Visually Abstracting Event Sequences as Double Trees Enriched with Category-Based Comparison

Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position double trees as a domain-agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour-coded categories. We integrate the double tree and category-based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non-spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.     

Taxonomy for visualizing location-based information

Location-based data is digital information that has a real-world location. Location-based data can be used for many purposes, such as providing additional information on real-world objects or helping a user in a specific task. Access to such data can be provided in many ways, for example, with augmented reality (AR) systems. AR techniques can help its user in various tasks and the AR data can be presented to the user in various ways, depending on the task at hand. The different visualizations that can be used are heavily dependent on the hardware platform and, thus, all technologies are not suitable for every situation. This paper studies two factors that affect the visualization of location-based data. The two factors are the environment model they use, ranging from three dimensions (3D) to no dimensions (0D) at all; and the viewpoint, whether it is a first-person or a third-person view. As a result, we define a taxonomy for visualizing location-based data, where each model–view (MV) combination is referred to using its MV number. We also present numerous case studies with different MV values.     

A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation

Real‐time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non‐trivial task. To this end, we present a visual analytics situational awareness environment that supports the real‐time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine‐grained, crisis‐related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph‐based visual design. We propose a transparency‐based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.     

DASS Good: Explainable Data Mining of Spatial Cohort Data

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.     

Interactive Investigation of Traffic Congestion on Fat‐Tree Networks Using TreeScope

Parallel simulation codes often suffer from performance bottlenecks due to network congestion, leaving millions of dollars of investments underutilized. Given a network topology, it is critical to understand how different applications, job placements, routing schemes, etc., are affected by and contribute to network congestion, especially for large and complex networks. Understanding and optimizing communication on large‐scale networks is an active area of research. Domain experts often use exploratory tools to develop both intuitive and formal metrics for network health and performance. This paper presents Tree Scope , an interactive, web‐based visualization tool for exploring network traffic on large‐scale fat‐tree networks. Tree Scope encodes the network topology using a tailored matrix‐based representation and provides detailed visualization of all traffic in the network. We report on the design process of Tree Scope , which has been received positively by network researchers as well as system administrators. Through case studies of real and simulated data, we demonstrate how Tree Scope 's visual design and interactive support for complex queries on network traffic can provide experts with new insights into the occurrences and causes of congestion in the network.     

Spatial attributes of AVHRR imagery for environmental monitoring

Abstract

Spatial analysis of Advanced Very High Resolution Radiometer (AVHRR) data at Global Area Coverage (GAC) and High Resolution Picture Transmission (HRPT) resolution shows that structural information is detectable across a range of scales and that different biomes exhibit different detectable spatial characteristics. The spatial patterns observed in GAC and HRPT data are similar at coarse resolutions. Differences between the two are observed where point phenomena occur, and where scene objects are generally linear. The undersampling in GAC data generation can cause artificial contiguity and artificial disunity to appear in the image of any scene. The spatial structure observed in GAC image data must therefore be considered unreliable, at least at the scale of the GAC AVHRR resolution-cell size. However, the use of the spatial domain in studies of surface phenomena operating at scales greater than that of the resolution-cell size are unlikely to be limited by the undersampling effects. Indeed, the spatial temporal evolution of structure in AVHRR images may provide important indicators of regional environmental change.     

Videotext technology: an overview with special reference to transaction processing as an interactive service or Everything you never wanted to know about text on TV and were terrified that someone would tell you

Abstract

This paper presents a comprehensive overview of videotext technology with specific reference to transaction processing. Transaction processing is taken to include: banking, shopping, ticket booking and the downline loading of software. The overview characterizes the interactive transaction services offered by videotext systems. First, the hardware aspects of delivery of the information are considered including broadcast delivery and telephone and cable network delivery. Second, the software aspects of the construction of the information are discussed, including character and graphics (picture) generation. Last, the interactive services offered on videotext systems are reviewed, including transaction modes, dialogue types, task elements and user classification. Conclusions are drawn with respect to the suitability of videotext systems for different modes of transaction processing.     

Been There,Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments

The distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real-world environments. Our method is based on a space-time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think-aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.     

Supporting factoring transactions in Brazil using reasoning maps: a language-based DSS for evaluating accounts receivable

Factoring companies are a widespread way of providing working capital to small enterprises in Brazil. This type of financial transaction has higher risks when performed in developing countries, due to unreliable financial information on firms, an unstable environment, and particular managerial practices. This paper describes a case study in which a language-based DSS was developed for a Brazilian factoring company to evaluate the perceived risk of buying accounts receivable; and discusses the suitability of different approaches to decision support for this type of decision in Brazil—which may be relevant for similar situations in other developing countries.     

