Processing nesteddata collections in large-scale distributed systems exhibits considerable challenges in query processing. Manipulating such data demands an extravagant number of operations, leading to extensive data...
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ISBN:
(纸本)9798350395679;9798350395662
Processing nesteddata collections in large-scale distributed systems exhibits considerable challenges in query processing. Manipulating such data demands an extravagant number of operations, leading to extensive data duplication and imposing challenges in ensuring balanced distribution across partitions. This research proposes preparing flattening procedures for nested data structures. The work aims to alleviate the adverse implications of data duplication and information loss while addressing the irregularity of nesting structures. The efficacy of the proposed approach is assessed on question-answering datasets, comparing its performance against the Pandas Python package flattening implementation.
Flattening is known to be a performance-boosting technique to orchestrate parallel computations on arbitrarily deeply nested arrays. In this paper, we propose a flattening transformation that deals with nesteddata st...
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ISBN:
(纸本)9783031745577;9783031745584
Flattening is known to be a performance-boosting technique to orchestrate parallel computations on arbitrarily deeply nested arrays. In this paper, we propose a flattening transformation that deals with nested data structures that are composed of combinations of arrays and records. We choose the functional array programming language SaC as basis for this work, as it already supports flattening of homogeneously nested arrays, i.e. arrays in which all elements have the same shape. We propose an extension of SaC's syntax for records that allows records and arrays to be used in homogeneously nested form, and provide an implementation of this record transformation in the SaC compiler. Based on that extension, we show how any legal program that operates with such datastructures can be transformed into an equivalent one that does not require any records at runtime.
Researchers in group counseling often encounter complex data from individual clients who are members of a group. Clients in the same group may be more similar than clients from different groups and this can lead to vi...
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Researchers in group counseling often encounter complex data from individual clients who are members of a group. Clients in the same group may be more similar than clients from different groups and this can lead to violations of statistical assumptions. The complexity of the data also means that predictors and outcomes can be measured at both the client and the group level. Researcher questions may focus on variables at the client level or the group level, or the interaction of client and group level variables. In this article, we introduce multilevel modeling as a tool that can be used both to account for the complex structure of the data and to incorporate variables at both the client and group levels. A published group counseling study is used as an example.
Objectives: This commentary illustrates the advantages of multilevel modeling compared to statistical techniques that ignore hierarchies, based on two empirical traffic safety examples. Methods: The common concept sha...
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An unconventional interpretation of Kohonen self-organizing maps is presented. It employs linguistic variables and mechanisms of fuzzy decision theory to quantitatively reveal pattern structure in the map following se...
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ISBN:
(纸本)0780302362
An unconventional interpretation of Kohonen self-organizing maps is presented. It employs linguistic variables and mechanisms of fuzzy decision theory to quantitatively reveal pattern structure in the map following self-organization. A simple example of pattern clustering is provided.
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