Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

Implicit Measures of Lostness and Success in Web Navigation

In two studies, we investigated the ability of a variety of structural and temporal measures computed from a web navigation path to predict lostness and task success. The user’s task was to find requested target information on specified websites. The web navigation measures were based on counts of visits to web pages and other statistical properties of the web usage graph (such as compactness, stratum, and similarity to the optimal path). Subjective lostness was best predicted by similarity to the optimal path and time on task. The best overall predictor of success on individual tasks was similarity to the optimal path, but other predictors were sometimes superior depending on the particular web navigation task. These measures can be used to diagnose user navigational problems and to help identify problems in website design

Hijacking Our Own Attention Controls to Curb Capitalistic Surveillance

There is currently a lacuna within the law with regard to the legality of the ethics of creating software with features that initiate and perpetrate addictive behavioral patterns: users are engaged in a perpetual scroll that allows for extensive free data mining that benefits the profit motives of corporations. The user is the product: in essence, the user’s attention is being mined, as the longer a user spends scrolling, the more profitable to a corporation he/she is. Internet companies are only concerned with how to best initiate, motivate and perpetrate addictive behaviors as strategies to mine data and in turn, optimize profits, and take no pains to protect or care for vulnerable populations that fall prey to the woes of addiction. This data is shared with corporations, institutions, and government agencies and used to modify behavioral changes and to classify, differentiate, and hierarchize individuals as they see fit. Knowledge is power. We no longer own our own data. We no longer own our own attention. Ethicists understand the need for enacting and enforcing policies and regulations that limit the data mining of Big Tech and limit the addictive potential of platform and app designs. In this paper, I examine the relationship between knowledge and power and its relevance and implications for the infiltration of surveillance as a mechanism of power in educational practices with the aim of increasing user conformity. I discuss the development of an app that helps redirect the obsessive-compulsive feedback loop of addictive thinking that benefits corporations and institutions into thinking patterns that help users control usage and break addiction, and in turn, generate positive physical, mental and socio-cultural benefits. Finally, I evaluate the positive and negative social implications of using attention distraction blocker apps.Plan II Honors Progra

Anticipating Information Needs Based on Check-in Activity

In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM '17), 201

Revisiting Interest Indicators Derived from Web Reading Behavior for Implicit User Modeling

Today, intelligent user interfaces on the web often come in form of recommendation services tailoring content to individual users. Recommendation of web content such as news articles often requires a certain amount of explicit ratings to allow for satisfactory results, i.e., the selection of content actually relevant for the user. Yet, the collection of such explicit ratings is time-consuming and dependent on users' willingness to provide the required information on a regular basis. Thus, using implicit interest indicators can be a helpful complementation to relying on explicitly entered information only. Analysis of reading behavior on the web can be the basis for the derivation of such implicit indicators. Previous work has already identified several indicators and discussed how they can be used as a basis for user models. However, most earlier work is either of conceptual nature and does not involve studies to prove the suggested concepts or relies on meanwhile potentially outdated technology. All earlier discussions of the topic further have in common that they do not yet consider mobile contexts. This paper builds upon earlier work, however providing a major update regarding technology and web reading context, distinguishing between desktop and mobile settings. This update also allowed us to identify a set of new indicators that so far have not yet been discussed. This paper describes (i) our technical work, a framework for analyzing user interactions with the browser relying on latest web technologies, (ii) the implicit interest indicators we either revisited or newly identified, and (iii) the results of an online study on web reading behavior as a basis for derivation of interest we conducted with 96 participants

Research-Based Web Design & Usability Guidelines [2006 edition]

The new edition of the U.S. Department of Health and Human Services’ (HHS) Research-Based Web Design and Usability Guidelines. These guidelines reflect HHS’ commitment to identifying innovative, research-based approaches that result in highly responsive and easy-to-use Web sites for the public. These guidelines help move us in that direction by providing practical, yet authoritative, guidance on a broad range of Web design and communication issues. Having access to the best available research helps to ensure we make the right decisions the first time around and reduces the possibility of errors and costly mistakes