Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: Described herein are enterprise search systems and methods that cluster search results that are associated with the same metadata or the same enterprise search site into one or more clustered results panels of a search results panel. The search results that are not included in the clustered results panel(s) are included in a non-clustered results panel of the search results panel.

Abstract: Described herein are enterprise search systems and methods that cluster search results that are associated with the same metadata or the same enterprise search site into one or more clustered results panels of a search results panel. The search results that are not included in the clustered results panel(s) are included in a non-clustered results panel of the search results panel.

Abstract: Described herein are enterprise search systems and methods that cluster search results that are associated with the same metadata or the same enterprise search site into one or more clustered results panels of a search results panel. The search results that are not included in the clustered results panel(s) are included in a non-clustered results panel of the search results panel.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: A location of a network user computing device is determined relative to a location of a point of interest. If the user device is determined to be stationary, the user device is monitored for movement, the movement resulting in re-determining the location of the user device relative to a location of the point of interest. If the user device is determined to be moving, the velocity of the user device is matched with a predetermined velocity, and a preliminary estimated time of arrival to the point of interest is determined based on the predetermined velocity matched to the user device. At a later time that is based on a function of the preliminary estimated time of arrival, an estimated time of arrival to the point of interest is verified based on the predetermined velocity matched to the user device.

Abstract: Embodiments are configured to provide information based on a user query. In an embodiment, a system includes a search component having a ranking component that can be used to rank search results as part of a query response. In one embodiment, the ranking component includes a ranking algorithm that can use the length of documents returned in response to a search query to rank search results.

Abstract: A location of a network user computing device is determined relative to a location of a point of interest. If the user device is determined to be stationary, the user device is monitored for movement, the movement resulting in re-determining the location of the user device relative to a location of the point of interest. If the user device is determined to be moving, the velocity of the user device is matched with a predetermined velocity, and a preliminary estimated time of arrival to the point of interest is determined based on the predetermined velocity matched to the user device. At a later time that is based on a function of the preliminary estimated time of arrival, an estimated time of arrival to the point of interest is verified based on the predetermined velocity matched to the user device.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: An initial search request can be received. At least a portion of a set of initial search results can be presented in response to receiving the initial search request. The initial search results can include representations of searchable items that are responsive to the initial search request, where each of the searchable items can be a computer-readable data structure. A search refinement indicator can also be presented with the initial search results. User input can be received selecting the refinement indicator. In response, at least a portion of a set of refined search results can be presented. The refined search results can be a subset of the initial search results representing searchable items that meet requirements including a set of pre-defined smart refinement requirements of the refinement indicator.

Abstract: A location of a network user computing device is determined relative to a location of a point of interest. If the user device is determined to be stationary, the user device is monitored for movement, the movement resulting in re-determining the location of the user device relative to a location of the point of interest. If the user device is determined to be moving, the velocity of the user device is matched with a predetermined velocity, and a preliminary estimated time of arrival to the point of interest is determined based on the predetermined velocity matched to the user device. At a later time that is based on a function of the preliminary estimated time of arrival, an estimated time of arrival to the point of interest is verified based on the predetermined velocity matched to the user device.

Abstract: A neural network is used to process a set of ranking features in order to determine the relevancy ranking for a set of documents or other items. The neural network calculates a predicted relevancy score for each document and the documents can then be ordered by that score. Alternate embodiments apply a set of data transformations to the ranking features before they are input to the neural network. Training can be used to adapt both the neural network and certain of the data transformations to target environments.

Abstract: Embodiments are configured to provide information based on a user query. In an embodiment, a system includes a search component having a ranking component that can be used to rank search results as part of a query response. In one embodiment, the ranking component includes a ranking algorithm that can use the length of documents returned in response to a search query to rank search results.

Abstract: A neural network is used to process a set of ranking features in order to determine the relevancy ranking for a set of documents or other items. The neural network calculates a predicted relevancy score for each document and the documents can then be ordered by that score. Alternate embodiments apply a set of data transformations to the ranking features before they are input to the neural network. Training can be used to adapt both the neural network and certain of the data transformations to target environments.

Abstract: It is common in distributed systems to replicate data. In many cases, this data evolves in a consistent fashion, and this evolution can be modeled. A probabilistic model of the evolution allows us to estimate the divergence of the replicas and can be used by the application to alter its behavior, for example, to control synchronization times, to determine the propagation of writes, and to convey to the user information about how much the data may have evolved. In this paper, we describe how the evolution of the data may be modeled and outline how the probabilistic model may be utilized in various applications, concentrating on a news database example.

Abstract: A strainer system having two separate strainers positioned in parallel, where one strainer can be cleaned while still permitting normal flow through the other strainer. The system flow circuit is arranged so that a high pressure backwashing flow can be applied to one strainer while the other strainer is providing its normal straining function. The backwashed strainer is then isolated by valves so that its access flange can be removed and the particulate buildup within the valve plug cleaned out. The strainer is then returned to service following the replacement of the access flange. In this manner, it is possible to alternate between the two strainers so that continuous flow can be maintained to the hydraulic choke located downstream of the strainer system.

Abstract: Three-dimensional computer model data, moving image data or still image data showing at least one person is stored in an archive database 126, 703, 850, 1303, together with additional information to improve the searching and retrieval of data therefrom. The additional information includes view parameter data 512, 1040 which defines at whom or what each person is looking during each predetermined period of time or image. Text data 504, 1020 which comprises words associated with the person, and viewing histogram data 540 which, for each period of text data defines the percentage of time that the speaking person spent looking at each other person or object, may also be stored.