Coordinated Multiple Views Research Articles

An interactive web-based geovisual analytics platform for co-clustering spatio-temporal data

Clustering methods are useful in analyzing patterns from big spatio-temporal data. However, previous studies typically rely on traditional clustering methods to explore spatial or temporal patterns. Co-clustering methods allow the concurrent analysis of spatial and temporal patterns by identifying location- and timestamp-clusters at the same time. By combining co-clustering with coordinated multiple views (CMV) in an interactive geovisual analytics platform, we facilitate the exploratory co-clustering analysis of spatio-temporal data and the results. Further enhanced by Web 2.0 standards, our geovisual analytics platform ease the access to co-clustering analysis from any web browser. More specifically, our platform allows users to upload data and to visually explore it using interactive CMV to help the selection of co-clustering parameters. Our platform also allows users to run co-clustering and to visually and interactively explore the results. To illustrate the use of our platform, we analyze Dutch annual average temperature for 28 stations from 1992 to 2011. Results show that our platform not only helps to get a better understanding of the dataset but also to choose the co-clustering parameters. Our platform helps to interpret the co-clustering results too, and it supports the extraction and exploration of complex patterns buried in the data. In the era of big data, our web-based platform enables the exploration of concurrent spatio-temporal patterns from large datasets by combing both computer power and human interpretative capabilities.

VISTILES: Coordinating and Combining Co-located Mobile Devices for Visual Data Exploration.

We present VISTILES, a conceptual framework that uses a set of mobile devices to distribute and coordinate visualization views for the exploration of multivariate data. In contrast to desktop-based interfaces for information visualization, mobile devices offer the potential to provide a dynamic and user-defined interface supporting co-located collaborative data exploration with different individual workflows. As part of our framework, we contribute concepts that enable users to interact with coordinated & multiple views (CMV) that are distributed across several mobile devices. The major components of the framework are: (i) dynamic and flexible layouts for CMV focusing on the distribution of views and (ii) an interaction concept for smart adaptations and combinations of visualizations utilizing explicit side-by-side arrangements of devices. As a result, users can benefit from the possibility to combine devices and organize them in meaningful spatial layouts. Furthermore, we present a web-based prototype implementation as a specific instance of our concepts. This implementation provides a practical application case enabling users to explore a multivariate data collection. We also illustrate the design process including feedback from a preliminary user study, which informed the design of both the concepts and the final prototype.

Interactive Document Clustering System Based on Coordinated Multiple Views

This paper proposes an interactive document clustering system, which is designed based on the concept of CMV (coordinated multiple views). An interactive document clustering is used by a user to obtain a set of document groups from a document collection in interactive manner. It is expected to be useful for various tasks such as text mining and document retrieval. As the result of document clustering consists of multiple objects such as clusters (document groups), documents, and words, each of those should be presented to users in different ways. Based on this consideration, the proposed system employs multiple views, each of which is designed for specific object such as document and keyword. A prototype system is implemented on TETDM (Total Environment for Text Data Mining), which is one of environments for developing text data mining tools. As it can provide the mechanism of coordination between modules, we decided to use it for developing the prototype system. The proposed system classifies information to be presented into 4 levels: clusters, document, bag of words, and word, each of which is displayed with different views. Experimental results with test participants show the effectiveness of the proposed system.

Modeling coordinated multiple views of heterogeneous data cubes for urban visual analytics

With the explosion of digital data, the need for advanced visual analytics, including coordinated multiple views (CMV), is rapidly increasing. CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets. CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network (AURIN) project. AURIN offers a platform providing seamless and secure access to an extensive range of distributed urban datasets across Australia. Visual exploration of these datasets is essential to support research endeavors. This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV. We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations. Using the concept of data cubes and hierarchical dimensions, we present strategies to automatically build render groups. This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion. The CMV approach is demonstrated using aggregate-level data, which is provided through federated data services. The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration.