This thesis describes a notation for rapid specification of information visualization, which can be used as a theoretical framework of integrating various types of information visualization, and its applications at a conceptual level. The notation is devised to codify the major characteristics of data/visual structures in conventionally-used data/information visualizations in business and statistics domains. It consists of unary and binary operators that can be combined to represent a visualization and each operator is divided into two major parts: data manipulation and conceptual representation. The data manipulation part indicates internal data operations required to visualize data. The conceptual representation part is to contain conceptual meanings of data in a visualization. Capturing structural features of a visualization, our notation can express a data visualization at an abstract level and be applied to match or compare two visualizations. Integrating data/information visualization into one framework is one of the unresolved problems in information visualization community. The major contribution of this work lies in formalizing the notation and its operator rules in the limited context. Our notation does not cover all types of visualization. Instead, it is limited to visualization types that has expressible data characteristics in the context of business and statistics data visualization domains. It is designed as a high-level abstraction for rapid specification of a visualization rather than a complete description of a visualization. It cannot be used as a complete description alone. Also, it is an only descriptive notation and not generative. The focus of this thesis is the development of the notation. First, the design of the major operators is discussed as we present their underlying concepts and define rules of operator equivalence and transformation. Second, to evaluate how expressive the notation is, we explore some commonly-used data visualizations. Last, to show usefulness of the notation, we take two applications, similarity measurement and visualization alternative generation, and show a protoype implementing them. In the similarity measurement, two given visualizations are converted into operator-based notation strings in a full binary tree format and compared with each other in terms of Levenshtein Edit Distance (LED). In the visualization alternative generation, for two given source and target notation expressions, a transformation mechanism and rules are developed and generates visualization alternatives for the source notation expression. The benefits of our approach are as follows: First, since the notation is a high-level abstraction of a visualization, it can focus on a user's conceptual intension better than a detailed description of a visualization. Second, the operators define a set of required capabilities on which a visualization system can be organized. Thus, the notation can be used to design a system that interconnects various data visualization tools by sending and receiving visualization requests between them. Third, it can be used to compare visualizations or to find/generate similar visualizations to a given one. User guidance/recommendations for naive users can be designed. For example, a user's request for a visualization can be compared with data visualization tools' presentation capabilities and the most appropriate ones for the user can be suggested. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]