An Experimental Evaluation of Grouping Definitions for Moving Entities

One important pattern analysis task for trajectory data is to find a group: a set of entities that travel together over a period of time. In this paper, we compare four definitions of groups by conducting extensive experiments using various data sets. The grouping definitions are different by one or more of three different characteristics: whether they use the measured sample points or continuous movement, how distance is used to decide if entities are in the same group, and whether the duration of the group is measured cumulatively or as one contiguous time interval. We are interested in the differences between the definitions and comparisons to human-annotated data, if available. We concentrate on pedestrian data and on different crowd densities. Furthermore, we analyze the robustness of the definitions with respect to their dependence on different sampling rates. We use two types of trajectory data sets: synthetic trajectories and real-life trajectories extracted from video surveillance. We present the results of the quantitative evaluations. For experiments with real-life trajectories, we augment them with a qualitative evaluation using videos that show groups in the trajectories with a color coding

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres

Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Clustering and Community Detection in Directed Networks: A Survey

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges non symmetric. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of applications. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method and tool for community detection and evaluation. The goal of this paper is to offer an in-depth review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear

A rule dynamics approach to event detection in Twitter with its application to sports and politics

The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events

Toxicity Pathways – from concepts to application in chemical safety assessment

Few would deny that the NRC report (NRC, 2007), "Toxicity Testing in the 21st Century: A Vision and Strategy”, represented a re-orientation of thinking surrounding the risk assessment of environmental chemicals. The key take-home message was that by understanding Toxicity Pathways (TP) we could profile the potential hazard and assess risks to humans and the environment using intelligent combinations of computational and in vitro methods. In theory at least, shifting to this new paradigm promises more efficient, comprehensive and cost effective testing strategies for every chemical in commerce while minimising the use of animals. For those of us who embrace the vision and the strategy proposed to achieve it, attention has increasingly focused on how we can actually practice what we preach. For a start, 21st century concepts described in the report have to be carefully interpreted and then translated into processes that essentially define and operationalize a TP framework for chemical risk assessment. In September 2011 the European Commission's Joint Research Centre (JRC) and the Hamner Institutes for Health Sciences co-organised a "Toxicity Pathways" workshop. It was hosted by the JRC and took place in Ispra, Italy. There were 23 invited participants with more or less equal representation from Europe and North America. The purpose of the meeting was to address three key questions surrounding a TP based approach to chemical risk assessment, namely – What constitutes a TP? How can we use TPs to develop in vitro assays and testing strategies? And, How can the results from TP testing be used in human health risk assessments? The meeting ran over two days and comprised a series of thought-starter presentations, breakout sessions and plenty of group discussions. The outcome was captured by rapporteurs and compiled as a workshop report which is available for download (without charge) from the JRC website. Here we expand on selected deliberations of the workshop to illustrate how TP thinking is still evolving and to indicate what pieces of the puzzle still need to fall into place before TP based risk assessment can become a reality.JRC.I.5-Systems Toxicolog

Improved Teaching of Database Schema Modeling by Visualizing Changes in Levels of Abstraction

Conceptual modeling of databases is a complex cognitive activity, particularly for novice database designers. The current research empirically tests a new pedagogy for this activity. It examines an instructional approach that stresses visualizing gradual transitions between levels of abstraction in different hierarchic levels of a relational database schema. The new approach builds on a four-level TSSL model from the field of human-computer interaction. TSSL, an acronym for the Task, Semantics, Syntax, and Lexical levels, is applied here to describe the levels of conceptual database modeling and to explain how improved instructional design can help minimize extraneous cognitive load during the design of database schemas. We tested the effectiveness of the proposed instructional approach via a controlled experiment carried out on IS students. We divided students into two groups, those exposed to a visual emphasis on the syntax of gradual transitions in a schema structure and those not exposed to it. We then measured performance in terms of errors in students’ solutions while also recording their perceptions and attitudes toward the instructional approach and the activity of database modeling. Our results show that the new approach is an effective tool for teaching database modeling