The Green Driver app is a dynamic routing application for GPS-enabled smartphones. Green Driver combines client GPS data with real-time traffic light information provided by cities to determine optimal routes in response to driver route requests. Routes are optimized with respect to travel time, with the intention of saving the driver both time and fuel, and rerouting can occur if warranted. During a routing session, client phones communicate with a centralized server that both collects GPS data and processes route requests. All relevant data are anonymized and saved to databases for analysis; statistics are calculated from the aggregate data and fed back to the routing engine to improve future routing. Analyses can also be performed to discern driver trends: where do drivers tend to go, how long do they stay, when and where does traffic congestion occur, and so on. The system uses a number of techniques from the field of artificial intelligence. We apply a variant of A* search for solving the stochastic shortest path problem in order to find optimal driving routes through a network of roads given light-status information. We also use dynamic programming and hidden Markov models to determine the progress of a driver through a network of roads from GPS data and light-status data. The Green Driver system is currently deployed for testing in Eugene, Oregon, and is scheduled for large-scale deployment in Portland, Oregon, in Spring 2011.

The state of security on the Internet is bad and becoming worse. One reaction to this state of affairs is a behavior termed "Ethical Hacking" which attempts to proactively increase security protection by identifying and patching known security vulnerabilities on systems owned by other parties. Ethical hackers may beta test unreleased software, stress test released software, and scan networks of computers for vulnerabilities. Previous work has emphasized ethical hacking as an altruistic behavior but we find ethical hackers act rationally, in self-interest, to secure systems that are within their own community (sometimes for pay)-networked systems are only as secure as the weakest system within perimeter defenses.

TPC-DS is a new decision support benchmark currently under development by the Transaction Processing Performance Council (TPC). This paper provides a brief overview of the new benchmark. The benchmark models the decision support functions of a retail product supplier, including data loading, multiple types of queries and data maintenance. The database consists of multiple snowflake schemas with shared dimension tables; data is skewed; and the query set is large. Overall, the benchmark is considerably more realistic than previous decision support benchmarks.

This paper presents a layered VPN planning framework for organizations based on actual implementation experience. Specifically, we describe security issues corresponding to five distinct layers of analysis: (1) VPN technology; (2) firewalls; (3) legacy networks; (4) survivability; and (5) legacy applications. There is a broad spectrum of VPN options available with each having its own strengths, weaknesses, and vulnerabilities. It is anticipated that no single VPN solution will supplant others but instead a diversity of choices will continue to emerge thus increasing the value of an advanced planning framework.