Paper Number
1278
Paper Type
Completed
Description
Often the textual data are either disorganized or misinterpreted because of unstructured Big Data in multiple dimensions. Managing readable textual alphanumeric data and its analytics is challenging. In spatial dimensions, the facts can be ambiguous and inconsistent, posing interpretation and new knowledge discovery challenges. The information can be wordy, erratic, and noisy. The research aims to assimilate the data characteristics through Information System (IS) artefacts that are appropriate to data analytics, especially in application domains that involve big data sources. Data heterogeneity and multidimensionality can make and preclude IS-guided veracity models in the data integration process, including customer analytics services. The veracity of big data thus can impact visualization and value, including knowledge enhancement in the vast amount of textual data qualitatively. The manner the veracity features construed in each schematic, semantic and syntactic attribute dimension in several IS artefacts and relevant documents can enhance the readability of textual data robustly.
Recommended Citation
Naik, Neelam; Nimmagadda, Shastri; Purohit, Seema; Reiners, Torsten; and Mani, Dr Neel, "Information System Articulation Development - Managing Veracity Attributes and Quantifying Relationship with Readability of Textual Data" (2023). ICIS 2023 Proceedings. 21.
https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/21
Information System Articulation Development - Managing Veracity Attributes and Quantifying Relationship with Readability of Textual Data
Often the textual data are either disorganized or misinterpreted because of unstructured Big Data in multiple dimensions. Managing readable textual alphanumeric data and its analytics is challenging. In spatial dimensions, the facts can be ambiguous and inconsistent, posing interpretation and new knowledge discovery challenges. The information can be wordy, erratic, and noisy. The research aims to assimilate the data characteristics through Information System (IS) artefacts that are appropriate to data analytics, especially in application domains that involve big data sources. Data heterogeneity and multidimensionality can make and preclude IS-guided veracity models in the data integration process, including customer analytics services. The veracity of big data thus can impact visualization and value, including knowledge enhancement in the vast amount of textual data qualitatively. The manner the veracity features construed in each schematic, semantic and syntactic attribute dimension in several IS artefacts and relevant documents can enhance the readability of textual data robustly.
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13-DataAnalytics