Keywords: Knowledge integration, Information visualization, Data exploration. The discussion places the use of information visualization tools within the context of 21st century information and knowledge management; and includes an examination of the strengths and weaknesses of visualization tools, the opportunities their use exploits, and notable challenges to their practical use and implementation for knowledge integration. Information visualization is rooted in the practice of data exploration.
Data exploration is a process through which large amounts of data, located in disparate databases, are examined for structure, patterns and other characteristics to understand relationships and trends. The practice enables users to perform interactive investigations that lead to new insights about relationships in complex data. Information visualization enables the creation and exploration of large collections of data and allows for interactive exploration Stone, Visualization is particularly useful for exploratory analyses that provide the basis of knowledge integration activities.
It provides researchers with a mechanism to explore patterns, test hypotheses, discover exceptions and explain their findings.
Information visualization strengthens the ability of users to uncover phenomena that have been previously hidden. Information visualization combines several different research areas including scientific visualization, human-computer interaction, data mining, information design, cognitive psychology, visual perception and computer graphics Kerren et al.
Although information visualization developed its roots from the field of scientific visualization there are key distinguishing features that set the two apart. Scientific visualization also tends to look at realistic renderings of volumes, surfaces, and illumination sources Friendly, Information visualization generally references large scale collections of non-numerical information and focuses on abstract phenomena such as social relationships, political polls and economic trends.
Readings in Information Visualization: Using Vision to Think - Mackinlay Card - Google книги
This is critical for knowledge integration. Information Visualization For Knowledge Integration. Information visualization can be seen as the applied science that examines large amounts of data through visual representation Friendly, Through visualization techniques, users are able to analyze and gain a better understanding of the data Russell et al.
Information visualization amplifies human cognition in six basic ways. Information visualization is most effective in heightening the understanding when data contain an underlying structure that supports the inference that proximate items can be inferred as being similar, users are unfamiliar with the contents of the data or have limited understanding of the data structure, users have difficulty verbalizing necessary underlying information, and information is easier to recognize than describe.
However captivating, questions regarding the usefulness of information visualization for more analytical tasks e. Indeed, moving beyond visualization toward analytical interpretation is where significant knowledge integration support may lie. The emerging field of visual analytics addresses this task. Visual Analytics. It combines automated analysis with visualization in order to have a more effective understanding and reasoning with the existing data sets Keim et al.
As such, many decision makers are turning toward this field in order to address complex problems. Visual analytics enables users to synthesize information and gain insight from vague and sometimes conflicting data. With its focus on human interaction within massive, dynamically changing information spaces, visual analytics research concentrates on support for perceptual and cognitive operations that enable users to detect the expected and discover the unexpected in complex information space. Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data.
Information visualization itself forms part of the direct interface between user and machine. This process is akin to the knowledge integration process in which multiple ideas are synthesized into a single representation that is larger than the sum of its parts. It is likely then, that the capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the knowledge integration process.
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Visualization tools are used for a variety of tasks common tasks including: list generation and graphical display e. Whereas some tools use statistics analysis techniques as their basis, others use semantic analysis algorithms to analyze data Yang et al. Types of textual data can be divided into three categories: structured content e. Accordingly, tools can be distinguished by their use of different types of data inputs structured, unstructured, and hybrid , and different forms of visual outputs e.
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Not surprisingly, the commercial space is filled with a plethora of off the shelf visualization tools that range from simple desktop software packages to complex, web-based enterprise applications. This section presents several of the more prominent visualization tools based on the type of content used as input and highlights their relative strengths. Structured Content. The first category of visualization tools uses structured data as input.
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Content from data sources such as databases and bibliographies are included in this category. It is designed to simultaneously search multiple online databases such as Web of Science, Library of Congress , perform statistical and linguistic analyses to analyze the results for themes and patterns, and display thematic content through interactive visualizations. VantagePoint uses natural language processing algorithms to rapidly navigate through structured text to discover hidden relationships and patterns.
One key strength of VantagePoint is the ability to ask who, what, where, and when questions that enable organizational profiling and technology assessment Yang et al. Unstructured Content. The second category of visualization tools uses unstructured or abstract data as input. Content from data sources such as documents, email, reports, published articles, news stories, all are included in this category.
It is capable of analyzing millions of documents or numeric data points, and can perform case sensitive text analyses to differentiate compound names from common words.
OmniViz combines sophisticated statistical and textual analysis algorithms with a range of visualizations to facilitate a deeper understanding of data. Through the integrated analysis environment, users can visually analyze textual, numerical, categorical and sequential data.
OmniViz has been used in educational and a variety of research environments. TEMIS uses a symantic approach to extract, categorize and cluster textual data into contextual groups. Hybrid Content.
Aureka enables the research of full text data and uses concept mapping to reveal trends in the data. Note: Aureka is specifically designed to analyze patent data; though the principles are transferable to other environments. Practical Considerations. A major limitation of most visualization tools reveals either a limited connection to a theoretical background, or an over-reliance on theory without a direct connection to user needs Wohlfart et al.
It is the later, that presents a major concern for knowledge integration efforts. Connecting the available tools to an actual environment might help to make these tools more practical for knowledge integration tasks. Sample Data Management Environment. Consider a data management environment characterized by an increasing volume and complexity of data on program activities, a heightened interest in evidence-based reports on program impact, and a growing number of ad hoc information requests from various constituents.
Although much of this information is static historical , periodic updates to project data are made at regular and irregular intervals.
Information Visualization: Perception for Design
Coupled with the immense volume and varied sources, the data are a hybrid collection of structured and unstructured e. Further, because different individuals e. Current information sharing practices are sufficient to provide a basic understanding of data in this type of environment. They do not, however, facilitate knowledge integration. Nor do they support the efficient response to requests for information regarding program impact, strategic decision-making, or comprehensive program planning.
Arguably, a data management environment with 21 st century challenges but not 21 st century information and knowledge management practices, is limited. The use of advanced data exploration and visualization tools might address the limitations of current knowledge integration practices.
Although many features of commercial off the shelf tools are similar, they do vary in strengths and weaknesses. In general, tools that support unstructured data analysis and visualization are the most flexible Yang, However, the use of symantic algorithms in these tools can require significant staff training and an upfront investment in time and money. Table 1 below summarizes the strengths and weaknesses of the tools highlighted above.
Perceived Strength. Perceived Weakness. Bibliographic reference focus.
Additional configuration required for data sources not included in application. Analytic capabilities. List cleaning of large datasets can be challenging. Interactive visualization. Focused primarily on visualization, not analysis. Data extraction method. Limited visualization capabilities. Mapping, clustering, and citation analysis. Usability — difficult to understand labeling process. In addition, other factors limit the practical usability of information visualization tools. Toggle navigation. William Jesse, Jr. Since Bill has been an investor in and advisor to growth oriented product and service companies.
Bill has served on over 20 boards in the last 35 years. He is the author of several case studies that have been taught at Harvard and other leading business schools. Tony Jewitt. Tony has more than 20 years of experience in marketing, sales and business development within the information technology industry. Joseph P. Joe has and continues to serve on the boards of a number of companies and resides in Dallas, Texas. Pierluigi Zappacosta.
Jim Bartoo. Jim has more than 25 years of experience in the software and information technology industries.