Abstract: An apparatus, method and computer readable medium, the method comprising: obtaining raw maritime data from a plurality of sources, the raw maritime data indicative of a geolocation of vessels at different times and comprises duplicative data obtained from separate sources; analyzing the raw maritime data to produce for each vessel a vessel story comprising a set of activities and corresponding timestamps, wherein the set of activities associated with each vessel is smaller by at least one order of magnitude than the raw maritime data associated with the each vessel; identifying a pattern in the vessel story associated with a vessel, wherein the pattern conforms with a risk event; and validating the risk event using the raw maritime data or vessel stories, whereby identifying the risk event using reduced resources than required to identify the risk event in the raw maritime data, and without increasing false positive metrics.

Abstract: Method, system and product for risk event identification in maritime data and usage thereof. Ram maritime data is analyzed to produce for each vessel a vessel story. The vessel story comprises a set of activities and corresponding timestamps, that are deduced from the raw maritime data. A pattern, that conforms with a risk event, in the vessel story associated with a vessel. The risk event is validated using the raw maritime data or using one or more vessel stories. So, the risk event is identified using reduced resources in comparison with resources required to identify the risk event in the raw maritime data, and without increasing false positive metrics. Additionally, or alternatively, a pattern, that conforms with a risk event, in the vessel story associated with a first vessel is identified. The risk event is validated using the vessel story associated with a second vessel.

Abstract: According to certain embodiments of the present invention behavioral patterns are utilized in order to calculate a predicted level of interest of viewers in a specific video item at any given moment throughout the length of the video item. More specifically, according to certain embodiments, this calculation allows to identify instances of peaks of high and low interest throughout the length of the video item. This allows predicting the occurrence of critical moments throughout the video item when it is likely that viewers would abandon the video item on one hand, and moments when viewers are most likely absorbed in the currently presented video, on the other hand. This information allows to capture viewers when their interest in the viewed content is low or decreasing, and to synchronize the presentation of alternative content to these instances.

Abstract: According to certain embodiments of the present invention behavioral patterns are utilized in order to calculate a predicted level of interest of viewers in a specific video item at any given moment throughout the length of the video item. More specifically, according to certain embodiments, this calculation allows to identify instances of peaks of high and low interest throughout the length of the video item. This allows predicting the occurrence of critical moments throughout the video item when it is likely that viewers would abandon the video item on one hand, and moments when viewers are most likely absorbed in the currently presented video, on the other hand. This information allows to capture viewers when their interest in the viewed content is low or decreasing, and to synchronize the presentation of alternative content to these instances.

Abstract: According to certain embodiments of the present invention behavioral patterns are utilized in order to calculate a predicted level of interest of viewers in a specific video item at any given moment throughout the length of the video item. More specifically, according to certain embodiments, this calculation allows to identify instances of peaks of high and low interest throughout the length of the video item. This allows predicting the occurrence of critical moments throughout the video item when it is likely that viewers would abandon the video item on one hand, and moments when viewers are most likely absorbed in the currently presented video, on the other hand. This information allows to capture viewers when their interest in the viewed content is low or decreasing, and to synchronize the presentation of alternative content to these instances.

Abstract: According to certain embodiments of the present invention behavioral patterns are utilized in order to calculate a predicted level of interest of viewers in a specific video item at any given moment throughout the length of the video item. More specifically, according to certain embodiments, this calculation allows to identify instances of peaks of high and low interest throughout the length of the video item. This allows predicting the occurrence of critical moments throughout the video item when is likely that viewers would abandon the video item on one hand, and moments when viewers are most likely absorbed in the currently presented video, on the other hand. This information allows to capture viewers when their interest in the viewed content is low or decreasing, and to synchronize the presentation of alternative content to these instances.

Abstract: Systems and methods for associating content. In one embodiment, one or more techniques are used in associating content. In one embodiment, a unified content stream is created which includes two or more content items (or parts thereof) which have been associated with one another, for example a non-advertisement content item such as a video and associated advertisement(s).