IEEE Trans Vis Comput Graph - How Can Visual Analytics Assist Investigative Analysis? Design Implications from an Evaluation.

Tópicos

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{ gene(2352) biolog(1181) express(1162) }
{ use(1733) differ(960) four(931) }
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{ method(1557) propos(1049) approach(1037) }
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{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
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{ ehr(2073) health(1662) electron(1139) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
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{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
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{ drug(1928) target(777) effect(648) }
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Resumo

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations on metrics and techniques for evaluating visual analytics systems for investigative analysis.

Resumo Limpo

despit grow number system provid visual analyt support investig analysi empir studi potenti benefit system conduct particular control compar evalu determin system foster insight sensemak import continu growth studi howev furthermor studi identifi peopl use system benefit can help inform design new system area conduct evalu visual analyt system jigsaw employ small investig sensemak exercis compar use three tradit method analysi sixteen particip perform simul intellig analysi task one four condit experiment result suggest jigsaw assist particip analyz data identifi embed threat describ differ analysi strategi use studi particip comput support lack thereof influenc strategi illustr sever characterist sensemak process identifi studi provid design implic investig analysi tool base thereon conclud recommend metric techniqu evalu visual analyt system investig analysi

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