Optimal dynamic decision network model for scientific inquiry learning environment

While recent studies employ heuristic to support learners in scientific inquiry learning environments, this study examined the theoretical and practical aspects of decision-theoretic approach to simultaneous reason about learners’ scientific inquiry skills and provision of adaptive pedagogical interventions across time. In this study, the dynamic learner model, represented by three different Dynamic Decision Network (DDN) models, were employed and evaluated through a three-phase empirical study. This paper discusses how insights gained and lessons learned from the evaluations of a preceding model had led to the improvements of subsequent model; before finalizing the optimal design of DDN model. The empirical studies involved six domain experts, 101 first-year university learners, and dataset from our previous research. Each learner participated in a series of activities including a pretest, a session with INQPRO learning environment, a posttest, and an interview session. For each DDN model, the predictive accuracies were computed by comparing the classifications given by the model with (a) the results obtained from the pretest, posttest, and learner self-rating scores, and (b) classifications elicited by domain experts based on the learner interaction logs and the graphs exhibited by each model.     

Group online adaptive learning

Sharing information among multiple learning agents can accelerate learning. It could be particularly useful if learners operate in continuously changing environments, because a learner could benefit from previous experience of another learner to adapt to their new environment. Such group-adaptive learning has numerous applications, from predicting financial time-series, through content recommendation systems, to visual understanding for adaptive autonomous agents. Here we address the problem in the context of online adaptive learning. We formally define the learning settings of Group Online Adaptive Learning and derive an algorithm named Shared Online Adaptive Learning (SOAL) to address it. SOAL avoids explicitly modeling changes or their dynamics, and instead shares information continuously. The key idea is that learners share a common small pool of experts, which they can use in a weighted adaptive way. We define group adaptive regret and prove that SOAL maintains known bounds on the adaptive regret obtained for single adaptive learners. Furthermore, it quickly adapts when learning tasks are related to each other. We demonstrate the benefits of the approach for two domains: vision and text. First, in the visual domain, we study a visual navigation task where a robot learns to navigate based on outdoor video scenes. We show how navigation can improve when knowledge from other robots in related scenes is available. Second, in the text domain, we create a new dataset for the task of assigning submitted papers to relevant editors. This is, inherently, an adaptive learning task due to the dynamic nature of research fields evolving in time. We show how learning to assign editors improves when knowledge from other editors is available. Together, these results demonstrate the benefits for sharing information across learners in concurrently changing environments.

     

VisCoMET: Visually Analyzing Team Collaboration in Medical Emergency Trainings

Handling emergencies requires efficient and effective collaboration of medical professionals. To analyze their performance, in an application study, we have developed VisCoMET, a visual analytics approach displaying interactions of healthcare personnel in a triage training of a mass casualty incident. The application scenario stems from social interaction research, where the collaboration of teams is studied from different perspectives. We integrate recorded annotations from multiple sources, such as recorded videos of the sessions, transcribed communication, and eye-tracking information. For each session, an information-rich timeline visualizes events across these different channels, specifically highlighting interactions between the team members. We provide algorithmic support to identify frequent event patterns and to search for user-defined event sequences. Comparing different teams, an overview visualization aggregates each training session in a visual glyph as a node, connected to similar sessions through edges. An application example shows the usage of the approach in the comparative analysis of triage training sessions, where multiple teams encountered the same scene, and highlights discovered insights. The approach was evaluated through feedback from visualization and social interaction experts. The results show that the approach supports reflecting on teams' performance by exploratory analysis of collaboration behavior while particularly enabling the comparison of triage training sessions.     

移动实时数据库系统综述

移动实时数据库系统涉及到信息处理技术、分布式计算技术、移动通讯技术、实时处理技术等多个学科领域,是当前信息领域中前沿、开创性的课题。论文对移动实时数据库系统的体系结构、系统需求及移动实时环境下事务模型、数据复制和缓存、位置相关查询、数据广播、故障恢复等相关研究做了一个综述。     

Antecedents of ERP assimilation and its impact on ERP value: A TOE-based model and empirical test

Drawing on the technology-organization-environment (TOE) framework and prior IT assimilation literature, we develop and test an integrated model of ERP assimilation and value in Chinese firms. We also contrast the strength of the antecedents of ERP assimilation and value across different ownership types. Our empirical results using a survey data showed that relative advantage, complexity, compatibility, top management support, organization fit, financial commitment and competitive pressure are significant predictors for ERP assimilation. We also found that the linkage from ERP assimilation to ERP value is very significant. In addition, the strength of the antecedents of ERP assimilation and value varies dramatically across different firm ownership types. Our study confirms that traditional diffusion of innovation attributes, top management support and competitive pressure are significant during the whole ERP life cycle. Our findings also emphasize and validate the importance of ERP assimilation in improving firm performance, and provide evidence for the continued debate on ERP payoff and information technology productivity paradox. In sum, these findings provide insights into how ERP assimilation and ERP value are influenced by contextual elements, and how the impacts may vary across different ownership types.     

