Kent State University

About: Kent State University is a education organization based out in Kent, Ohio, United States. It is known for research contribution in the topics: Liquid crystal & Population. The organization has 10897 authors who have published 24607 publications receiving 720309 citations. The organization is also known as: Kent State & KSU.

TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data

Abstract: We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

Topology discovery in heterogeneous IP networks: the NetInventory system

Abstract: Knowledge of the up-to-date physical topology of an IP network is crucial to a number of critical network management tasks, including reactive and proactive resource management, event correlation, and root-cause analysis. Given the dynamic nature of today's IP networks, keeping track of topology information manually is a daunting (if not impossible) task. Thus, effective algorithms for automatically discovering physical network topology are necessary. Earlier work has typically concentrated on either 1) discovering logical (i.e., layer-3) topology, which implies that the connectivity of all layer-2 elements (e.g., switches and bridges) is ignored, or 2) proprietary solutions targeting specific product families. In this paper, we present novel algorithms for discovering physical topology in heterogeneous (i.e., multi-vendor) IP networks. Our algorithms rely on standard SNMP MIB information that is widely supported by modern IP network elements and require no modifications to the operating system software running on elements or hosts. We have implemented the algorithms presented in this paper in the context of the NetInventory topology-discovery tool that has been tested on Lucent's own research network. The experimental results clearly validate our approach, demonstrating that our tool can consistently discover the accurate physical network topology with reasonably small running-time requirements even for fairly large network configurations.

Undecidability of CA classification schemes

Abstract: Abstra ct. Stephen wolfram int roduced the use of cellula r au tom ata. as mod els of complex sys tems and proposed a clas sification of th ese automata based on th eir st a t ist ical ly observed behavior. We invest igate various properti es of these classes; in part icular, we as k wheth er certain prop erties are effective, and we obtain several somewhat surpri sing result s. For examp le, we show th at it is undecidable wheth er all th e fini te configu rations of a given cellular automaton eventually become qu iescent. Consequently, it is undecidable to which class a given cellula r automaton belongs, even when choosing only between the two simp lest classes.

Accountability in Governance Networks: An Assessment of Public, Private, and Nonprofit Emergency Management Practices Following Hurricane Katrina

Abstract: What is the most effective framework for analyzing complex accountability challenges within governing networks? Recognizing the multiscale and intersector (public, private, and nonprofit) characteristics of these networks, an accountability model is advanced organized around democratic (elected representatives, citizens, and the legal system), market (owners and consumers), as well as administrative (bureaucratic, professional and collaborative) relationships. This concept draws from 2005 events following Hurricane Katrina. Multiple failures of governing networks to plan for and respond to Katrina include a breakdown in democratic, market, and administrative accountability as well as a pervasive confusion over trade-offs between accountability types emerging from crises. This essay offers several useful recommendations for emergency management planners as well as for those who teach and research.