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公开(公告)号：US20110123066A9
公开(公告)日：2011-05-26
申请号：US12501242
申请日：2009-07-10
IPC分类号： G06K9/62
CPC分类号： G06T7/75 , G06T2207/10032 , G06T2207/30184
摘要： Methods for locating a feature on geospatial imagery and systems for performing those methods are disclosed. An accuracy level of each of a plurality of geospatial vector datasets available in a database can be determined. Each of the plurality of geospatial vector datasets corresponds to the same spatial region as the geospatial imagery. The geospatial vector dataset having the highest accuracy level may be selected. When the selected geospatial vector dataset and the geospatial imagery are misaligned, the selected geospatial vector dataset is aligned to the geospatial imagery. The location of the feature on the geospatial imagery is then determined based on the selected geospatial vector dataset and outputted via a display device.
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公开(公告)号：US20140343984A1
公开(公告)日：2014-11-20
申请号：US14213610
申请日：2014-03-14
申请人： Cyrus Shahabi , Leyla Kazemi
发明人： Cyrus Shahabi , Leyla Kazemi
CPC分类号： G06Q10/06311
摘要： Spatial crowdsourcing systems and methods assign spatial tasks to be performed by human workers. The systems and methods can verify the validity of the results provided by workers. Every worker can have a reputation score stating the probability that the worker performs a task correctly. Every spatial task can have a confidence threshold determining the minimum quality of the accepted level of its result. To satisfy this threshold, a task may be assigned redundantly to multiple workers. A reputation score can be associated to every worker, which represents the probability that a worker performs a task correctly. A task may be assigned to a subset of workers whose aggregate reputation score satisfies the confidence of the task.
摘要翻译： 空间众包系统和方法分配了由人类工作人员执行的空间任务。 系统和方法可以验证工作人员提供的结果的有效性。 每个工作人员都可以获得声誉分数，说明工作人员正确执行任务的可能性。 每个空间任务可以具有确定其结果的可接受水平的最小质量的置信度阈值。 为了满足该阈值，可以将任务冗余地分配给多个工作人员。 声誉分数可以与每个工作人员相关联，这表示工作人员正确执行任务的可能性。 任务可以被分配给其聚集的信誉评分满足任务的置信度的工作者的子集。
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公开(公告)号：US20140114556A1
公开(公告)日：2014-04-24
申请号：US14060360
申请日：2013-10-22
申请人： Bei Pan , Ugur Demiryurek , Cyrus Shahabi
发明人： Bei Pan , Ugur Demiryurek , Cyrus Shahabi
IPC分类号： G08G1/00
CPC分类号： G06N5/04 , G08G1/00 , G08G1/0112 , G08G1/0129 , G08G1/0141
摘要： Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.
摘要翻译： 运输网络上的实时高保真时空数据可用于了解不同时间和地点的交通行为，有可能大大节省时间和燃料。 从运输网络收集的现实数据可用于将数据的内在行为纳入时间序列挖掘技术，以提高其对流量预测的准确性。 例如，高峰时段和事件的时空行为可以用于对路段上的短期和长期平均速度进行更准确的预测，即使在出现偶然事件（例如事故）的情况下也是如此。 考虑到历史高峰时期的行为，可以将传统预测指标的准确度分别提高67％和78％，在短期和长期预测中。 此外，可以引入事故的影响，将预测精度提高高达91％。
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公开(公告)号：US08675995B2
公开(公告)日：2014-03-18
申请号：US12501242
申请日：2009-07-10
IPC分类号： G06K9/32
CPC分类号： G06T7/75 , G06T2207/10032 , G06T2207/30184
摘要： Methods for locating a feature on geospatial imagery and systems for performing those methods are disclosed. An accuracy level of each of a plurality of geospatial vector datasets available in a database can be determined. Each of the plurality of geospatial vector datasets corresponds to the same spatial region as the geospatial imagery. The geospatial vector dataset having the highest accuracy level may be selected. When the selected geospatial vector dataset and the geospatial imagery are misaligned, the selected geospatial vector dataset is aligned to the geospatial imagery. The location of the feature on the geospatial imagery is then determined based on the selected geospatial vector dataset and outputted via a display device.
