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Background 2D baseline and follow-up clinical images are potentially subject to inconsistency due to alteration of imaging parameters. However, no study to date has attempted to quantify the magnitude by which such images can be influenced. Objective The objective of the present study is to identify the magnitude by which images can be influenced by changing the imaging light angle. Methods This study is based on the evaluation of 2D frontal images of the face and included a total of 51 subjects of which n = 14 were males and n = 37 were females. Faces were photographed at 0°, 30°, and 60° light angle under identical and standardized conditions. Images were randomized and rated by 27 blinded raters for age, facial attractiveness, body mass index (BMI), temporal hollowing, lower cheek fullness, nasolabial sulcus severity, and jawline contour. Results Facial attractiveness decreased, facial unattractiveness increased and the evaluated BMI (based on facial assessment) increased statistically significantly at 60°. The assessment of regional facial scores, i.e., temporal hollowing, lower cheek fullness, and jawline contour, showed no statistically meaningful changes both at 30° and at 60° light angle. Conclusion The results indicate that there might be an observed blind range in light angle (0°–30°) which does not influence facial assessment. Increasing the light angle past the threshold value to 60° might result in a statistically significant impact on facial perception which should be accounted for when documenting and/or presenting facial 2D images. Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

In a global market the capacity to mine and analyze user data is one way for companies to be as close in time and accuracy to the needs of their users. Big Data Platforms are one solution for companies to solve the necessary challenges to accomplish these capacities. Unfortunately the number of challenges that need to be addressed, allied with the high number of different solutions proposed, has led to the creation of a high number of different platforms making it hard to name one definitive and adequate platform for companies. In this paper we compare six of the most important Big Data Open Source Platforms to help companies or organizations choose the most adequate one to their needs. We analyze the following open source platforms - Apache Mahout, MOA, R Project, Vow pal Wabbit, PEGASUS and Graph Lab Create TM.