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

On Managing Contextual Knowledge of Digital Document Ecosystems, characterized by Alphanumeric Textual Data

The multidisciplinary textual-data are often disorganized and misinterpreted in many documents, which can obscure the information retrieval and its interpretation in company networks and even the World Wide Web. Managing textual information in particular with large-size alphanumeric data sources is challenging and at times can preclude the prompt delivery of good quality document services to diverse customers. Optimizing the words, sentences and alphanumeric characters of a script is the purpose of research, without losing intelligibility, semantics, perception, content flow and the contextual scenarios, represented as dimensions. We interpret the manuscript as a document ecosystem, within which different dimensions are construed. We choose different lexes, sentences, paragraphs and pages that possess frequent alphanumeric characters, interpreted in multiple domains and contexts. The ontologies of alphanumeric textual-data dimensions and their metaphors are presented in several data schemas, connecting various contexts of document ecosystems. The domain ontologies that can deliver text-mining, the semantic and schematic information of textual data, can expedite the textual-data integration process in the multidimensional warehouse modelling procedure. Diverse views and contexts that are generic within the document ecosystems are analysed for contextual knowledge. The ontologically structured document ecosystems that can facilitate more legibility and reproducibility to a variety of document designers are research outcomes. Data analysts, text mining experts and document managers can benefit the current research

Analysis of the Engineering Students' Self-Reflections Through Text Mining of Their Learning Statements

At the University of Oklahoma (OU), AME4163: Principles of Engineering Design which is based on the “Design, Build, and Test” approach and has an experiential learning structure, is a course for preparing senior undergraduate students with ability to adapt quickly to new circumstances as junior engineers. In order to measure students’ learning effectiveness, instructors require the students to write down their learning statements using a triple construct including experience, learning, and value. The learning statements submitted by the students are the linguistic embodiment of their engineering competencies through reflection on doing which is anchored in Kolb’s experiential learning cycle. There are two main issues facing instructors in manually assessing students’ learning statements: 1) labor-intensiveness due to the huge amount of text, and 2) subjectivity due to the instructors’ personal judgments. In this thesis, the hypothesis is that through the exploration in the text mining program, the things as follows facilitate instructors analyzing students’ learning statements. - Proposing a text mining framework to answer the development question. - Formalizing the heterogeneous text contents and building the schemed database in the framework - Adapting the machine learning methods to analyze and predict the data source. - Visualizing the text mining results for gaining more insights from the textual documents. - Automating a text mining program to integrate the functionalities of the framework. The intellectual merit of this thesis is the realization of the text mining program to gain insights from the linguistic learning statements. By incorporating machine learning methods to analyze the heterogeneous text contents, the text mining program can be updated by experiments, evaluation, and users’ feedbacks. Through the improvement of the text mining program, more and more visualization results and the corresponding insights can be generated