A Framework for Knowledge Derivation Incorporating Trust and Quality of Data

Today, across all major industries gaining insight from data is seen as an essential part of business. However, while data gathering is becoming inexpensive and relatively easy, analysis and ultimately deriving knowledge from it is increasingly difficult. In many cases, there is the problem of too much data such that important insights are hard to find. The problem is often not lack of data but whether knowledge derived from it is trustworthy. This means distinguishing "good" from "bad" insights based on factors such as context and reputation. Still, modeling trust and quality of data is complex because of the various conditions and relationships in heterogeneous environments. The new TrustKnowOne framework and architecture developed in this dissertation addresses these issues by describing an approach to fully incorporate trust and quality of data with all its aspects into the knowledge derivation process. This is based on Berlin, an abstract graph model we developed that can be used to model various approaches to trustworthiness and relationship assessment as well as decision making processes. In particular, processing, assessment, and evaluation approaches are implemented as graph expressions that are evaluated on graph components modeling the data. We have implemented and applied our framework to three complex scenarios using real data from public data repositories. As part of their evaluation we highlighted how our approach exhibits both the formalization and flexibility necessary to model each of the realistic scenarios. The implementation and evaluation of these scenarios confirms the advantages of the TrustKnowOne framework over current approaches

Service Oriented Architecture for Monitoring Cargo in Motion Along Trusted Corridors

This thesis describes a system called the Transportation Security SensorNet that can be used to perform extensive cargo monitoring. It is built as a Service Oriented Architecture (SOA) using open web service specifications and Open Geospatial Consortium (OGC) standards. This allows for compatibility, interoperability and integration with other web services and Geographical Information Systems (GIS). The two main capabilities that the Transportation Security SensorNet provides are remote sensor management and alarm notification. The architecture and the design of its components are described throughout this thesis. Furthermore, the specifications used and the fundamental ideas behind a Service Oriented Architecture are explained in detail. The system was evaluated in real world scenarios and performed as specified. The alarm notification performance throughout the system, from the initial detection at the Sensor Node service to the Alarm Reporting service, is on average 2.1 seconds. Location inquiries took 4.4 seconds on average. Note that the majority of the time, around 85% for most of the messages sent, is spent on the transmission of the message while the rest is used on processing inside the web services. Finally the lessons learned are discussed as well as directions for future enhancements to the Transportation Security SensorNet, in particular to security, complex management and asynchronous communication

An open system transportation security sensor network: field trial experiences

Abstract Cargo shipments are subject to hijack, theft, or tampering. Furthermore, cargo shipments are at risk of being used to transport contraband, potentially resulting in fines to shippers. The Transportation Security Sensor Network (TSSN), which is based on open software systems and Service Oriented Architecture (SOA) principles, has been developed to mitigate these risks. Using commercial off-the-shelf (COTS) hardware, the TSSN is able to detect events and report those relevant to appropriate decision makers. However, field testing is required to validate the system architecture as well as to determine if the system can provide timely event notification. Field experiments were conducted to assess the TSSN's suitability for monitoring rail-borne cargo. Log files were collected from these experiments and postprocessed. We present the TSSN architecture and results of field experiments, including the time taken to report events using the TSSN as well as on the interaction between various components of the TSSN. These results show that the TSSN architecture can be used to monitor rail-borne cargo. i