Scientific Data Mining Task

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A Scientific Data Mining Task is a domain-specific data analytics task that is restricted to scientific data.



References

2020

2008

2001a

  • (Mann et al., 2001) ⇒ Bob Mann, Roy Williams, Malcolm Atkinson, Ken Brodlie, Amos Storkey, and Chris Williams. (2001). “Scientific Data Mining, Integration, and Visualization." Report on the Workshop on Scientific Data Mining, Integration and Visualization (SDMIV).
    • QUOTE: ... Much of the scientific data discussed at the workshop fell into three categories, and, while these do not represent an exhaustive list of scientific data types, much of the technology discussed in the meeting was directed to them. The three categories are:
      • The datacube, or array, class - meaning an annotated block of data in one, two, or more dimensions. This includes time-series and spectra (one dimensional); images, frequency-time spectra, etc (two-dimensional); voxel datasets and hyperspectral images (three-dimensional), and so on. The highly-optimised chips of modern computers handle these data structures well.
      • Records, or events, collected as a table. Also known as multi-parameter data. These datasets may come directly from an instrument (for example in a particle accelerator) or may be derived by picking features from a datacube (when stars are identified from an astronomical image). Relational databases hold these data effectively.
      • Sequences of symbols, for example a biological gene is represented by ...

2001b

2001c

  • (Ramakrishanan & Grama, 2001) ⇒ Naren Ramakrishnan, and Ananth Grama. (2001). “Mining Scientific Data.” In: Advances in Computers, 55.
    • The past two decades have seen rapid advances in high performance computing and tools for data acquisition in a variety of scientific domains. Coupled with the availability of massive storage systems and fast networking technology to manage and assimilate data, these have given a significant impetus to data mining in the scientific domain. Data mining is now recognized as a key computational technology, supporting traditional analysis, visualization, and design tasks. Diverse applications in domains such as mineral prospecting, computer aided design, bioinformatics, and computational steering are now being viewed in the data mining framework. This has led to a very effective crossfertilization of computational techniques from both continuous and discrete perspectives. In this chapter, we characterize the nature of scientific data mining activities and identify dominant recurring themes. We discuss algorithms, techniques, and methodologies for their effective application and present application studies that summarize the state-of-the-art in this emerging field. We conclude by identifying opportunities for future