摘要翻译： 公开了用于定位地理空间图像特征的方法和用于执行这些方法的系统。 可以确定数据库中可用的多个地理空间矢量数据集中的每一个的精度水平。 多个地理空间矢量数据集中的每一个对应于与地理空间图像相同的空间区域。 可以选择具有最高精度水平的地理空间矢量数据集。 当所选择的地理空间矢量数据集和地理空间图像不对齐时，所选择的地理空间矢量数据集与地理空间图像相一致。 然后，基于所选择的地理空间矢量数据集确定地理空间图像上的特征的位置，并通过显示装置输出。
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公开(公告)号：US20120278310A1
公开(公告)日：2012-11-01
申请号：US13456088
申请日：2012-04-25
申请人： Grady F. Laksmono , Cyrus Shahabi
发明人： Grady F. Laksmono , Cyrus Shahabi
IPC分类号： G06F17/30
CPC分类号： G06Q30/0631 , G06F16/9535
摘要： This specification describes technologies relating to collaborative filtering, such as collaborative filtering using a spatial-aware social graph. In at least one aspect, a method includes: receiving data including objects related to a social graph; identifying a proper subset of the objects based on their relationships with respect to the social graph; applying one or more spatial queries against the proper subset of the objects; and using a result of the one or more spatial queries, applied against the proper subset of the objects, as a feature in a recommendation process. In another aspect, a system includes: a user interface device; and one or more computers configured and arranged to provide a user a recommendation, with respect to objects, based on social and spatial information for the user associated with a spatial-aware social graph.
摘要翻译： 本说明书描述了与协作过滤相关的技术，例如使用空间感知社交图的协同过滤。 在至少一个方面，一种方法包括：接收包括与社交图相关的对象的数据; 基于它们与社交图的关系，识别对象的适当子集; 对对象的正确子集应用一个或多个空间查询; 以及使用针对对象的适当子集应用的一个或多个空间查询的结果作为推荐过程中的特征。 另一方面，一种系统包括：用户接口设备; 以及一个或多个计算机，其配置和布置成基于与空间感知社交图相关联的用户的社会和空间信息向用户提供关于对象的推荐。
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公开(公告)号：US20110119265A1
公开(公告)日：2011-05-19
申请号：US12619554
申请日：2009-11-16
IPC分类号： G06F17/30
CPC分类号： G06F17/3087 , G06F17/30722 , G06Q30/00
摘要： Document relevance is determined with respect to a region of interest (ROI). A set of location references may be associated with a set of documents. The system selects location references associated with an ROI and then selects documents corresponding to the selected location references. The selected documents can be reported or processed further. A document-location reference index can be accessed when the present system is ‘online’ and processing a request for documents relevant to an ROI. The document-location reference index may be generated and updated while the present system is ‘offline’ and not processing a request for documents. The resulting relevant documents may be provided to a user in response to a document search associated with the ROI or along with an advertisement associated with the ROI.
摘要翻译： 相对于感兴趣区域（ROI）确定文档相关性。 一组位置引用可以与一组文档相关联。 系统选择与ROI相关联的位置参考，然后选择与所选位置参考相对应的文档。 所选文件可以进一步报告或处理。 当本系统“联机”并处理与ROI有关的文件的请求时，可以访问文档位置参考索引。 文档位置参考索引可以在当前系统“脱机”时生成和更新，而不处理对文档的请求。 可以响应于与ROI相关联的文档搜索或与ROI相关联的广告来将所得到的相关文档提供给用户。
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公开(公告)号：US07096328B2
公开(公告)日：2006-08-22
申请号：US10351269
申请日：2003-01-24
IPC分类号： G06F12/00
摘要： Systems and techniques to pseudorandomly place and redistribute data blocks in a storage system. In general, in one implementation, the techniques include: distributing data blocks over multiple storage devices according to a reproducible pseudorandom sequence that provides load balancing across the storage devices, and determining current storage locations of the data blocks by reproducing the pseudorandom sequence. The techniques may also include: distributing data blocks over multiple storage devices according to a reproducible pseudorandom sequence, in response to initiation of a storage scaling operation, pseudorandomly redistributing a selected subset of the data blocks and saving information describing the storage scaling operation, determining current storage locations based on the pseudorandom sequence and the saved scaling operation information, and accessing the data blocks according to the determined current storage locations.
