Abstract:
Multiple tuples are combined (202) by a processing node into a message that has a key portion including a value of a key, and a payload including a relation containing content of the combined plurality of tuple. The message is sent (204) to a receiving node.
Abstract:
A technique for resending messages can include determining if a particular message with a first unique identifier has been received by a recipient task among a number of potential recipient tasks in a distributed streaming system, notifying the number of potential recipient tasks to ignore the particular message with the first unique identifier, and resending the particular message with a second unique identifier.
Abstract:
A technique includes obtaining a plurality of n-grams from a plurality of messages, determining a temporal histogram for each n-gram, and determining synonyms among the n-grams based on a combination of a correlation of the histograms and a distance measure between n-grams.
Abstract:
According to an example, a catalog of scripts may be managed. Management of the catalog of scripts may include the addition of a script description into the catalog of scripts. In one example, the script description may be directly added to the catalog of scripts. In another example, the script description may be added through generation of a merged query of scripts.
Abstract:
A technique of batching tuples can include determining a plurality of key-attributes for a plurality of tuples, creating a batch tuple, and calculating a hash value for the batch tuple.
Abstract:
A pattern of geocoded pixels is generated by accessing data point values, where each data point value includes an attribute value and coordinates of a geographic location. Each data point value corresponds to a geocoded pixel that is positioned on the pattern based on the coordinates of the data point value such some geocoded pixels overlap other geocoded pixels. Different levels of the pattern of geocoded pixels correspond to a different degree of overlap between the geocoded pixels. The different levels of the pattern of geocoded pixels are associated with different magnification levels of a geographic map such that changing a magnification level of the geographic map causes a degree of overlap between the geocoded pixels of the pattern to change.
Abstract:
Using a contingency calculation based on a number of events sharing a collection of values of plural attributes, a discriminative metric is computed representing a statistical significance of the events that share the collection of values of the plural attributes. A visualization is generated that includes cells representing respective events, the visualization including a region containing a subset of the cells corresponding to the collection of values of the plural attributes, and the visualization including a significance visual indicator associated with the region to indicate the statistical significance of the events sharing the collection of values of the plural attributes.
Abstract:
Example embodiments relate to providing visual analytics of spatial time series data. In example embodiments, sensors may be located at regions within a building for collecting sensor data at regular time intervals. A sensor hierarchy can be generated including sensor nodes that are hierarchically arranged according to a physical infrastructure of the building, where each of the sensor nodes corresponds to a sensor. Sensor data can be obtained from the sensors, and a pixel calendar tree can be generated based on the sensor data and the sensor hierarchy, where the pixel calendar tree is recursively subdivided into tree portions according to a proportion of the sensor data attributable to each of the sensors. The pixel calendar tree can be displayed, where each of the tree portions includes time series sensor data of a corresponding region that is generated based on the sensor data.
Abstract:
A technique of batching tuples can include determining a plurality of key-attributes for a plurality of tuples, creating a batch tuple, and calculating a hash value for the batch tuple.
Abstract:
Causal topic mining can include incorporating non-text time series data with a number of articles based on a time relationship and analyzing the incorporated non-text time series data and the number of articles at a particular time to determine a causal relationship.