Intelligent Visual Analytics Queries

Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.; Morent, D.; Schneidewind, Jörn

doi:10.1109/vast.2007.4389001

Cited by 33 publications

(20 citation statements)

References 11 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…28 The Intelligent Visual Analytics Query (IVQuery) is a visual analysis tool to help users perceive relationships among multiple time-series data dimensions. 29 TimeWheel puts the time axis in the center and other data dimensions arranged circularly. Each data item corresponds to a group of lines from the time axis to the axes for data dimensions .…”

Section: Related Workmentioning

confidence: 99%

Exploring large scale time-series data using nested timelines

2013

View full text Add to dashboard Cite

When data analysts study time-series data, an important task is to discover how data patterns change over time. If the dataset is very large, this task becomes challenging. Researchers have developed many visualization techniques to help address this problem. However, little work has been done regarding the changes of multivariate patterns, such as linear trends and clusters, on time-series data. In this paper, we describe a set of history views to fill this gap. This technique works under two modes: merge and non-merge. For the merge mode, merge algorithms were applied to selected time windows to generate a change-based hierarchy. Contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. In the non-merge mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid-based views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields and distance maps were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time.

show abstract

Section: Related Workmentioning

confidence: 99%

Exploring large scale time-series data using nested timelines

2013

View full text Add to dashboard Cite

show abstract

“…Step 3: Enable users to perform visual query [8] to find the relationship of problem attributes with the other attributes in the stream; then present the mining results (i.e., persistency and correlations) from the marked areas in an interactive visual representation (i.e., tooltips) for finding the impact factors. The workload has been evenly distributed among servers except for a few servers which show more red (e.g., Server 8 in SYS 1; Server 3 in SYS 3).…”

Section: Our Approachmentioning

confidence: 99%

“…An enlarged marked area provides a focus area for users to analyze the correlation between the problem attribute and other attributes from the entire data stream. Intelligent visual analytic queries have been introduced in our previous work [8]. Instead of using a rubber-banding technique to select the focus area, we use a marked area for correlation analysis.…”

Section: Automated Visual Correlation Querymentioning

confidence: 99%

See 1 more Smart Citation

Visual analytics of anomaly detection in large data streams

et al. 2009

Self Cite

View full text Add to dashboard Cite

Most data streams usually are multi-dimensional, high-speed, and contain massive volumes of continuous information. They are seen in daily applications, such as telephone calls, retail sales, data center performance, and oil production operations. Analysts want insight into the behavior of this data. They want to catch the exceptions in flight to reveal the causes of the anomalies and to take immediate action. To guide the user in finding the anomalies in the large data stream quickly, we derive a new automated neighborhood threshold marking technique, called AnomalyMarker. This technique is built on cell-based data streams and user-defined thresholds. We extend the scope of the data points around the threshold to include the surrounding areas. The idea is to define a focus area (marked area) which enables users to (1) visually group the interesting data points related to the anomalies (i.e., problems that occur persistently or occasionally) for observing their behavior; (2) discover the factors related to the anomaly by visualizing the correlations between the problem attribute with the attributes of the nearby data items from the entire multi-dimensional data stream. Mining results are instantaneously presented in graphical representations (i.e., tooltip) for the user to zoom into the problem regions. Different algorithms are introduced which try to optimize the size and extent of the anomaly markers. We have applied this technique with success to detect data stream anomalies in large real-world enterprise server performance and data center energy management.

show abstract

“…Significant research has been conducted on improving the sense-making process by providing more convenient visualization techniques [7,13]. Most of these efforts focus on visualizing datasets to more easily reveal the narratives within.…”

Section: Introductionmentioning

confidence: 99%

Capturing and supporting the analysis process

Kadivar

Chen

Dunsmuir

et al. 2009

2009 IEEE Symposium on Visual Analytics Science and Technology

View full text Add to dashboard Cite

Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw [24]). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw's approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.

show abstract

Intelligent Visual Analytics Queries

Cited by 33 publications

References 11 publications

Exploring large scale time-series data using nested timelines

Exploring large scale time-series data using nested timelines

Visual analytics of anomaly detection in large data streams

Capturing and supporting the analysis process

Contact Info

Product

Resources

About