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Interactive Data Mashups for User-Centric Data Analysis

Published: 27 August 2023 Publication History

Abstract

Nowadays, the amount of data is growing rapidly. Through data mining and analysis, information and knowledge can be derived based on this growing volume of data. Different tools have been introduced in the past to specify data analysis scenarios in a graphical manner, for instance, PowerBI, Knime, or RapidMiner. However, when it comes to specifying complex data analysis scenarios, e.g., in larger companies, domain experts can easily become overwhelmed by the extensive functionality and configuration possibilities of these tools. In addition, the tools vary significantly regarding their powerfulness and functionality, which could lead to the need to use different tools for the same scenario. In this demo paper, we introduce our novel user-centric interactive data mashup tool that supports domain experts in interactively creating their analysis scenarios and introduces essential functionalities that are lacking in similar tools, such as direct feedback of data quality issues or recommendation of suitable data sources not yet considered.

References

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Michael Behringer, Pascal Hirmer, Manuel Fritz, and Bernhard Mitschang. 2020. Empowering Domain Experts to Preprocess Massive Distributed Datasets. In Business Information Systems. Springer International Publishing, Cham, 61–75.
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Michael Behringer, Pascal Hirmer, and Bernhard Mitschang. 2018. A Human-Centered Approach for Interactive Data Processing and Analytics. In Enterprise Information Systems – 19th International Conference on Enterprise Information Systems, ICEIS 2017, Porto, Portugal, April 26-29, 2017, Revised Selected Papers. Springer International Publishing, Cham, 498–514.
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Michael Behringer, Pascal Hirmer, Dennis Tschechlov, and Bernhard Mitschang. 2022. Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1. INSTICC, SciTePress, 364–373.
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Michael Behringer, Dennis Treder-Tschechlov, Julius Voggesberger, Pascal Hirmer, and Bernhard Mitschang. 2023. SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1. INSTICC, SciTePress, 419–428.
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Cited By

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  • (2024)Enhancing Data Trustworthiness in Explorative Analysis: An Interactive Approach for Data Quality MonitoringSN Computer Science10.1007/s42979-024-02781-w5:5Online publication date: 20-Apr-2024

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cover image ACM Other conferences
SSDBM '23: Proceedings of the 35th International Conference on Scientific and Statistical Database Management
July 2023
232 pages
ISBN:9798400707469
DOI:10.1145/3603719
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2023

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Author Tags

  1. Data Analysis
  2. Data Mashup
  3. Data Preprocessing
  4. Data Quality
  5. Human-in-the-Loop

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  • Demonstration
  • Research
  • Refereed limited

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SSDBM 2023

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Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

View all
  • (2024)Enhancing Data Trustworthiness in Explorative Analysis: An Interactive Approach for Data Quality MonitoringSN Computer Science10.1007/s42979-024-02781-w5:5Online publication date: 20-Apr-2024

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