1. INTRODUCTION "It is a capital mistake to theorize before you have all the evidence. It distorts judgment." This quote is from the fictional master detective Sherlock Holmes, the protagonist of Sir Arthur Conan Doyle's detective novels (2004). Sherlock Holmes knew the importance of gathering data and analyzing it thoroughly before attempting to solve a crime, and this inductive method is still the hallmark of a good criminal investigator. Today, databases and archives abound with information on various crimes, information that if looked at holistically could often lead to the arrest of the offender. Unfortunately, just because data is available doesn't mean it can be easily analyzed. The amount of data in law enforcement databases has become so large that even a team of detectives of the caliber of Sherlock Holmes. he wouldn't be able to do as the experienced detective suggests and examine everything before drawing any conclusions. Modern law enforcement maintains huge databases on all types of information that could potentially lead to a breakthrough in an investigation. Crime locations, crime reports, weather data, offender descriptions, criminal profiles are all stored in huge databases around the world. The LAPD alone has documents containing information on more than thirteen million crimes (The Age of Big Data, 2013). If analyzed thoroughly, all or even some of this information could reveal patterns that would point investigators to one or more suspects in many cases. . Unfortunately, trying to discern patterns and similarities from these massive data sets would take humans decades, perhaps even centuries, and be a futile exercise. This is, however, a perfectly realistic effort…half of the paper…in-the-womb-assisted tracking and characterization of homicides and sexual assaults (CATCH) (Vol. 3722, pp. 250–260). doi:10.1117/12.342880 Kirwan, B., & Ainsworth, L. K. (1992). Guide to Business Analysis: The Business Analysis Working Group. London: CRC Press.Mena, J. (2003). Investigative data mining for security and criminal detection. Amsterdam: Butterworth-Heinemann. Mena, J. (2011). Machine learning forensics for law enforcement, security, and intelligence. Boca Raton, Florida: CRC Press.Mueller, S. (2013). Upgrading and Repairing PCs (21st ed.). Indianapolis, USA: QUE.Peretto, P. (1992). An introduction to neural network modeling. Cambridge, NY New York: Cambridge University Press. Priddy, K. L., & Keller, P. E. (2005). Artificial neural networks: an introduction. Bellingham, Wash: SPIE PressRegalado, A. (1999). Capture as CATCH can. MIT Technology Review, 102(2), 28.