An authorization model for query execution in the cloud

Context-aware Authorization Model for Smartphones

2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) ◽

10.1109/iemtronics52119.2021.9422667 ◽

2021 ◽

Author(s):

Moeiz Miraoui

Keyword(s):

Context Aware ◽

Authorization Model

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Designing A Healthcare Authorization Model Based On Cloud Authentication

Intelligent Automation & Soft Computing ◽

10.1080/10798587.2014.888243 ◽

2014 ◽

Vol 20 (3) ◽

pp. 365-379 ◽

Cited By ~ 7

Author(s):

Chin-Ling Chen ◽

Tsai-Tung Yang ◽

Fang-Yie Leu ◽

Yi-Li Huang

Keyword(s):

Model Based ◽

Authorization Model

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CoPart: a context-based partitioning technique for big data

Journal Of Big Data ◽

10.1186/s40537-021-00410-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sara Migliorini ◽

Alberto Belussi ◽

Elisa Quintarelli ◽

Damiano Carra

Keyword(s):

Big Data ◽

Quality Criteria ◽

Query Execution ◽

Data Correlation ◽

Huge Amount ◽

Programming Paradigm ◽

Computation Node ◽

Contextual Dimension ◽

Partitioning Technique ◽

The Way

AbstractThe MapReduce programming paradigm is frequently used in order to process and analyse a huge amount of data. This paradigm relies on the ability to apply the same operation in parallel on independent chunks of data. The consequence is that the overall performances greatly depend on the way data are partitioned among the various computation nodes. The default partitioning technique, provided by systems like Hadoop or Spark, basically performs a random subdivision of the input records, without considering the nature and correlation between them. Even if such approach can be appropriate in the simplest case where all the input records have to be always analyzed, it becomes a limit for sophisticated analyses, in which correlations between records can be exploited to preliminarily prune unnecessary computations. In this paper we design a context-based multi-dimensional partitioning technique, called CoPart, which takes care of data correlation in order to determine how records are subdivided between splits (i.e., units of work assigned to a computation node). More specifically, it considers not only the correlation of data w.r.t. contextual attributes, but also the distribution of each contextual dimension in the dataset. We experimentally compare our approach with existing ones, considering both quality criteria and the query execution times.

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Distributed numerical and machine learning computations via two-phase execution of aggregated join trees

Proceedings of the VLDB Endowment ◽

10.14778/3450980.3450991 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1228-1240

Author(s):

Dimitrije Jankov ◽

Binhang Yuan ◽

Shangyu Luo ◽

Chris Jermaine

Keyword(s):

Machine Learning ◽

Query Execution ◽

Relational System ◽

Numerical Computations ◽

Two Phase ◽

Execution Strategy ◽

Relational Joins

When numerical and machine learning (ML) computations are expressed relationally, classical query execution strategies (hash-based joins and aggregations) can do a poor job distributing the computation. In this paper, we propose a two-phase execution strategy for numerical computations that are expressed relationally, as aggregated join trees (that is, expressed as a series of relational joins followed by an aggregation). In a pilot run, lineage information is collected; this lineage is used to optimally plan the computation at the level of individual records. Then, the computation is actually executed. We show experimentally that a relational system making use of this two-phase strategy can be an excellent platform for distributed ML computations.

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DeepEverest

Proceedings of the VLDB Endowment ◽

10.14778/3485450.3485460 ◽

2021 ◽

Vol 15 (1) ◽

pp. 98-111

Author(s):

Dong He ◽

Maureen Daum ◽

Walter Cai ◽

Magdalena Balazinska

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Query Execution ◽

Indexing Technique ◽

Efficient Execution

We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63X and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes.

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