Defining Interaction Design Patterns to Extract Knowledge from Big Data

[EN] The Big Data domain offers valuable opportunities to gain valuable knowledge. The User Interface (UI), the place where the user interacts to extract knowledge from data, must be adapted to address the domain complexities. Designing UIs for Big Data becomes a challenge that involves identifying and designing the user-data interaction implicated in the knowledge extraction. To design such an interaction, one widely used approach is design patterns. Design Patterns describe solutions to common interaction design problems. This paper proposes a set of patterns to design UIs aimed at extracting knowledge from the Big Data systems data conceptual schemas. As a practical example, we apply the patterns to design UI s for the Diagnosis of Genetic Diseases domain since it is a clear case of extracting knowledge from a complex set of genetic data. Our patterns provide valuable design guidelines for Big Data UIs.The authors thank the members of the PROS Center's Genome group for fruitful discussions. In addition, it is also important to highlight that Secretaria Nacional de Educacion, Ciencia y Tecnologia (SENESCYT) and Escuela Politecnica Nacional from Ecuador have supported this work. This project also has the support of Generalitat Valenciana through project IDEO (PROMETEOII/2014/039) and Spanish Ministry of Science and Innovation through project DataME (ref: TIN2016-80811-P).Iñiguez Jarrín, CE.; Panach Navarrete, JI.; Pastor López, O. (2018). Defining Interaction Design Patterns to Extract Knowledge from Big Data. Springer. 490-504. https://doi.org/10.1007/978-3-319-91563-0_30S490504Power, D.J.: ‘Big Data’ decision making use cases. In: Delibašić, B., Hernández, J.E., Papathanasiou, J., Dargam, F., Zaraté, P., Ribeiro, R., Liu, S., Linden, I. (eds.) ICDSST 2015. LNBIP, vol. 216, pp. 1–9. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18533-0_1Genetic Alliance: Capítulo 2, Diagnóstico de una enfermedad genética (2009). https://www.ncbi.nlm.nih.gov/books/NBK132200/Pabinger, S., et al.: A survey of tools for variant analysis of next-generation genome sequencing data. Brief Bioinform. 15(2), 256–278 (2014). https://doi.org/10.1093/bib/bbs086Borchers, J.O.: Pattern approach to interaction design. In: Proceedings of the Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, DIS 2000, pp. 369–378 (2000)Tidwell, J.: Designing Interfaces, vol. XXXIII, no. 2. O’Reilly Media, Sebastopol (2012)Van Duyne, D.K., Landay, J.A., Hong, J.I.: The Design of Sites: Patterns, Principles, and Processes for Crafting a Customer-Centered Web Experience. Addison-Wesley, Boston (2003)Schmettow, M.: User interaction design patterns for information retrieval. In: EuroPLoP 2006, pp. 489–512 (2006)IBM big data use cases – What is a big data use case and how to get started - Exploration. http://www-01.ibm.com/software/data/bigdata/use-cases.htmlDatamer e-book: Top Five High-Impact Use Cases for Big Data Analytics (2016). https://www.datameer.com/pdf/eBook-Top-Five-High-Impact-UseCases-for-Big-Data-Analytics.pdfBig Data Uses Cases | Pentaho. http://www.pentaho.com/big-data-use-casesHenderson-Sellers, B., Ralyté, J.: Situational method engineering: state-of-the-art review. J. Univers. Comput. Sci. 16(3), 424–478 (2010)Iñiguez-Jarrin, C., García, A., Reyes, J.F., Pastor, O.: GenDomus: interactive and collaboration mechanisms for diagnosing genetic diseases. In: ENASE 2017 - Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering, Porto, Portugal, 28–29 April 2017, pp. 91–102 (2017). https://doi.org/10.5220/0006324000910102Román, J.F.R., López, Ó.P.: Use of GeIS for early diagnosis of alcohol sensitivity. In: Proceedings of the BIOSTEC 2016, pp. 284–289 (2016). https://doi.org/10.5220/0005822902840289Laskowski, N.: Ten big data case studies in a nutshell. http://searchcio.techtarget.com/opinion/Ten-big-data-case-studies-in-a-nutshellMolina, P.J., Meliá, S., Pastor, O.: JUST-UI: a user interface specification model. In: Kolski, C., Vanderdonckt, J. (eds.) Computer-Aided Design of User Interfaces III, pp. 63–74. Springer, Dordrecht (2002). https://doi.org/10.1007/978-94-010-0421-3_

