Towards a Multi-parametric Visualisation Approach for Business Process Analytics

Bachhofner, Stefan; Kis, Isabella; Ciccio, Claudio Di; Mendling, Jan

doi:10.1007/978-3-319-60048-2_8

Cited by 7 publications

(15 citation statements)

References 13 publications

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“…This topic was discussed at the IEEE VIS 2014 conference during the "2D vs 3D" panel [208]. We already observe an increase in visualisation adoption (see Table 5 and Figure 28 on page 44) whereby newer publications are being published in non-visualisation journals, but still focus on visualisation [44,45,52,68,69,72,73]. This is a reliable indicator of adoption as business oriented journals are publishing visualisation centred papers.…”

Section: Discussion and Observationsmentioning

confidence: 90%

“…Wright [47] Proprietary Case Study from portfolio management, derivatives management, customer credit scores Gresh and Kelton [78] Proprietary Private IBM business by-product data Eick [79] Proprietary Log data from web servers used to analyse the efficiency of their website Burkhard [23] Proprietary Case study from Swiss Federal Institute of Technology using business strategy data Vliegen et al [48] Proprietary Unspecified business data Keim et al [80] Proprietary Transaction datasets Otsuka et al [20] Proprietary Digital nametags collect employee interaction data Sedlmair et al [24] Survey Existing software evaluation Kandel et al [18] Proprietary Interview Study with industry experts Du et al [70] Survey A survey of business process visualisation literature Aigner [25] Proprietary Text from interview study Broeksema et al [71] Proprietary Decision model data Bai et al [49] Proprietary Geospatial data for utility network coverage Lafon et al [26] Proprietary User Study of unspecified business data visualisation Nicholas et al [50] Proprietary Private customer survey database from automotive company Roberts et al [51] Proprietary Private call centre interaction database Ghooshchi et al [72] Proprietary Business Processes from undefined source Kumar and Belwal [52] Public Multiple public data sources looking at different aspects of a business Bachhofner et al [73] Proprietary Business processes from industry contacts Lea et al [74] Proprietary Business process data was used alongside simulated data to test prototypes Roberts et al [53] Proprietary Call centre event data from industry partner…”

Section: Classification Paper Ref Access Descriptionmentioning

confidence: 99%

“…The secondary classification used in the survey is shown in Table 1. Aigner [25] Broeksema et al [71] Bai et al [49] Lafon et al [26] Ferreira et al [54] Basole et al [58] Hao et al [42] 2014 Nicholas et al [50] Basole [76] Deligiannidis and Noyes [59] Basole and Bellamy [77] Lu et al [35] Shi et al [36] Rodden [84] Yaeli et al [21] Saitoh [43] 2015 Keahey [29] Basole et al [60] Dou et al [32] Kameoka et al [66] Nair et al [85] 2016 Roberts et al [51] Liu et al [81] Iyer and Basole [61] Basole et al [31] Wu et al [67] Nagaoka et al [22] Sijtsma et al [37] 2017 Ghooshchi et al [72] Kumar and Belwal [52] Bachhofner et al [73] Ramesh et al [34] Schotter et al [62] Kang et al [69] Fayoumi et al [44] 2018 Lea et al [74] Roberts et al [53] Basole et al [63] Sathiyanarayanan et al [68] Haleem et al [45] Saga and Yagi [46] Primary Data as Intentional, Active Digital Collection (CB): The following research conveys geo-location data collected through hardware that is used to track customer be...…”

Section: Customer Behaviour (Cb)mentioning

confidence: 99%

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Visualising Business Data: A Survey

Roberts

Laramee

2018

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A rapidly increasing number of businesses rely on visualisation solutions for their data management challenges. This demand stems from an industry-wide shift towards data-driven approaches to decision making and problem-solving. However, there is an overwhelming mass of heterogeneous data collected as a result. The analysis of these data become a critical and challenging part of the business process. Employing visual analysis increases data comprehension thus enabling a wider range of users to interpret the underlying behaviour, as opposed to skilled but expensive data analysts. Widening the reach to an audience with a broader range of backgrounds creates new opportunities for decision making, problem-solving, trend identification, and creative thinking. In this survey, we identify trends in business visualisation and visual analytic literature where visualisation is used to address data challenges and identify areas in which industries use visual design to develop their understanding of the business environment. Our novel classification of literature includes the topics of businesses intelligence, business ecosystem, customer-centric. This survey provides a valuable overview and insight into the business visualisation literature with a novel classification that highlights both mature and less developed research directions.

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Section: Discussion and Observationsmentioning

confidence: 90%

Section: Classification Paper Ref Access Descriptionmentioning

confidence: 99%

Section: Customer Behaviour (Cb)mentioning

confidence: 99%

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Visualising Business Data: A Survey

Roberts

Laramee

2018

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“…The following study was retrieved about the context of the application. Bachhofner et al [49] presented a multiparametric visualization approach to address the lack of visualization principles applied to business process analytics. These principles were used to create, evaluate, and improve the approach in designing a process.…”

Section: Business Processmentioning

confidence: 99%

A Systematic Review of Recommendations of Long-Term Strategies for Researchers Using Data Science Techniques

2021

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The impact of the strategies that researchers follow to publish or produce scientific content can have a long-term impact. Identifying which strategies are most influential in the future has been attracting increasing attention in the literature. In this study, we present a systematic review of recommendations of long-term strategies in research analytics and their implementation methodologies. The objective is to present an overview from 2002 to 2018 on the development of this topic, including trends, and addressed contexts. The central objective is to identify data-oriented approaches to learn long-term research strategies, especially in process mining. We followed a protocol for systematic reviews for the engineering area in a structured and respectful manner. The results show the need for studies that generate more specific recommendations based on data mining. This outcome leaves open research opportunities from two particular perspectives—applying methodologies involving process mining for the context of research analytics and the feasibility study on long-term strategies using data science techniques.

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“…The military can use this information to protect embassies in the case of an emergency or in the case of a natural disaster. Consequently, point clouds derived form tri-stereo satellite imagery can bring value for companies and public institutions alike, which can incorporate it in the life cycles of their business processes [77], in, e.g., process monitoring [78,79], process compliance [80], process conformance checking [81,82], process mining [83][84][85][86], and performance mining [87][88][89].…”

Section: Introductionmentioning

confidence: 99%

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

et al. 2020

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We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.

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Towards a Multi-parametric Visualisation Approach for Business Process Analytics

Cited by 7 publications

References 13 publications

Visualising Business Data: A Survey

Visualising Business Data: A Survey

A Systematic Review of Recommendations of Long-Term Strategies for Researchers Using Data Science Techniques

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

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