Showing papers by "Alexander G. Nikolaev published in 2012"

Optimal Aviation Security Screening Strategies With Dynamic Passenger Risk Updates

Abstract: Passenger screening is a critical component of aviation security systems. This paper introduces the multistage sequential passenger screening problem (MSPSP), which models passenger and carry-on baggage screening operations in an aviation security system with the capability of dynamically updating the perceived risk of passengers. The passenger screening operation at an airport terminal is subdivided into multiple screening stages, with decisions made to assign each passenger to one of several available security classes at each such stage. Each passenger's assessed threat value (initially determined by an automated passenger prescreening system) is updated after the passenger proceeds through each screening stage. The objective of MSPSP is to maximize the total security of all passenger screening decisions over a fixed time period, given passenger perceived risk levels and security device performance parameters. An optimal policy for screening passengers in MSPSP is obtained using optimal sequential assignment theory. A Monte Carlo simulation-based heuristic is presented and compared with stochastic sequential assignment and feedback control algorithms. Computational analysis of a two-stage security system provides an assessment of the total security performance.

An Efficient Map-Reduce Algorithm for the Incremental Computation of All-Pairs Shortest Paths in Social Networks

Abstract: Today's social networks are getting larger, and the need to analyze datasets with millions of nodes and billions of edges is not uncommon any more. As a network of social relationships evolves by the addition of new nodes and edges, fast algorithms are desirable for the recomputation of key network measures such as actor centrality. The distributed computing paradigm offers a scalable approach to addressing the recomputation challenge. This paper develops a Map-Reduce implementation of an incremental All-Pairs Shortest Path (APSP) algorithm. The incremental nature of the approach allows for performing minimal work in updating centrality measures, while the Map-Reduce implementation makes it scalable to large data. The key idea of the incremental APSP algorithm [1] is based on the efficient use of past information about the shortest paths between any node and the neighbors of the newly added node. A presented parallelized version of the algorithm relies on a three-step iterative execution of the "map" and "reduce" jobs. Experiences with its implementation are reported in application to a real-world dataset containing 7115 nodes. The experimental runs were performed using the Amazon's EMR service.