Elevators are regarded as important modern day necessities for urban communities. Although elevators are widely used in various installations, the actual control and monitoring of these systems is given less attention by the research community. Specifically, the problem of maintaining and analyzing massive real-time elevator control signals has been largely ignored by data mining community. Every elevator consists of at least one onboard controller (circuit board). Like any other realtime systems, an onboard elevator controller can generate a large volume of signals. The size of real-time signals and maintenance records generated annually from a number of high-rise housing estate elevators can reach hundreds of Gigabytes. In this paper, we describe a data warehousing approach for managing massive real-time elevator signals. Our prototype system shows significant reduction in storage requirement and allows efficient query processing across massive real-time data.I.

Elevators are considered as important transportation systems for urban communities. Elevators are installed with onboard controllers (circuit boards) and these controllers can generate a large volume of signals and events. In this paper, we describe an event-driven system to test, control, and monitor a large number of on board elevator controllers. The integrated system consists of a virtual controller, control and monitoring terminals, a central server, a playback function with animation, a genetic algorithm based maintenance scheduling module, and a data warehouse for managing massive real-time elevator signals. Based on the event-driven architecture, the proposed system is capable of facilitating faster deployment of new types of elevators. The system also provides engineers with playback functions for troubleshooting any hardware or software errors. In order to reduce overhead cost, the proposed system is designed to optimize resource allocation in maintenance scheduling. By deploying data warehouse technology, the proposed system allows significant reduction of storage requirement for managing real-time signals.