Visual Analytics for Model Selection in Time Series Analysis

Visualization System Requirements for Data Processing Pipeline Design and Optimization

“…Each colored cell indicates a requirement that is important for a given case study/paper, and an 'x' indicates that the requirement remains unmet by users' current tools. [25] req req req req req Chemical process models [26] x req req x Economic modelling [27] req req xx req req x req Aircraft engine design [28] req req x req req Phylogenetic trees [29,30] x req x x req req Molecular evolution [31,32] x x req x req x * Raidou et al [75] iCoCooN req req req * Ruppert et al [46] --x req x Luboshik et al [38] --x req req req Bruckner et al [10] --x req x req * Beham et al [43] Cupid req req xx Konyha et al [39] --x req x Pretorius et al [12] Paramorama req x req Afzal et al [36] RVF x req req x Bergner et al [13] ParaGlide xx req Torsney-Weir et al [33] Tuner x req x x * Padua et al [11] --x req x * Bögl et al [42] TiMoVA req req req x x req Spence et al [73] --req req Berger et al [9] --req x xx Piringer et al [40] HyperMoVal xx req req req Matkovic et al [17] --req x Coffey et al [34] --x x req Matkovic et al [50] --x Potter et al [49] Ensemble-Vis x req Booshehrian et al [19] Vismon req x req x Brecheisen et al [52] --x req x Unger et al [54] --req req req Amirkhanov et al [74] --x Marks et al [48] Design Galleries req x Waser et al [18] World Lines req x x * Martins et al [35] --x req x * Wu [53] --xx x Guo et al [41] --req…”

“…Sometimes the pipeline is wellestablished (e.g., [38], [39]), but in other situations users need to choose between algorithms (e.g., the 3D image segmentation case study in Sec. 4.2), improve the sophistication of an existing pipeline [17], [40], make a pipeline robust to the characteristics of the input data [12], or design the pipeline from scratch [27], [41], [42]. The latter is particularly true in exploratory analysis, where users are analyzing a new form of data or looking for new patterns (e.g., [9], [10], [11], [43]).…”

“…Exemplars include the chemical processes case study, the provision of many options in an environmental flooding scenario [18], and. Alternatively, the pipeline may contain a number of 'what if' branches [42]. Sometimes individual parameters are easy for users to understand (e.g., the number of clusters for k-means), but other parameters are abstractions of physical systems [34] or the parameters are difficult to relate to output consequences [11], [46].…”

“…However, although capturing provenance is a central tenet of good data analysis practice, it is not often stated as an explicit requirement. Notable exceptions are [36], [42].…”

“…The support ranges from iterative user-defined selection of model parameters [42], [46], to automated parameter selection (e.g., by random or regular sampling [12], [13], [29]). Moreover, hybrid approaches combine user steering and automatic parameter value sampling [63].…”