Interactive, Flexible, and Generic What-If Analyses Using In-Memory Column Stores

Klauck, Stefan; Butzmann, Lars; Müller, Stephan; Faust, Martin; Schwalb, David; Uflacker, Matthias; Sinzig, Werner; Plattner, Hasso

doi:10.1007/978-3-319-18123-3_29

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…It is performed if the outcomes cannot be easily captured as a mathematical function over the input parameters. What-if analysis has diverse applications, e.g., in data warehousing [27], system workload profiling [17], policy design and forecasting [16], and source code management [4]. Traditional What-if Analysis.…”

Section: Introductionmentioning

confidence: 99%

What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

Nguyen¹,

Zheng

Weidlich

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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What-if analysis is a data-intensive exploration to inspect how changes in a set of input parameters of a model influence some outcomes. It is motivated by a user trying to understand the sensitivity of a model to a certain parameter in order to reach a set of goals that are defined over the outcomes. To avoid an exploration of all possible combinations of parameter values, efficient what-if analysis calls for a partitioning of parameter values into data ranges and a unified representation of the obtained outcomes per range. Traditional techniques to capture data ranges, such as histograms, are limited to one outcome dimension. Yet, in practice, what-if analysis often involves conflicting goals that are defined over different dimensions of the outcome. Working on each of those goals independently cannot capture the inherent trade-off between them. In this paper, we propose techniques to recommend data ranges for what-if analysis, which capture not only data regularities, but also the trade-off between conflicting goals. Specifically, we formulate a parametric data partitioning problem and propose a method to find an optimal solution for it. Targeting scalability to large datasets, we further provide a heuristic solution to this problem. By theoretical and empirical analyses, we establish performance guarantees in terms of runtime and result quality.

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Section: Introductionmentioning

confidence: 99%

What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

Nguyen¹,

Zheng

Weidlich

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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Toward a notation for modeling value driver trees: Classification development and research agenda

Matthies

2024

J Corp Accounting Finance

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Value driver trees (VDTs) are abstract, indicator‐based representations of a business model. Although they are conceptual models by nature, no systematic and unified approaches to their modeling exist to date. In fact, a heterogeneous understanding of their conception and methodological implementation has been established. The goal of this study is to provide more clarity in this regard by examining the semantics (the question of “what?”) and syntax (the question of “how?”) with respect to VDT modeling. For this purpose, a structured literature review was conducted in which a collection of 161 VDTs was evaluated. Based on an extended taxonomy design process, the typical model constructs of VDTs were extracted. As a result, a so‐called VDT Model Classification was derived, which structures 34 model constructs into three dimensions with eight categories. This classification establishes a clearer understanding of the model constructs and their representation, thereby providing a conceptual framework for a unified and more substantiated “vocabulary” for VDT modeling. Finally, a research agenda has been formulated that generally addresses the role and future application potential of VDTs and, in particular, describes the next steps toward a sound notation for modeling VDTs.

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Conceiving Hybrid What-If Scenarios Based on Usage Preferences

Carvalho

Belo

2017

Lecture Notes in Business Information Processing

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Interactive, Flexible, and Generic What-If Analyses Using In-Memory Column Stores

Cited by 3 publications

References 14 publications

What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

Toward a notation for modeling value driver trees: Classification development and research agenda

Conceiving Hybrid What-If Scenarios Based on Usage Preferences

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