Decision Support for Complex Networks

Permanent URI for this collection


Recent Submissions

Now showing 1 - 5 of 5
  • Item
    Bayesian Inference for Static Traffic Network Flows with Mobile Sensor Data
    ( 2018-01-03) Tan, Zhen ; Gao, H.Oliver
    Vehicle trajectory information are becoming available from mobile sensors such as onboard devices or smart phones. Such data can provide partial information of origin-destination trips and are very helpful in solving the network flow estimation problem which can be very challenging if only link counts are used. Even with this new information, however, there is still structural bias in the maximum likelihood based approach because of uncertainties in the penetration rates. A Bayesian inference approach in which the earlier link-count-based methods are extended is proposed. We incorporate posterior simulation of route-choice probabilities and penetration rates. The results of a numerical example show that our method can infer network flow parameters effectively. Inclusion of mobile sensor data and prior beliefs based on it can yield much better inference results than when non-informative priors and only link counts are used.
  • Item
    Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration
    ( 2018-01-03) Calma, Adrian ; Oeste-Reiß, Sarah ; Sick, Bernhard ; Leimeister, Jan Marco
    When a learning system learns from data that was previously assigned to categories, we say that the learning system learns in a supervised way. By "supervised", we mean that a higher entity, for example a human, has arranged the data into categories. Fully categorizing the data is cost intensive and time consuming. Moreover, the categories (labels) provided by humans might be subject to uncertainty, as humans are prone to error. This is where dedicate collaborative interactive learning (D-CIL) comes together: The learning system can decide from which data it learns, copes with uncertainty regarding the categories, and does not require a fully labeled dataset. Against this background, we create the foundations of two central challenges in this early development stage of D-CIL: task complexity and uncertainty. We present an approach to "crowdsourcing traffic sign labels with self-assessment" that will support leveraging the potentials of D-CIL.
  • Item
    Proof of Concept for a Visual Analytics Dashboard for Transportation Network Analysis
    ( 2018-01-03) Nistor, Marian Sorin
    This paper discusses the latest developments in the field of visual analytics, and the role of network analysis for transportation systems. Multilayer and multiplex based visualizations are considered reliable solutions for handling the information overload the decision makers are facing in the addressed domain. The existing tools matching these requirements are briefly reviewed. Then, a proof of concept for a dashboard is presented focusing on a transportation network analysis with multiple network measures and indices in a multiplex visualization.
  • Item
    XLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat
    ( 2018-01-03) Simek, Olga ; Davis, Curtis ; Heier, Andrew ; Mohindra, Sanjeev ; O'Brien, Kyle ; Passarelli, John ; Waugh, Frederick
    XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper describes a novel system prototype that addresses threats arising from biological weapons of mass destruction. The prototype applies knowledge extraction analytics-”including link estimation, entity disambiguation, and event detection-”to build a knowledge base of 40 million entities and 140 million relationships from open sources. Exact and inexact subgraph matching analytics enable analysts to search the knowledge base for instances of modeled threats. The paper introduces new methods for inexact matching that accommodate threat models with temporal and geospatial patterns. System performance is demonstrated using several simplified threat models and an embedded scenario.
  • Item
    Introduction to the Minitrack on Decision Support for Complex Networks
    ( 2018-01-03) Bein, Wolfgang ; Pickl, Stefan