Strategies for array data retrieval from a relational back-end based on access patterns

Andrejev, Andrej; Orsborn, Kjell; Risch, Tore

doi:10.1007/s00607-020-00804-x

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Despite its low similarity, this transitivity has more limitations than standard recognition techniques, allowing the disrupted signal to be detected well under fuzzy constraints. Information entropy has been extensively employed in cluster analysis, outlier identification, uncertainty measurement, and so on as an efficient measurement technique [13]. e information entropy within and between classes is used to measure the importance of each attribute in the clustering process.…”

Section: Trust Probability Evaluation Algorithm For Cluster Analysis ...mentioning

confidence: 99%

Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability

Zhang¹,

Talha

2022

Security and Communication Networks

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In view of the present poor impact of multi relational data clustering, a multirelational data clustering effectiveness analysis model based on trusted probability is created. The construction of the multirelational data clustering trusted probability evaluation model, standardization of the multirelational data clustering trusted probability evaluation index, optimization of the multirelational data clustering trusted probability analysis process, and data clustering processing quality and finally, investigations show that the validity analysis model of multirelational data clustering based on credible probability is more effective in practice and satisfies the research goals entirely.

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Section: Trust Probability Evaluation Algorithm For Cluster Analysis ...mentioning

confidence: 99%

Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability

Zhang¹,

Talha

2022

Security and Communication Networks

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“…6 The alternative is using LLVM directly for generating LLVM IR. Using the NVPTX 7 backend, we can generate PTX, which can be run on the GPU. This is the approach we can take to reuse the Umbra IR to LLVM IR backend to have a highlevel API for our code generation.…”

Section: Llvm Nvptx Interfacementioning

confidence: 99%

Recursive SQL and GPU-support for in-database machine learning

Schüle¹,

Lang²,

Springer³

et al. 2022

Distrib Parallel Databases

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In machine learning, continuously retraining a model guarantees accurate predictions based on the latest data as training input. But to retrieve the latest data from a database, time-consuming extraction is necessary as database systems have rarely been used for operations such as matrix algebra and gradient descent. In this work, we demonstrate that SQL with recursive tables makes it possible to express a complete machine learning pipeline out of data preprocessing, model training and its validation. To facilitate the specification of loss functions, we extend the code-generating database system Umbra by an operator for automatic differentiation for use within recursive tables: With the loss function expressed in SQL as a lambda function, Umbra generates machine code for each partial derivative. We further use automatic differentiation for a dedicated gradient descent operator, which generates LLVM code to train a user-specified model on GPUs. We fine-tune GPU kernels at hardware level to allow a higher throughput and propose non-blocking synchronisation of multiple units. In our evaluation, automatic differentiation accelerated the runtime by the number of cached subexpressions compared to compiling each derivative separately. Our GPU kernels with independent models allowed maximal throughput even for small batch sizes, making machine learning pipelines within SQL more competitive.

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“…Published by Francis Academic Press, UK -66-into the blockchain technology framework by Ethereum platform that mobile network big data played a greater value, which also upgraded the blockchain from the 1.0 era to the 2.0 era [6]. The application concept of mobile network big data in blockchain is that it is effectively realized with the help of blockchain architecture, which has the characteristics of decentralization and distrust, and has an executable environment, as shown in the figure 1.…”

Section: Mobile Network Big Data Privacy Feature Recognitionmentioning

confidence: 99%

Privacy retrieval method of big data in mobile network based on edge computing

2022

AJCIS

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In order to better achieve the goal of big data privacy retrieval and ensure user data security, a mobile network big data privacy retrieval method based on edge computing is proposed. Combined with the principle of edge computing, identify the privacy characteristics of mobile network big data, build a feature database for data management, optimize the privacy data security encryption algorithm, and simplify the privacy retrieval process of mobile network big data. Finally, experiments show that the mobile network big data privacy retrieval method based on edge computing has high security and effectiveness in the process of practical application, and fully meets the research requirements.

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Strategies for array data retrieval from a relational back-end based on access patterns

Cited by 3 publications

References 19 publications

Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability

Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability

Recursive SQL and GPU-support for in-database machine learning

Privacy retrieval method of big data in mobile network based on edge computing

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