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| 1 | Big-Data Processing Techniques and Their Challenges in Transport Domain显示文摘This paper describes the fundamentals of cloud computing and current big-data key technologies. We categorize big-data processing as batch- based, stream- based, graph- based,DAG-based, interactive-based, or visual-based according to the processing technique. We highlight the strengths and weaknesses of various big-data cloud processing techniques in order to help the big-data community select the appropriate processing technique. We also provide big data research challenges and future directions in aspect to transportation management systems. | Aftab Ahmed Chandio Nikos Tziritas Cheng-Zhong Xu | 2015 | ZTE Communications2015，13，1： | 3 |
| 2 | Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories显示文摘智慧城市为智能交通管理和交通网络智能应用的发展提供了巨大推动力。近来,智能交通系统(Intelligent transportation systems,ITSs)和移动位置服务(Location-based services,LBSs)也成为了研究领域的热点。交通领域数据量在快速不断增长,云计算在巨量数据的存储、接入、管理和处理方面有着巨大作用。交通领域相当比例的数据为GPS数据,此类数据具有非频繁、含噪声等特性,这使得维护基于GPS的实时交通软件的服务质量较为困难。在诸多智能交通系统应用中,地图匹配处理起着将GPS观测点准确排列于路网中的关键作用。考虑到准确性时,地图匹配策略的性能由两个连续的GPS观测点间的最短路径决定;另一方面,处理最短路径查询(Processing shortest path queries,SPQs)耗费着较高计算量。现有的地图匹配技术采用固定参数(固定的候选点数量,固定的误差圆半径)的办法,这可能导致确认线路分段时产生不确定性,也可导致低精度结果(或需进行大量SPQ处理以保证精度)。此外,由于采样错误的存在,较高采样时间(大于10 s)内的GPS数据常含有冗余数据,这也导致需要额外的SPQ处理。由于SPQ处理导致的高运算量问题,现有的地图匹配策略并不能实现实时应用。在本文中,我们提出一种实时地图匹配方法(Real-time map-matching,RT-MM)。该方法以云计算为基础,是一种全自适应地图匹配策略,能够应对实时GPS轨迹地图匹配中SPQ处理的关键问题。本研究还通过基于虚拟数据和实际数据的仿真,对所述方法与现有方法的性能进行了比较。 | Aftab Ahmed CHANDIO Nikos TZIRITAS Fan ZHANG Ling YIN Cheng-Zhong XU | 2016 | Frontiers of Information Technology & Electronic Engineering2016，17，12： | 2 |
| 3 | Image completion using efficient belief propagation via priority scheduling and dynamic pruning显示文摘 | Nikos Komodakis Gcorgios Tziritas | 2007 | IEEE Transaction on Image Processing2007，16，11： | 1 |
| 4 | Scheduling Heuristics for Live Video Transcoding on Cloud Edges显示文摘Efficient video delivery involves the transcoding of the original sequence into various resolutions,bitrates and standards,in order to match viewers’capabilities.Since video coding and transcoding are computationally demanding,performing a portion of these tasks at the network edges promises to decrease both the workload and network traffic towards the data centers of media providers.Motivated by the increasing popularity of live casting on social media platforms,in this paper we focus on the case of live video transcoding.Specifically,we investigate scheduling heuristics that decide on which jobs should be assigned to an edge minidatacenter and which to a backend datacenter.Through simulation experiments with different Qo S requirements we conclude on the best alternative. | Panagiotis Oikonomou Maria G. Koziri Nikos Tziritas Thanasis Loukopoulos XU Cheng-Zhong | 2017 | ZTE Communications2017，15，2： | 0 |
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