摘要翻译： 在存储系统中伪随机放置和重新分发数据块的系统和技术。 通常，在一个实施方式中，技术包括：根据可重现的伪随机序列在多个存储设备上分配数据块，该冗余序列在存储设备之间提供负载平衡，以及通过再现伪随机序列来确定数据块的当前存储位置。 这些技术还可以包括：响应于存储缩放操作的启动，根据可重现的伪随机序列在多个存储设备上分配数据块，伪随机重新分配所选数据块的子集并保存描述存储缩放操作的信息，确定当前 基于伪随机序列和保存的缩放操作信息的存储位置，以及根据所确定的当前存储位置来访问数据块。
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公开(公告)号：US09501509B2
公开(公告)日：2016-11-22
申请号：US14482805
申请日：2014-09-10
申请人： Afsin Akdogan , Cyrus Shahabi , Ugur Demiryurek
发明人： Afsin Akdogan , Cyrus Shahabi , Ugur Demiryurek
CPC分类号： G06F17/30327 , G06F17/30241
摘要： The present disclosure relates to a short-lived throwaway index structure for generating an index from scratch in a short period of time rather than updating an index with every location change of moving objects. Rapid index construction results from the generation of Voronoi diagrams in parallel using multiple cloud servers simultaneously.
摘要翻译： 本公开涉及一种用于在短时间内从头开始生成索引而不是按照移动对象的每个位置改变更新索引的短命丢弃索引结构。 同时使用多个云服务器并行生成Voronoi图的快速索引构建。
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9.发明授权公开(公告)号：US09062985B2
公开(公告)日：2015-06-23
申请号：US14059212
申请日：2013-10-21
IPC分类号： G01C21/34 , G08G1/01 , G08G1/0968
CPC分类号： G01C21/3476 , G01C21/3446 , G01C21/3492 , G08G1/0116 , G08G1/0129 , G08G1/096816
摘要： The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
摘要翻译： 文献中已经研究了空间网络中k个最近邻（k NN）查询的类。 空间网络中k NN搜索的现有方法假设空间网络中每个边缘的权重是恒定的。 然而，现实世界边际权重是时间依赖性的，并且在短时间内显着变化，因此使现有解决方案无效。 这里解决了时间依赖空间网络中k NN搜索的问题，其中每个边缘的权重是时间的函数。 提出了两个索引方案（紧密网络索引和松散网络索引），以最小化候选最近邻居对象的数量，并减少时间依赖空间网络中昂贵的最快路径计算的调用。 我们通过实际数据集的实验评估来展示我们提出的解决方案的效率，包括具有实际流量数据的各种大型空间网络。
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10.发明申请公开(公告)号：US20140046593A1
公开(公告)日：2014-02-13
申请号：US14059212
申请日：2013-10-21
IPC分类号： G01C21/34
CPC分类号： G01C21/3476 , G01C21/3446 , G01C21/3492 , G08G1/0116 , G08G1/0129 , G08G1/096816
摘要： The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
摘要翻译： 文献中已经研究了空间网络中k个最近邻（k NN）查询的类。 空间网络中k NN搜索的现有方法假设空间网络中每个边缘的权重是恒定的。 然而，现实世界边际权重是时间依赖性的，并且在短时间内显着变化，因此使现有解决方案无效。 这里解决了时间依赖空间网络中k NN搜索的问题，其中每个边缘的权重是时间的函数。 提出了两个索引方案（紧密网络索引和松散网络索引），以最小化候选最近邻居对象的数量，并减少时间依赖空间网络中昂贵的最快路径计算的调用。 我们通过实际数据集的实验评估来展示我们提出的解决方案的效率，包括具有实际流量数据的各种大型空间网络。
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