Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

This chapter treats an interactive visual analysis tool called PCTT, Parallel Coordinates Version of Time-tunnel, for multidimensional data and multi-attributes data. Especially, in this chapter, the author introduces the combinatorial use of PCTT and 2Dto2D visualization functionality for visual analytics of network data. 2Dto2D visualization functionality displays multiple lines those represent four-dimensional (four attributes) data drawn from one (2D, two attributes) plane to the other (2D, two attributes) plane in a 3D space. Network attacks like the intrusion have a certain access pattern strongly related to the four attributes of IP packet data, i.e., source IP, destination IP, source Port, and destination Port. So, 2Dto2D visualization is useful for detecting such access patterns. Although it is possible to investigate access patterns of network attacks at the attributes level of IP packets using 2Dto2D visualization functionality, statistical analysis is also necessary to find out suspicious periods of time that seem to be attacked. This is regarded as the macro level visual analytics and the former is regarded as the micro level visual analytics. In this chapter, the author also introduces such combinatorial use of PCTT for macro level to micro level visual analytics of network data as an example of multidimensional data. Furthermore, the author introduces other visual analytics example about sensor data to clarify the usefulness of PCTT.

Original languageEnglish
Title of host publicationModeling and Optimization in Science and Technologies
PublisherSpringer Verlag
Pages231-255
Number of pages25
DOIs
Publication statusPublished - Jan 1 2015

Publication series

NameModeling and Optimization in Science and Technologies
Volume4
ISSN (Print)2196-7326
ISSN (Electronic)2196-7334

Fingerprint

Visual Analytics
Multidimensional Data
Tunnel
Macros
Tunnels
Attribute
Visualization
Attack
Information Storage and Retrieval
Statistical methods
Period of time
Statistical Analysis
Sensors
Sensor
Necessary
Line

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Medical Assisting and Transcription
  • Applied Mathematics

Cite this

Okada, Y. (2015). Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data. In Modeling and Optimization in Science and Technologies (pp. 231-255). (Modeling and Optimization in Science and Technologies; Vol. 4). Springer Verlag. https://doi.org/10.1007/978-3-319-09177-8-10

Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data. / Okada, Yoshihiro.

Modeling and Optimization in Science and Technologies. Springer Verlag, 2015. p. 231-255 (Modeling and Optimization in Science and Technologies; Vol. 4).

Research output: Chapter in Book/Report/Conference proceedingChapter

Okada, Y 2015, Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data. in Modeling and Optimization in Science and Technologies. Modeling and Optimization in Science and Technologies, vol. 4, Springer Verlag, pp. 231-255. https://doi.org/10.1007/978-3-319-09177-8-10
Okada Y. Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data. In Modeling and Optimization in Science and Technologies. Springer Verlag. 2015. p. 231-255. (Modeling and Optimization in Science and Technologies). https://doi.org/10.1007/978-3-319-09177-8-10
Okada, Yoshihiro. / Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data. Modeling and Optimization in Science and Technologies. Springer Verlag, 2015. pp. 231-255 (Modeling and Optimization in Science and Technologies).
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