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Towards Design Principles for Visual Analytics in Operations Contexts Matthew Conlen ∗ Sara Stalla † Chelly Jin ‡

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Towards Design Principles for Visual Analytics in Operations Contexts Matthew Conlen ∗ Sara Stalla † Chelly Jin ‡ Towards Design Principles for Visual Analytics in Operations Contexts Matthew Conlen ∗ Sara Stalla † Chelly Jin ‡ Maggie Hendrie § Hillary Mushkin ¶ Santiago Lombeyda k Scott Davidoff ∗∗ ABSTRACT INTRODUCTION Operations engineering teams interact with complex data sys- Many tasks are routine, but their reliable performance is not tems to make technical decisions that ensure the operational always indicative of regularity in procedure. The term “opera- efficacy of their missions. To support these decision-making tions” often classifies tasks that require a calibrated response tasks, which may require elastic prioritization of goals depen- to complex variables governing a domain. For the purposes dent on changing conditions, custom analytics tools are often of this paper, we define operations as processes that 1) re- developed. We were asked to develop such a tool by a team at quire time-sensitive attention, 2) generate an action-oriented the NASA Jet Propulsion Laboratory, where rover telecom op- output, and 3) assess risk and impact across multiple scenar- erators make decisions based on models predicting how much ios. There are many domains that deal with operations issues, data rovers can transfer from the surface of Mars. Through including air traffic control, supply chain logistics, and search- research, design, implementation, and informal evaluation of and-rescue missions. As it is critical that operators are able to our new tool, we developed principles to inform the design make quick, deliberate, and thoughtful decisions, special tools of visual analytics systems in operations contexts. We offer are often needed to support their tasks. these principles as a step towards understanding the complex Spacecraft systems engineers at the NASA Jet Propulsion Lab- task of designing these systems. The principles we present are oratory manage the Opportunity rover on Mars. To support applicable to designers and developers tasked with building Meridian analytics systems in domains that face complex operations their work, we designed , a tool for analyzing pre- Opportunity challenges such as scheduling, routing, and logistics. dictions about the amount of data can transmit to overpassing satellites under changing conditions. Meridian drives rover path planning decisions efficiently, particularly ACM Classification Keywords critical given a hard drive failure that wipes Opportunity’s H.5.m. Information Interfaces and Presentation (e.g. HCI): data cache upon entering power-saving “nap” mode. Meridian Miscellaneous is also adaptable to shifting priorities, from maximizing data transfer to facilitating uplink at a certain location on the hori- zon. While only a handful of experts use Meridian, the needs Author Keywords met by this tool are ubiquitous in analogous contexts. operations; theory; design principles; design research methods; qualitative methods; visual design; visualization; The visualization community has contributed a number of contextual inquiry; information seeking & search systems that assist with operations tasks, but their design often targets domain specificities that seldom extend across contexts. To ensure that the lessons learned from work done across *University of Washington, [email protected] †Carnegie Mellon University, [email protected] these many domains are not overlooked, we developed a suite ‡University of California, Los Angeles, [email protected] of principles to guide the creation of visual analytics tools §ArtCenter College of Design, [email protected] supporting various operations tasks. ¶California Institute of Technology, [email protected] Through the design of Meridian — via direct observations, || California Institute of Technology, [email protected] simulations with paper- and code-based prototypes, and in- ** NASA Jet Propulsion Laboratory, [email protected] quiry into failed representations — we crafted strategies for Permission to make digital or hard copies of all or part of this work for personal or optimizing operations tasks. We submit the following princi- classroom use is granted without fee provided that copies are not made or distributed ples to facilitate an understanding of the particular challenges for profit or commercial advantage and that copies bear this notice and the full citation of designing for these contexts, and offer suggestions for pri- on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or oritizing focus and framing expected trade-offs. We believe republish, to post on servers or to redistribute to lists, requires prior specific permission these principles contribute to a growing scholarship on de- and/or a fee. Request permissions from [email protected]. signing for operations and the continued investigation of this CHI 2018, April 21–26, 2018, Montreal, QC, Canada space. © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-5620-6/18/04. $15.00 DOI: https://doi.org/10.1145/3173574.3173712 RELATED WORK smart home control [5], and Horvitz presented an influential set Research in visual analytics has seen a number of systems of principles for developing mixed-initiative user interfaces [7]. designed to help analysts across domains, from risk manage- Rule and Forlizzi distilled design principles for interfaces in ment, to enterprise decision management, to multi-objective multi-user, multi-robot systems [10]. optimization. These domains require a human operator to Our work adds to this conversation about designerly knowl- oversee complex processes, ensure they run nominally, and edge in visualization by contributing principles and best prac- make real-time decisions at important junctures. tices for visual analytics systems that focus on the less ex- For example, ViDX is a system for real-time monitoring and di- plored domain of operations. Wang et al. developed a two- agnostics of assembly line performance in smart factories [17], stage framework for designing visual analytics systems in a domain that requires quick decision making about auto- organizational environments [16]. The organizational domain mated factory processes. Malik et al. [8] offer a visual ana- has overlap with operations, given that there are competing lytics system to help coast guards allocate their maritime re- interests to be prioritized, and tasks may be time-sensitive sources and evaluate the risks associated with potential actions; and mission-critical. The framework presented, however, is Tomaszewski and MacEachren present a geovisual analytics very high-level and may be more useful for characterizing system in support of crisis management [13], discussing the the design process than for informing it. With the principles importance of providing context in visual analytics tools [12]. we present, we aim to help guide the design visual analytics Both domains require operational oversight of tasks of criti- systems for operations in practice. cal importance that deal with human lives. Another tool, the Decision Exploration Lab [2], targets operations problems generally, providing users with ways to represent abstract busi- MERIDIAN ness goals and create decision models to optimize them. The Meridian was designed for the Mars Exploration Rover team system is focused on automating decision-making tasks and at the NASA Jet Propulsion Laboratory. Operators working testing user-defined rules that can be used to perform relevant with Opportunity needed a way to analyze predictions about decisions. the amount of data the rover would be able to send back to Earth via an overpassing satellite. They then needed to relay While these systems address the specific challenges of their this information to other team members, including scientists particular verticals, we see them as representative of a more who may update their data collection plans, and path planners general category of tasks that we refer to as “operations.” The who may choose to take a path that optimizes for data transfer. operations tasks we examine share three characteristics: they require time-sensitive attention, generate an action-oriented Currently our method of evaluating our heading choices output, and assess risk and impact across multiple scenarios. is to open all of the plots on different computers or win- Under this definition of operations, we can find commonalities dows and to examine each one, moving them around the across problems, and as a discipline, look to understand how screen to compare next to each other. Sometimes we will one solution manifested in one domain might in some way even print out some of the plots and hold layered paper help solve similar problems in an analogous domain. up to the light to compare them together. Towards that end, rather than characterize our contribution — NASA operations engineer around the impact of the single artifact that we designed, we instead reflect on the design decisions we made. We present The design of Meridian began with field user research with not the what but the how, and look to use the knowledge gen- 4 spacecraft operations engineers (the entire active MER erated through making. This “Research through Design” [18] mission operations team). The 2-hour engagement included approach to visual analytics leads us to communicate the space semi-structured interview and artifact walkthrough using a of choices that we faced, and explore how these choices mani- Think Aloud protocol with the
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