摘要:
The subject technology provides configurations for providing aggregated analytics tracking data associated with a dynamically generated session in response to a query for analytics tracking data. A query for analytics tracking data associated with a period of time is received in which the analytics tracking data includes data for tracking activity associated with a web site or application. The subject technology determines analytics tracking data for aggregating according to the period of time in order to associate the aggregated analytics tracking data with a dynamically generated session. The aggregated analytics tracking data associated with the dynamically generated session is then provided in response to the query.
摘要:
A method for sessionization of visitor activity includes receiving a first activity of a first client and a first client identifier from the first activity of the first client; the first client identifier stored at the first client; creating a first session from the first activity and the first client identifier; receiving a session link associated with the first activity; and creating a visitor-identified session based on the first client identifier, the first activity, and the session link.
摘要:
A method and system for aggregating analytics data is discussed. The system differentiates between analytics data that is context sensitive and therefore cannot be reliably updated incrementally (e.g., unique page views, time on site, etc.) and analytics data that is not context sensitive. The system aggregates the context insensitive metrics and dimensions incrementally, while aggregating the context sensitive metrics and dimensions after a specified time duration, such as at the end of the day. It is estimated that less than 10% of all metrics and dimensions are context sensitive. In this way, the aggregator server 160 is able to improve the freshness of more than 90% of the analytics data (represented by the context insensitive metrics and dimensions) to a shorter period of time than the prior art. Further, this reduces the possibility of over-counting metrics.
摘要:
A method and a system optimize the performance of an automatic path generation algorithm on a computer network. The system may include routers in the computer network configured into regions according to geographical locations, with some routers being designated inter-region routers in each region. The inter-region routers may be organized to be interconnected in a highly redundant manner (e.g., a full mesh) to ensure connectivity among the regions. Within such a network, when a path is required between routers in different regions, a processor applies the automatic path generation algorithm to: (a) the network segment in the region of the source router to generate a first set of paths, (b) the network segment of the inter-region routers to generate a second set of paths; and (c) the network segment of the destination router to generate a third set of paths, These set of paths are then combined to provide a set of paths between the source and destination routers. In addition, each router within each region may be further assigned a numerical rank indicative of a distance between the router and an inter-region router within the same region. In such a system, when a path is required between routers of the same region, the automatic path generation algorithm may be applied on a network segment pruned to include only routers with within a certain range of ranks.
摘要:
A method and system for aggregating analytics data is discussed. The system differentiates between analytics data that is context sensitive and therefore cannot be reliably updated incrementally (e.g., unique page views, time on site, etc.) and analytics data that is not context sensitive. The system aggregates the context insensitive metrics and dimensions incrementally, while aggregating the context sensitive metrics and dimensions after a specified time duration, such as at the end of the day. It is estimated that less than 10% of all metrics and dimensions are context sensitive. In this way, the aggregator server 160 is able to improve the freshness of more than 90% of the analytics data (represented by the context insensitive metrics and dimensions) to a shorter period of time than the prior art. Further, this reduces the possibility of over-counting metrics.