نقشه تطبیق مبتنی بر رایانش ابری برای مرکز داده حمل و نقل
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|74129||2015||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Electronic Commerce Research and Applications, Volume 14, Issue 6, October–November 2015, Pages 431–443
Transportation data center has recently become a common practice of modern integrated transportation management in major cities of China. Being the convergence center of large-scale multi-source vehicle tracking data, it caused great challenge on GPS map-matching efficiency and privacy protection. In this paper, we propose a secure parallel map-matching system based on Cloud Computing technology to meet the demand of transportation data center. The main contributions are as follows: (1) we propose a leapfrog method to improve the efficiency of traditional serial map-matching algorithm on the increasingly common high sampling rate GPS data; (2) we adapt the serial leapfrog map-matching algorithm for cloud computing environment by reforming it in the MapReduce paradigm; (3) we propose a privacy-aware map-matching model over hybrid clouds to realize the sensitive GPS data protection. We implemented the proposed map-matching system in the hadoop platform and tested its performance with a large-scale vehicle tracking dataset, which exceeds 100 billion records. The experimental results show that our approach is highly efficient and effective on massive vehicle tracking data processing.