Data management challenges in modern astronomy research

Space Data

While technological advances have helped and assisted mankind in nearly all of his endeavors, the burdens associated with its applications cannot be ignored or misinterpreted. This problem is glaring in the case of Astronomy and its ability to use technology to gather enormous amounts of data. This stockpile of nearly 60 petabytes (PB) of data available to these scientists have revealed that humanity cannot keep up with the pace of acquiring data.

The purpose of this essay is to discuss the issues with data collection and performance degradation in the subject of astronomy. The essay will first examine how data is collected and how this process has led us to this problem. The next section discussed in this essay will address the emerging technologies that are becoming available that might alleviate the situation. Finally the writing will address the costs associated with fixing this problem and the general outlook of the practicality of the solutions that are being applied to the case.

Data Gathering Techniques

Berriman & Groom's (2011) argument suggested that the availability of large amounts of data have " transformed research in astronomy and the STScI now reports that more paper are published with archived data sets than with newly acquired data, (p.1). Astronomy as a practice is being threatened due to extreme degradation in acquiring real time information and applying it in a useful and practical manner.

Technology is truly a Pandora's box. In this case, technology has allowed for the gathering of so much information and data, covering an immense span of time and distance, there is no way that the current structure of astronomical study can fully come to grips with what they are gathering. The scenario is changing too fast for humans to keep up. The changing nature of the cosmos is not waiting for human kind to keep up and nature appears to keep producing and evolving whether we can model these changes or not. As a result the science itself appears to becoming somewhat obsolete if this tsunami of data is somehow incorporated in a useful manner that helps bring reason and understanding to the situation.

Emerging Technologies

Wall (2010) wrote that "the challenge will be for astronomers to sift through and follow up on the instrument's observations. Researchers are just now figuring out how they might be able to tackle such a job. Universities and colleges need to train future astronomers more fully in computer science and data management, in the short-term, astronomers should team up with computer scientists." Graphic processing units (GPUs) seem to have some of the answers to help reduced the clutter. GPUs were designed for manipulating graphics in large scale operations. GPU's are designed with much more processing power than CPUs and use astronomical algorithms to help decode and visual produce data into a palpable means of modeling.

Cloud computing is also another option in helping reducing the data overload in astronomy. Some alteration of the cloud systems must be addressed before its potential can be fully realized in this branch of study; " applications that are I/O intensive, which in astronomy often involve processing large quantities of image data, are, however, uneconomical to run because of high cost of data transfer and storage," (Berriman & Groom, p.5). The cloud may be of assistance but it may only be a more superficial approach unless certain more subtle aspects of the problem are addressed and treated beforehand.

Reducing the Costs of Data

The costs of humanity's astronomical pursuits are great and troubling and span over a vast amount of resources. Reducing costs associated with the availability of data due to technology must be addressed in principle firsts. This starts with the educational and learning systems associated with the science itself. Perhaps astronomy in its current state cannot be actually considered a science anymore due to the impossibility of real time and measurable experiment, the backbone of the scientific method.