Accepted HICSS, 2013 © 2012 Oculus Info Inc. Aperture: An Open Web 2.0 Visualization Framework David Jonker Oculus Info Inc. djonker@oculusinfo.com Scott Langevin Oculus Info Inc. slangevin@oculusinfo.com Neil Bozowsky Oculus Info Inc. nbozowsky@oculusinfo.com William Wright Oculus Info Inc. bwright@oculusinfo.com Abstract Aperture is an open, adaptable and extensible Web 2.0 visualization framework, designed to produce visualizations for analysts and decision makers in any common web browser. Aperture utilizes a novel layer based approach to visualization assembly, and a data mapping API that simplifies the process of adaptable transformation of data and analytic results into visual forms and properties. This common visual layer and data mapping API, combined with core elements such as contextually derivable color palettes, layout and symbol ontology services is designed to enable highly creative and expressive visual analytics, rapidly and with less effort. This paper introduces the Aperture framework, describing key features of the programming API and reference implementation, presents example use cases, and proposes an approach for measuring technical performance metrics for software development, and operational performance metrics for visualization support of analysis and decision making. 1. Introduction To achieve optimal information visualization and visual analytic solutions [1], [2], rapidly and with less effort, we have designed and developed an open, adaptable and extensible software development framework, to produce visualization applications for analysts and decision makers in any common web browser. This framework—its design, API and implementations—is called Aperture. Key to Aperture is a new layer based approach to visualization assembly, and a data mapping API that simplifies the process of adaptable transformation of data and analytic results into visual forms and properties. The Aperture framework and API are designed for ease of extension, allowing a broad community to leverage and extend capabilities and even to invent new paradigms, and interoperability with Web 2.0, Service Oriented Architecture (SOA) and geographic information system (GIS) standards. Our primary goal in designing Aperture was to support “limitless extensibility” (layering visualizations in unpredictable ways) for complex data problems, using a simple and intuitive programming grammar. Key constraints were that it be optimized for efficient dynamic updates, and runtime adaptive to varying levels of browser support for graphics APIs such as svg, vml and canvas. In the next section we provide brief background on the challenges in creating effective information visualizations and visual analytics. In Section 3 we present our objectives in designing and implementing the Aperture visualization framework. Section 4 discusses some examples of related work. Section 5 describes Aperture as both an architectural framework and reference implementation, with use case examples to illustrate the layer based visualization and data mapping APIs. An example application where Aperture was used to rapidly develop a tailored cyber situation awareness and analysis "big data" application (8 GB, 158M rows) is discussed in Section 6, and in Section 7 we conclude and suggest future work. 2. Background Visualization is an external mental aid that enhances cognitive abilities. When information is presented visually, efficient innate human capabilities can be used to perceive and process data. Information visualization techniques amplify cognition by increasing human mental resources, reducing search times, improving recognition of patterns, increasing inference making, and increasing monitoring scope [1], [3]. These benefits can translate into significant system and task related performance gains. Recently a new field has emerged called visual analytics which builds on information visualization and computational analytics. Visual analytics support analytical reasoning facilitated by interactive visual interfaces and integration with computational analytics. People, data and analytics work together in a visual system of systems to harness the respective strengths of each component. People use visual analytics tools and techniques to synthesize information and derive