Visual feedback in querying large databases

Keim, Daniel A.; Kriegel, H.-P.; Seidl, Thomas

doi:10.1109/visual.1993.398864

Cited by 16 publications

(10 citation statements)

References 15 publications

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“…Nice overviews of the approaches can be found in a number of recent books [3] [20] [23] [26]. The techniques can be classified based on three criteria [10] (see also Figure 1):…”

Section: Classification Of Visual Data Mining Techniquesmentioning

confidence: 99%

Pushing the Limit in Visual Data Exploration: Techniques and Applications

Keim

Panse

Schneidewind

et al. 2003

Lecture Notes in Computer Science

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Abstract. With the rapid growth in size and number of available databases, it is necessary to explore and develop new methods for analysing the huge amounts of data. Mining information and interesting knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Analyzing the huge amount (usually tera-bytes) of data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics, however, a very difficult task. Visual Exploration and Visual Data Mining techniques apply human visual perception to the exploration of large data sets and have proven to be of high value in exploratory data analysis. Presenting data in an interactive, graphical form often opens new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual exploration techniques and present new results obtained from applying PixelBarCharts in sales analysis and internet usage management.

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“…Nice overviews of the approaches can be found in a number of recent books [3] [20] [23] [26]. The techniques can be classified based on three criteria [10] (see also Figure 1):…”

Section: Classification Of Visual Data Mining Techniquesmentioning

confidence: 99%

Pushing the Limit in Visual Data Exploration: Techniques and Applications

Keim

Panse

Schneidewind

et al. 2003

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Nice overviews of the approaches can be found in a number of recent books [5] [32] [37] [42]. The techniques can be classified based on three criteria [17] (see also figure 1):…”

Section: Classification Of Visual Data Mining Techniquesmentioning

confidence: 99%

Visual Data Mining of Large Spatial Data Sets

Keim

Panse

Sips

2003

Databases in Networked Information Systems

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Abstract. Extraction of interesting knowledge from large spatial databases is an important task in the development of spatial database systems. Spatial data mining is the branch of data mining that deals with spatial (location) data. Analyzing the huge amount (usually terabytes) of spatial data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics etc. is, however, a very difficult task. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially the area of analyzing spatial data. We provide some examples for effective visualizations of spatial data in important application areas such as consumer analysis, e-mail traffic analysis, and census demographics.

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“…There are opportunities beyond these, including visualization of the query specification process in databases. One of the approaches is to develop a database query system like, for example, the system VisDB [ 16] that supports the query specification process by using each pixel of the display to represent one data item of the database. By arranging and colouring the pixels according to their relevance to the query, the user gets a visual impression of data and range queries on the data.…”

Section: Graphical User Interfaces Employing Visualization Techniquesmentioning

confidence: 99%