FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2894167989> ?p ?o ?g. }

• W2894167989 endingPage "180" @default.
• W2894167989 startingPage "166" @default.
• W2894167989 abstract "In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making." @default.
• W2894167989 created "2018-10-05" @default.
• W2894167989 creator A5008149778 @default.
• W2894167989 creator A5016219620 @default.
• W2894167989 creator A5032694211 @default.
• W2894167989 creator A5042382359 @default.
• W2894167989 creator A5047912015 @default.
• W2894167989 creator A5057538550 @default.
• W2894167989 creator A5057840369 @default.
• W2894167989 creator A5076632872 @default.
• W2894167989 date "2018-09-01" @default.
• W2894167989 modified "2023-10-10" @default.
• W2894167989 title "Guidance in the human–machine analytics process" @default.
• W2894167989 cites W1924369998 @default.
• W2894167989 cites W1935234898 @default.
• W2894167989 cites W1961845056 @default.
• W2894167989 cites W1966950823 @default.
• W2894167989 cites W1969862674 @default.
• W2894167989 cites W1975424506 @default.
• W2894167989 cites W1978469498 @default.
• W2894167989 cites W1983909484 @default.
• W2894167989 cites W1987135115 @default.
• W2894167989 cites W1990401068 @default.
• W2894167989 cites W2001084593 @default.
• W2894167989 cites W2001487069 @default.
• W2894167989 cites W2013996050 @default.
• W2894167989 cites W2031746736 @default.
• W2894167989 cites W2033074184 @default.
• W2894167989 cites W2058203255 @default.
• W2894167989 cites W2071376492 @default.
• W2894167989 cites W2073800769 @default.
• W2894167989 cites W2090687959 @default.
• W2894167989 cites W2091236139 @default.
• W2894167989 cites W2103776060 @default.
• W2894167989 cites W2105552354 @default.
• W2894167989 cites W2114651926 @default.
• W2894167989 cites W2115452613 @default.
• W2894167989 cites W2116436752 @default.
• W2894167989 cites W2121557515 @default.
• W2894167989 cites W2125775272 @default.
• W2894167989 cites W2130736456 @default.
• W2894167989 cites W2136614128 @default.
• W2894167989 cites W2141033859 @default.
• W2894167989 cites W2142493242 @default.
• W2894167989 cites W2150085383 @default.
• W2894167989 cites W2157379557 @default.
• W2894167989 cites W2161133721 @default.
• W2894167989 cites W2169429690 @default.
• W2894167989 cites W2470432365 @default.
• W2894167989 cites W2488113179 @default.
• W2894167989 cites W2511469011 @default.
• W2894167989 cites W2516807252 @default.
• W2894167989 cites W2556734596 @default.
• W2894167989 cites W2562236173 @default.
• W2894167989 cites W2592540486 @default.
• W2894167989 cites W2752059862 @default.
• W2894167989 cites W2761004920 @default.
• W2894167989 cites W2768531527 @default.
• W2894167989 cites W2788844524 @default.
• W2894167989 cites W2795376812 @default.
• W2894167989 cites W2798663387 @default.
• W2894167989 cites W2886531750 @default.
• W2894167989 cites W2888660171 @default.
• W2894167989 cites W2894490168 @default.
• W2894167989 cites W2896743066 @default.
• W2894167989 cites W2898033571 @default.
• W2894167989 cites W2905763413 @default.
• W2894167989 cites W2906194767 @default.
• W2894167989 cites W2906577465 @default.
• W2894167989 cites W3138773240 @default.
• W2894167989 cites W4230275437 @default.
• W2894167989 cites W4250128948 @default.
• W2894167989 doi "https://doi.org/10.1016/j.visinf.2018.09.003" @default.
• W2894167989 hasPublicationYear "2018" @default.
• W2894167989 type Work @default.
• W2894167989 sameAs 2894167989 @default.
• W2894167989 citedByCount "55" @default.
• W2894167989 countsByYear W28941679892019 @default.
• W2894167989 countsByYear W28941679892020 @default.
• W2894167989 countsByYear W28941679892021 @default.
• W2894167989 countsByYear W28941679892022 @default.
• W2894167989 countsByYear W28941679892023 @default.
• W2894167989 crossrefType "journal-article" @default.
• W2894167989 hasAuthorship W2894167989A5008149778 @default.
• W2894167989 hasAuthorship W2894167989A5016219620 @default.
• W2894167989 hasAuthorship W2894167989A5032694211 @default.
• W2894167989 hasAuthorship W2894167989A5042382359 @default.
• W2894167989 hasAuthorship W2894167989A5047912015 @default.
• W2894167989 hasAuthorship W2894167989A5057538550 @default.
• W2894167989 hasAuthorship W2894167989A5057840369 @default.
• W2894167989 hasAuthorship W2894167989A5076632872 @default.
• W2894167989 hasBestOaLocation W28941679891 @default.
• W2894167989 hasConcept C199360897 @default.
• W2894167989 hasConcept C2522767166 @default.
• W2894167989 hasConcept C41008148 @default.
• W2894167989 hasConcept C79158427 @default.
• W2894167989 hasConcept C98045186 @default.
• W2894167989 hasConceptScore W2894167989C199360897 @default.

• next