Trends in Development of Databases and Blockchain

Raikwar, Mayank; Gligoroski, Danilo; Velinov, Goran

doi:10.1109/sds49854.2020.9143893

Cited by 21 publications

(5 citation statements)

References 22 publications

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“…By organizing the data in blocks that are inseparably linked to each other via cryptographic hashing, it can be ensured that neither individual data items within a block nor entire blocks can be manipulated. As this chain of blocks is managed in a distributed manner, i.e., multiple parties manage an equivalent copy of the chain, manipulations can be easily detected and reversed [94]. In addition to providing a secure storage for IoT data [95], however, blockchain technologies also facilitate the trustworthy sharing of sensitive data in inherently semi-trusted or unreliable environments [96].…”

Section: Confidentialitymentioning

confidence: 99%

Data Is the New Oil–Sort of: A View on Why This Comparison Is Misleading and Its Implications for Modern Data Administration

Stach

2023

Future Internet

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Currently, data are often referred to as the oil of the 21st century. This comparison is not only used to express that the resource data are just as important for the fourth industrial revolution as oil was for the technological revolution in the late 19th century. There are also further similarities between these two valuable resources in terms of their handling. Both must first be discovered and extracted from their sources. Then, the raw materials must be cleaned, preprocessed, and stored before they can finally be delivered to consumers. Despite these undeniable similarities, however, there are significant differences between oil and data in all of these processing steps, making data a resource that is considerably more challenging to handle. For instance, data sources, as well as the data themselves, are heterogeneous, which means there is no one-size-fits-all data acquisition solution. Furthermore, data can be distorted by the source or by third parties without being noticed, which affects both quality and usability. Unlike oil, there is also no uniform refinement process for data, as data preparation should be tailored to the subsequent consumers and their intended use cases. With regard to storage, it has to be taken into account that data are not consumed when they are processed or delivered to consumers, which means that the data volume that has to be managed is constantly growing. Finally, data may be subject to special constraints in terms of distribution, which may entail individual delivery plans depending on the customer and their intended purposes. Overall, it can be concluded that innovative approaches are needed for handling the resource data that address these inherent challenges. In this paper, we therefore study and discuss the relevant characteristics of data making them such a challenging resource to handle. In order to enable appropriate data provisioning, we introduce a holistic research concept from data source to data sink that respects the processing requirements of data producers as well as the quality requirements of data consumers and, moreover, ensures a trustworthy data administration.

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Section: Confidentialitymentioning

confidence: 99%

Data Is the New Oil–Sort of: A View on Why This Comparison Is Misleading and Its Implications for Modern Data Administration

Stach

2023

Future Internet

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“…DynamoDB and PostgreSQL are two different databases provided as Amazon Web Service that are widely used by corporations and individuals [21]. For this project, PostgreSQL was first used for testing purposes and then converted into DynamoDB as the final choice.…”

Section: Databases (Dynamodb Vs Postgresql)mentioning

confidence: 99%

An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data

Li,

Zou

2021

Preprint

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The rapid development in the field of System of Chip (SoC) technology, Internet of Things (IoT), cloud computing, and artificial intelligence has brought more possibilities of improving and solving the current problems. With data analytics and the use of machine learning/deep learning, it is made possible to learn the underlying patterns and make decisions based on what was learned from massive data generated from IoT sensors. When combined with cloud computing, the whole pipeline can be automated, and free of manual controls and operations. In this paper, an implementation of an automated data engineering pipeline for anomaly detection of IoT sensor data is studied and proposed. The process involves the use of IoT sensors, Raspberry Pis, Amazon Web Services (AWS) and multiple machine learning techniques with the intent to identify anomalous cases for the smart home security system.

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“…Since, for distributed ledger technologies, it has been conjectured that consistency, decentralization, and scalability in their 'strongest forms' cannot be achieved simultaneously [1], [2], our analysis focuses on these aspects and the distinct trade-offs made by a MEG: in brief, we show that the MEG achieves decentralization and scalability, but does not strive for consensus or strong consistency. As the main contribution, we therefore provide an analysis of the degree to which the MEG fulfills consistency, deployability in decentralized scenarios, and scalability: Consistency: Since Matrix provides availability and partition tolerance, in accordance with the CAP theorem [3], the MEG necessarily has to sacrifice strong consistency.…”

Section: Introductionmentioning

confidence: 96%