heterogeneity. Central to the discussion was the role of schema mapping in data alignment, proving indispensable for linking diverse data sources. Recent advancements, notably the adoption of machine learning techniques, were significantly reshaping the landscape. The paper also addressed current challenges, including the integration of new technologies and the necessity for effective schema matching solutions, highlighting the continuously evolving nature of schema matching in the context of Big Data.

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Section: Types and Classificationmentioning

confidence: 99%

Overview on Data Ingestion and Schema Matching

El Haddadi,

Chevalier,

Dousset

et al. 2024

Data and Metadata

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“…There are several strategies for the utility function f : R N → R. We propose to use the well-known model of capacity [6]. This model proposes some levels of the attributes for which the attribute is considered unacceptable, acceptable, advisable, optimum or any other qualifier that make the attributes comparable.…”

Section: Security Assurance Of Supportingmentioning

confidence: 99%

Multi-Layer Security Assurance of the 5G Automotive System Based on Multi-Criteria Decision Making

Mongay Batalla,

de la Cruz Llopis,

Gómez

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Security assurance is the capacity of any teleinformatic system to demonstrate that the system is secure. It is provided by testing the system by an independent laboratory. Such an evaluation gives to the customer certainty that the system or product is secure enough for the intended use. In this paper we discuss the security assurance that the automotive sector requires to the 5G network for the secure communication of the Intelligent Transport System applications. In this case, the automotive sector takes the role of the customer of the network operator that provides connectivity of cars, trucks, bicycles, pedestrians and traffic infrastructure for creating vehicle to everything platform. Concretely, in this paper we (1) show new methodologies for evaluating network security, (2) provide a new strategy for the customer (automotive company) to select the underlying system based on network assurance levels, (3) survey security functionalities of a network devoted to automotive applications and demonstrate how automotive applications, 5G network and physical infrastructure, cooperate for enhancing security of the end-to-end system, and (4) provide example security evaluation Manuscript

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