Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data

Tatu, Andrada; Albuquerque, Georgia; Eisemann, Martin; Bak, Peter; Theisel, Holger; Magnor, Marcus; Keim, Daniel A.

doi:10.1109/tvcg.2010.242

Cited by 59 publications

(41 citation statements)

References 24 publications

(49 reference statements)

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“…Tatu et al [22] proposed an automatic analysis method to help users locate effective visual representations from various candidates via extracting potentially relevant visual structures. TGRASS [23] is a temporal-extended GIS that aims at not only managing, analyzing, processing and visualizing large environmental spatiotemporal datasets, but also investigating and assessing the temporal relationships between them.…”

Section: Visualization Of Spatiotemporal Datamentioning

confidence: 99%

A Web-Based Visual and Analytical Geographical Information System for Oil and Gas Data

Wei

Wang

2017

IJGI

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With the development of strategic oil and gas assets, massive spatiotemporal oil and gas data have been accumulated. Application systems that assist in the storage and management of the voluminous and complex oil and gas datasets are in high demand. The voluminous and various data should be leveraged and turned into information for business decision-making and operation assistance. In this paper, we propose a set of visual analytic methods that specialize in oil and gas data; and, we develop a web-based oil and gas data management, visualization and analytical system, called Oil and Gas Visual Exploration System (OGVES). With OGVES, complex and multi-sourced oil and gas data can be stored, searched, filtered, and represented. As a web-based system, the OGVES provides more accessibility, convenience and efficiency than traditional desktop systems. Spatial scales and temporal primitives contained in oil and gas data are discussed. Different visualization methods are then presented to explore and represent spatiotemporal features of the oil and gas data. Various case studies demonstrate the usability of the system.

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Section: Visualization Of Spatiotemporal Datamentioning

confidence: 99%

A Web-Based Visual and Analytical Geographical Information System for Oil and Gas Data

Wei

Wang

2017

IJGI

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“…Authors from the data visualization literature have proposed methods to detect the "best" projections of multidimensional data sets, such as Projection Pursuit [8], Scagnostics [22], or Tatu et al's relevance measures [19]. Such methods would form excellent complements for Claude's recommendations.…”

Section: Related Workmentioning

confidence: 99%

Semi-Automated Exploration of Data Warehouses

Sellam

Müller²,

Kersten

2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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Exploratory data analysis tries to discover novel dependencies and unexpected patterns in large databases. Traditionally, this process is manual and hypothesis-driven. However, analysts can come short of patience and imagination. In this paper, we introduce Claude, a hypothesis generator for data warehouses. Claude follows a 2-step approach: (1) It detects interesting views, by exploiting non-linear statistical dependencies between the dimensions and the measure.(2) To explain its findings, it detects local patterns in these views and describes them with SQL queries. Technically, we derive a model of interestingness from fundamental information theory. To exploit this model, we present aggressive approximations and heuristics, allowing Claude to be fast and more accurate than state-of-art view selection algorithms.

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“…Also, projection pursuit approaches, such as initially presented by Friedman and Tukey [13], try to identify interesting 2D subspaces in highdimensional data (mostly depicted by scatter plot views). Further heuristic interestingness filters for Scatter-and Parallel Coordinate plots have been discussed in [31,9] and may narrow down the potentially large search space for high-dimensional data. In [32], an explorative overview of subspaces contained in high-dimensional data based on mutual differences and clustering quality properties was introduced.…”

Section: Interest-driven Data Filtering For Visual Analysismentioning

confidence: 99%

Feedback-driven interactive exploration of large multidimensional data supported by visual classifier

Behrisch

Korkmaz

Shao

et al. 2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

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Abstract-The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user-and context-dependent visual exploration systems.

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Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data

Cited by 59 publications

References 24 publications

A Web-Based Visual and Analytical Geographical Information System for Oil and Gas Data

A Web-Based Visual and Analytical Geographical Information System for Oil and Gas Data

Semi-Automated Exploration of Data Warehouses

Feedback-driven interactive exploration of large multidimensional data supported by visual classifier

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