Towards a Data-Centric Architecture in the Automotive Industry

Alvarez-Coello, Daniel; Wilms, Daniel; Bekan, Adnan; Gómez, Jorge Marx

doi:10.1016/j.procs.2021.01.215

Cited by 16 publications

(2 citation statements)

References 5 publications

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“…In recent years, the rise of big data and advanced analytics has led to a growing emphasis on data-centric approaches across various industries, from healthcare to finance and to retail [21]. By adopting a data-centric approach, organizations can gain a competitive advantage by improving decision making, increasing efficiency, and reducing costs [22]. A data-centric approach is particularly important in the context of big data, where the 7 Vs of big data (velocity, volume, value, variety, veracity, volatility, and validity) can make it challenging to extract meaningful insights [23].…”

Section: Data-centric and Data-driven Aimentioning

confidence: 99%

Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

2023

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The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas to rethink our approaches to data strategies for AI.

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Section: Data-centric and Data-driven Aimentioning

confidence: 99%

Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

2023

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“…The layered hierarchy has rigidly set building nodes in this pyramid-like structure, where the data comes first. It collects medical/clinical facts in an unorganized form, such as a number or character [30]. However, without patient context, medical data can mean little, such as a patient name, which cannot provide a complete, detailed understanding of the specific record.…”

Section: A Layered Hierarchy Of Medical Dikw and Blockchainmentioning

confidence: 99%

BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

Shaikh

Khan

Teng

et al. 2022

Wireless Communications and Mobile Computing

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This paper presents a layered hierarchy that depicts the progressive relationship between data, information, knowledge, and wisdom. To begin with, data is gathered and organized into information. Information is gathered, filtered, refined, and put through an investigation process to create knowledge. Wisdom is attained after knowledge discovery through the process of filtration and aggregation through experience. The layered hierarchy in the domain of e-healthcare necessitates higher scheduling costs for data collection, processing wisdom, and management, which is also an insecure and untrustworthy process for progressive medical service. The medical industry faces a difficult problem in providing collected data integrity, information reliability, and knowledge trustworthiness for the service of progressive medical relationships in the face of an increasing number of day-to-day records. The blockchain consortium hyperledger (fabric) has been used in this paper to act as a bridge that bridges the gap between electronic data, information, knowledge, and wisdom (DIKW) movement and processes by enabling the process of the layered hierarchy of schedule information and management and providing security and transparency. For e-healthcare information management and privacy, the DIKW-ledger, such as patients’ consultancy information, availing medical services, personal records, appointments, treatment details, and other health-related transactions, a consortium hyperledger fabric-enabled efficient architecture is proposed. This proposed architecture creates two networks: a public network for medical stakeholders to exchange and agree on specific medical activities before being preserved on distributed storage (read-only after record registration) and a private network for complete DIKW process scheduling and management. We designed and created smart contracts for this purpose, as well as use-case diagrams to describe the overall execution process. The proposed architectural solution provides more efficient information integrity, provenance, and storage procedures to immutably preserve the medical ledger in a permissioned hash-encrypted structure.

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Concept Idea of Engineer’s Decision Support System

Macutkevičius

Junevičius

2022

Lecture Notes in Intelligent Transportation and Infrastructure

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Towards a Data-Centric Architecture in the Automotive Industry

Cited by 16 publications

References 5 publications

Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

Concept Idea of Engineer’s Decision Support System

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