Data Farming Around the World Overview - Cornwallis Group

HORNE AND SCHWIERZ: DATA FARMING AROUND THE WORLD OVERVIEW 267Thus, the statistical approach, using efficient experimental designs is essential and workin this area has been performed at the Naval Postgraduate School (NPS) in Monterey,California and NPS researchers have collaborated with others worldwide as well (seeKleijman, Sanchez, Lucas, and Cioppa, 2005). Data Farming continues to evolve from initialProject Albert efforts (Hoffman and Horne, 1998) to the work documented in the latestedition of the Scythe (Horne and Meyer, 2009). This proceedings and bulletin of theInternational Data Farming Community contains the proceedings of the International DataFarming Workshops that have taken place since Project Albert ended and is put out by theSEED Center for Data Farming at NPS.QUESTION-BASEDOver the past few years several articles have captured the fundamentals of Data Farming (e.g.Horne and Meyer, 2005 and Lawler, 2005), but the key is the question basis. Over the pastdecade and as we shall present in the next section, over 120 international work teams haveformed around questions at International Data Farming Workshops. On the other hand, at theNaval Postgraduate School over 60 theses, many by international students, have beencompleted which have used data farming.These types of questions can never have precisely defined initial conditions and acomplete set of algorithms that describe the system being considered. These questionsaddress open systems that defy prediction. Data Farming is used to provide insight that canbe used by decision-makers. To accomplish this challenging task, Data Farming relies upontwo basic ideas:1. Use high performance computing (HPC) to execute models many times overvaried initial conditions to gain understanding of the possible outliers, trends,and distribution of results, and2. Develop models, called distillations, that are focused to specifically addressthe question.The discovery of surprises (both positive and negative) and potential options are madepossible by Data Farming, by providing the ability to process large parameter spaces.ITERATIVE PROCESSData Farming is a collaborative and iterative team process (Horne and Meyer 2004b). Figure1 describes the Data Farming process as a set of imbedded loops of iteration. This processnormally requires input and participation by subject matter experts, modelers, analysts, anddecision-makers.The “Scenario Building” loop shown on the left side of the Figure involves developingand honing a model that adequately represents the system that addresses the question beingasked by the decision-maker. This is an iterative process that often requires honing thequestion as well.