A Class Analysis for the Future? Comment on Grusky and Weeden: ‘Decomposition Without Death: A Research Agenda for a New Class Analysis’
As the Marxian project falls out of favour, scholars have therefore settled into some version of Weberianism or postmodernism, neither of which pays much attention to occupation-level structuration. We have outlined a quasi-Durheimian third road that refocuses attention on local forms of structuration within the division of labor. (Grusky & Weeden 2001:214)
Traffic information systems based on mobile, in-car sensor technology are a challenge for data management systems as a huge amount of data has to be processed in real-time. Data mining methods must be adapted to cope with these challenges in handling streaming data. Although several data stream mining methods have been proposed, an evaluation of such methods in the context of traffic applications is yet missing. In this paper, we present an evaluation framework for data stream mining for traffic applications. We apply a traffic simulation software to emulate the generation of traffic data by mobile probes. The framework is evaluated in a first case study, namely queue-end detection. We show first results of the evaluation of a data stream mining method, varying multiple parameters for the traffic simulation. The goal of our work is to identify parameter settings for which the data stream mining methods produce useful results for the traffic application at hand.