Business ecosystems are complex, dynamic systems characterized by a multitude of entities, including companies, ventures, and technologies, as well as activities and trends. Understanding the state of business ecosystems is an increasingly critical strategic imperative for many decision makers, but it is a resource-intensive activity as relevant information sources are dispersed, often highly unstructured, and not integrated or curated to deliver actionable insights. In this research, we present the design and implementation of an interactive visual analytic system that integrates artificial intelligence and graph visualization techniques to augment decision makers’ understanding of the complex public narrative associated with business ecosystems entities. Our system is driven by a real-time content engine of 100,000+ global data sources including press releases, news articles, industry reports, analyst blogs in multiple languages organized across several domain-specific repositories. Following a user-specified query, the engine extracts both domain-agnostic and domain-specific entities and concepts for each document in the result set. We then model and visualize the resulting data as a dynamic, multipartite network and implement graph pruning algorithms and interactive data controls to enable users to interactively explore and discover the underlying business ecosystem from multiple perspectives. We illustrate and discuss the value of our system using representative use cases. Our study makes multiple contributions to visual decision support theory and practice, including mining unstructured data, constructing and interacting with knowledge graphs, and designing visual analytic tools for ecosystem intelligence. We conclude the study with implications and future research opportunities.

Asking pairwise comparison questions is common. Yet, we often find ourselves comparing apples and oranges --- the two entities of interest are not readily comparable. To understand how technologies can extend our capabilities to conduct pairwise comparisons during data analysis, we analyzed pairwise comparison questions collected from crowd workers and propose a taxonomy of pairwise comparisons. We demonstrate how the taxonomy can be adopted by incorporating pairwise comparison capabilities into Duo, a spreadsheet application that supports comparing two groups of records in a data table. Duo decomposes a pairwise comparison question into rules and showcases sloppy rules, a query technique for specifying pairwise comparisons. We conducted a user study comparing sloppy rules and natural language. The findings suggest that for easier pairwise comparison tasks, the two techniques are comparable in efficiency and preference and that for more difficult pairwise comparison tasks, sloppy rules allow faster specification and are more preferable.

Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specification: when one object group (i.e. a group of records in a data table) is specified, Duet recommends object groups that are similar to or different from the specified one; when two object groups are specified, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specification is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.

Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.

Many real-world datasets are large, multivariate, and relational in nature and relevant associated decisions frequently require a simultaneous consideration of both attributes and connections. Existing visualization systems and approaches, however, often make an explicit trade-off between either affording rich exploration of individual data units and their attributes or exploration of the underlying network structure. In doing so, important analysis opportunities and insights are potentially missed. In this study, we aim to address this gap by (1) considering visualizations and interaction techniques that blend the spectrum between unit and network visualizations, (2) discussing the nature of different forms of contexts and the challenges in implementing them, and (3) demonstrating the value of our approach for visual exploration of multivariate, relational data for a real-world use case. Specifically, we demonstrate through a system called Graphicle how network structure can be layered on top of unit visualization techniques to create new opportunities for visual exploration of physician characteristics and referral data. We report on the design, implementation, and evaluation of the system and effectiveness of our blended approach.

The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns across multiple participant timelines of mHealth data. It then combines novel interaction and visualization techniques to enable multifocus event sequence analysis, which allows health researchers to interactively define, explore, and compare groups of participant behaviors using event sequence combinations. Through summarizing insights gained from a pilot study with 20 behavioral and biomedical health experts, we discuss Chronodes's efficacy and potential impact in the mHealth domain. Ultimately, we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research.

Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.

Management of supply network risks is a critical competency for today's global enterprises. Current practices and tools, however, have limited capabilities and do not allow for systemic exploration of alternate risk strategies. We develop a computational model of risk diffusion in global supply networks that is grounded in techniques from complex systems, network analysis, and epidemiological risk modeling. We draw on a unique, curated dataset of firms, their supply networks, and financial risk in the global electronics industry. Specifically, we assess and visualize the impact of network structure on risk diffusion and supply network health, and determine the impact of visibility on reduction and potential mitigation of cascading risks. Our approach enables decision makers to identify risks and determine potential paths of their diffusion. In doing so, we advance our understanding of the design and development of computational risk management tools in a global supply network context.

Health care delivery processes consist of complex activity sequences spanning organizational, spatial, and temporal boundaries. Care is human-directed so these processes can have wide variations in cost, quality, and outcome making systemic care process analysis, conformance testing, and improvement challenging. We designed and developed an interactive visual analytic process exploration and discovery tool and used it to explore clinical data from 5784 pediatric asthma emergency department patients.

Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks. Existing tools, however, are predominantly query- or list-centric with limited interactive, exploratory capabilities. Guided by a field study of corporate analysts, we have designed and implemented dotlink360, an interactive visualization system that provides capabilities to gain systemic insight into the compositional, temporal, and connective characteristics of business ecosystems. dotlink360 consists of novel, multiple connected views enabling the analyst to explore, discover, and understand interfirm networks for a focal firm, specific market segments or countries, and the entire business ecosystem. System evaluation by a small group of prototypical users shows supporting evidence of the benefits of our approach. This design study contributes to the relatively unexplored, but promising area of exploratory information visualization in market research and business strategy.