Improvement of usability in user interfaces for massive data analysis: an empirical study

[EN] Big Data challenges the conventional way of analyzing massive data and creates the need to improve the usability of existing user interfaces (UIs) in order to deal with massive amounts of data. How the UIs facilitate the search for information and helps in the end-user's decision-making depends on developers and designers, who have no guides for producing usable UIs. We have proposed a set of interaction patterns for designing massive data analysis UIs by studying 27 real case studies of massive data analysis. We evaluate if the proposed patterns improve the usability of the massive data analysis UIs in the context of literature search. We conducted two replications of the same controlled experiment, one with 24 undergraduate students experienced in scientific literature search and the other with eight researchers who are experienced in biomedical literature search. The experiment, which was planned as a repeated measures design, compares UIs that have been enhanced with the proposed patterns versus original UIs in terms of three response variables: effectiveness, efficiency, and satisfaction. The outcomes show that the use of interaction patterns in UIs for massive data analysis yields better and more significant effects for the three response variables, enhancing the discovery and visualization of the data. The use of the proposed interaction design patterns improves the usability of the UIs that deal with massive data. The patterns can be considered as guides for helping designers and developers to design usable UIs for massive data analysis web applications.The authors thank the members of the PROS Center Genome group for productive discussions. In addition, it is also important to highlight that the Secretaría Nacional de Educación, Ciencia y Tecnología (SENESCYT) and the Escuela Politécnica Nacional from Ecuador have supported this work. This project has also been developed with the financial support of the Spanish State Research Agency and the Generalitat Valenciana, under the projects TIN2016-80811-P and PROMETEO/2018/176, and co-financed with ERDF.Iñiguez-Jarrín, C.; Panach, JI.; Pastor López, O. (2020). Improvement of usability in user interfaces for massive data analysis: an empirical study. Multimedia Tools and Applications. 79(17-18):12257-12288. https://doi.org/10.1007/s11042-019-08456-6S12257122887917-18Borchers JO (2000) “Interaction Design Patterns : Twelve Theses,” Position Pap. CHI Work. “‘Pattern Lang. INteractoin Des. Build. Momentum,’”Borchers J (2009) The aachen media space: design patterns for augmented work environments, in Designing User Friendly Augmented Work Environments, Springer, 2009, pp. 261–312.Borchers J, Buschmann F (2001) A pattern approach to interaction design. WileyCohen J (1988) Statistical power analysis for the behavioral sciences 2nd edn. 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Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

StructMatrix: large-scale visualization of graphs by means of structure detection and dense matrices

Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at large-scale. Hence, this paper describes StructMatrix, a methodology aimed at high-scalable visual inspection of graph structures with the goal of revealing macro patterns of interest. StructMatrix combines algorithmic structure detection and adjacency matrix visualization to present cardinality, distribution, and relationship features of the structures found in a given graph. We performed experiments in real, large-scale graphs with up to one million nodes and millions of edges. StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and DBLP) have characterizations that reflect the nature of their corresponding domains; our findings have not been seen in the literature so far. We expect that our technique will bring deeper insights into large graph mining, leveraging their use for decision making.Comment: To appear: 8 pages, paper to be published at the Fifth IEEE ICDM Workshop on Data Mining in Networks, 2015 as Hugo Gualdron, Robson Cordeiro, Jose Rodrigues (2015) StructMatrix: Large-scale visualization of graphs by means of structure detection and dense matrices In: The Fifth IEEE ICDM Workshop on Data Mining in Networks 1--8, IEE

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201