15,704 research outputs found
Interaction design in multidimensional visualization : techniques for multidimensional data visualization, exploration and visual analytics
University of Technology Sydney. Faculty of Engineering and Information Technology.Interaction is an overloaded term in information visualization. Basically, every software tool is interactive but mostly through the manipulation of a widget. Broadly speaking, a visualization is just a software application. What makes the interactive component of a visualization really distinctive is how well it supports an arbitrary selection of data directly in the interface in order to facilitate subsequent analytic tasks. This is challenging due to over-plotting and visual clutter in the multidimensional space and such phenomenon is commonly known as the curse of dimensionality.
Data selection is a frontier of a visualization and too many multidimensional visualizations claiming to be interactive mostly address the change of view without explicitly specifying the core technique of how to materialize such operations. Perhaps, the interactive component is achieved through the traditional widget.
To overcome the complexity of truly interacting with multidimensional data for effective visual analytics, we first propose an interactive framework for better understanding of the problem domains. Dynamic data selection is materialized by a novel and sophisticated technique called the Hierarchical Virtual Node which opens an application to interact with data directly in parallel coordinates that would otherwise have been impossible or difficult to achieve by existing methods. It works well even under the circumstance of the curse of dimensionality and offers several advantages over others. For example, the use case only requires a mouse click to select a set of data item(s). To achieve an efficient visual analytics, a set of analytic tasks are also developed in each layer of the proposed framework
Integration of Exploration and Search: A Case Study of the M3 Model
International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
Exploring data requires a fast feedback loop from the analyst to the system,
with a latency below about 10 seconds because of human cognitive limitations.
When data becomes large or analysis becomes complex, sequential computations
can no longer be completed in a few seconds and data exploration is severely
hampered. This article describes a novel computation paradigm called
Progressive Computation for Data Analysis or more concisely Progressive
Analytics, that brings at the programming language level a low-latency
guarantee by performing computations in a progressive fashion. Moving this
progressive computation at the language level relieves the programmer of
exploratory data analysis systems from implementing the whole analytics
pipeline in a progressive way from scratch, streamlining the implementation of
scalable exploratory data analysis systems. This article describes the new
paradigm through a prototype implementation called ProgressiVis, and explains
the requirements it implies through examples.Comment: 10 page
On-line analytical processing
On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft
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