A centrality analysis of the transaction relationships in Panasonic

Panasonic initiated a reform strategy called “Value Creation 21” in 2001. This strategy had a strong impact on its transaction relationships. This research covers one of the important issues in analyzing how the transaction network in Panasonic has changed during the period of “Value Creation 21.” In order to make Panasonic’s transaction relationships visible and countable, we have introduced graph theory and a measure centrality index from the viewpoints of degree, closeness, and betweenness by using data collected in 2002 and 2005. Our findings are reported here. First, the number of firms in Panasonic’s transaction network in 2005 was smaller than in 2002. Second, not only the degree, but also the closeness and betweenness, of the main firms in the Panasonic Group and their suppliers decreased a little more in 2005. Third, the number of in-degree firms declined, whereas the relative importance of Panasonic in the transaction network was more significant. Fourth, Panasonic’s affiliated firms in components & devices and the digital AVC network domain ranked higher than other firms in the transaction network. Last, its out-degree suppliers dropped more in 2005 than in 2002. With these findings, we finally concluded how Panasonic arranged its transaction relationships during the turnaround.     

Voting techniques for expert search

In an expert search task, the users’ need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users’ query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques. Extended version of ‘Voting for candidates: adapting data fusion techniques for an expert search task’. C. Macdonald and I. Ounis. In Proceedings of ACM CIKM 2006, Arlington, VA. 2006. doi: 10.1145/1183614.1183671.     

面向频繁项集挖掘的本地差分隐私事务数据收集方法

事务数据常见于各种应用场景中,如购物记录、页面浏览历史等.为了提供更好的服务,服务提供商收集用户数据并进行分析,但收集事务数据会泄露用户的隐私信息.为了解决上述问题,基于压缩的本地差分隐私模型,提出一种事务数据收集方法.首先,定义了一种新的候选项集分值函数;其次,基于该函数,将候选项集的样本空间划分为多个子空间;然后,随机选择其中一个子空间,基于该子空间随机生成事务数据并发送给不可信的数据收集者;最后,考虑到隐私参数的设置问题,基于最大后验置信度攻击模型设计启发式隐私参数设置策略.理论分析表明,该方法能够同时保护事务数据的长度与内容,满足压缩的本地差分隐私要求.实验结果表明,与目前最优的工作相比,所收集的数据具有更高的效用性,隐私参数设置更具有语义性.     

Structuring Knowledge Bases for Designers of Learning Materials

Designers of interactive video training packages need access to multiple sources of information to be able to create exciting materials for learning or training. Hypertext allows dynamic linking of different strands of knowledge in ways which encourage effective elicitation of knowledge from experts, and understandable representation for subsequent users.

This paper describes the knowledge representation aspect of a system which will allow a designer to collect information from clients, from experts, as well as from printed and visual sources — to co-ordinate the information in a single environment, and using the same set of tools, to link the information to learning materials using videodisc and graphics packages.

The total system for a designer consists of an authoring environment, using NoteCards on a Xerox 1186, with a multi-tasking workstation (Amiga) which will bring together output from the knowledge base on the Xerox with information from videodisc. Information about the physical requirements of the authoring environment — constraints, domain limits, financial aspects — can be recorded alongside subject domain knowledge using different ‘modes’ of the Note Cards environment. Modes are a feature of a supplementary package. IDE (Instructional Design Environment), which operates as a ‘shell’ along with Note Cards. Our involvement with the designer's system is in looking at ways in which domain knowledge can be recorded and represented for use interactively with graphics and video materials.     

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.     

The Moderating Effect of Industry Types on IT-Coordination Costs Relationship

ABSTRACT

This study builds on prior research that examines the IT-coordination costs and firm size relationships using the transaction cost economics perspective. Specifically, this study aims to answer the following research question: Does firm’s industry type—information product industries (IPI) versus physical product industries (PPI) moderately affects the relationships between IT spending, coordination costs, and firm size? To address this research question, this study uses firm-level data from the Information Week and Compustat dataset in the United States, from 2011 to 2013. Further, this study employs the PLS-multigroup analysis (MGA) for both IPI and PPI firms. The overall PLS-MGA path analysis results show that there is a significant difference on the IT impact between the IPI and PPI firms. Indeed, firms in IPI and PPI can utilize the presence of IT pertaining to different information and physical processing activities at large.