There's an awesome science side to this website, change the subject from math to science and I'm sure the people there would love to help.
will u give me a medal if i answer this?
Of course :) Message me and we can talk through there.
ok the asnwer is (Drumrol plzzz)
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Obviously, data management is critical for appropriate and effective utilization of large volumes of data. In scientific and engineering computing problems today there are considerable independent efforts for data collection and simulation. To properly understand a phenomenon of interest, one much consider a notion of data fusion, by which these disparate sources can be utilized. For example, such data may come from remotely-sensed or in-situ observations in the earth and space sciences seismic sounding of the earth for petroleum geophysics (or similar signal processing endeavors in acoustics/oceanography, radio astronomy, nuclear magnetic resonance, synthetic aperture radar, etc.) large-scale supercomputer-based models in computational fluid dynamics (e.g., aerospace, meteorology, geophysics, astrophysics), quantum physics and chemistry, etc. medical (tomographic) imaging (e.g., CAT, PET, MRI) computational chemistry genetic sequence mapping intelligence gathering geographic mapping and cartography census, financial and other "statistical" data The instrumentation technology behind these data generators is rapidly improving, typically much faster than the techniques available to manage and use the resultant data. In fact, an onslaught of orders of magnitude more data from these and other sources is expected over the next several years. Hence, greater cognizance of the impact of these data volumes is required. For example, NASA's Earth Observing System (Eos), which is planned for deployment by the end of the century, will have to receive, process and store up to ten TB (1013 bytes) of complex, interdisciplinary, multidimensional earth sciences data per day, for over a decade from a number of instruments. These data will be compared and utilized with models. Another example is the work under the US Department of Energy Accelerated Strategic Computing Initiative (ASCI) to shift from physical testing to computational-based methods for ensuring the safety, reliability and performance of the nuclear weapons stockpile. This requires the most advanced, state-of-the-art scientific simulations derived from a number of distinct codes. The results of such codes must be coupled and compared to the archived data from the physical testing. Given the fact that such data sets are or will be generated, what can be done to cope with this deluge from the perspective of their access, management and utilization? The ability to generate pictorial representations of data is currently in vogue as being the answer. This concept of (scientific) data visualization really implies a method of computing that gives visual form to complex data using graphics and imaging technology. It is based upon the notion that the human visual system has an enormous capacity for receiving and interpreting data efficiently. Despite the advancement of visualization techniques for scientific data over the last several years, there are still significant problems in bringing today's hardware and software technology into the hands of the typical scientist. Some of these same problems do occur in more general processing and analysis of scientific data in many disciplines. For example, data management is required to make such computing effective, which can be expressed by the need for a class of data models that is matched to the structure of scientific data as well how such data may be used. The critical component of data management is typically missing in many such computing efforts. When this concept is scaled to support large data sets (e.g., a few GB), several critical problems emerge in the access and use of such data, which brings the problem back to the data level. This circular reasoning does not imply that applications such as visualization are not relevant, but the challenge is more complex than simply flowing data from storage and looking at the pictures or even "pointing" back to the numbers behind the pictures. This requirement for scientific data models extends beyond the definition and support for well-defined physical formats. It must include the logical specification of self-documenting (including semantics) scientific data to be studied via a visualization system and data derived through the operation of the tools in such a system. Recently, considerable attention has also been placed on metadata support for the management of current and past large data streams because through it a scientist would be able to select data of interest for analysis. However, if adequate mechanisms for use of the actual data that meet the requirements and expectations of the scientific users of such data are not available, then the efforts to generate and archive the data and supporting metadata management will be for nought. Although it is beyond the scope of this discussion, advances in visualization and data structures can also be applied to metadata management in the form of browsing, support of spatial search and selection criteria, etc. What can be done? A place to begin is a discussion about solutions for how data should be organized, managed and accessed, which is often not adequately addressed in visualization and related software. The following is a survey of some of the methods and requirements for data management in visualization. It is hardly meant to be either exhaustive or definitive, but merely as an introduction to an important topic.
There u have ur answer
a little shorter lol.. like a paragraph..
i just copy pasted this
im sorry i just want a medal that all im only 10
so i dont know this