Abstract: An example method comprises performing for each class from a plurality of classes: constructing binary training set for the class, the binary training set including labeled cases for that class from the main training set other labeled cases from the main training set; training classifier for the class on the binary training set; computing a local calibration threshold using scores of the labeled cases in the binary training set; and adjusting all scores of the label cases in the binary training set with the local calibration threshold to meet a global decision threshold. The method also comprises determining, with the processor, a global hierarchical calibration threshold by using the adjusted scores for all classes to optimize a performance measurement of all trained classifiers. The method further comprises classifying, with the processor, a new case by using a previously trained classifier, a local calibration threshold, and the global hierarchical calibration threshold.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises receiving a training dataset of cases, where each of a plurality of classes is associated with a set of labeled cases in the training dataset. The method also comprises defining a proper subset of classes in the training dataset, and training a first classifier model on the proper subset of classes in the training dataset. The method further comprises testing the first classifier model on at least one class in the training dataset that was excluded from the proper subset, and determining a performance measurement of the first classifier model.

Abstract: Example implementations relate to records based on bit-shifting. For example, a computing device may include at least one processor to receive a query, create a normalized query, and generate a mapping of the normalized query for each record in a set of records in a database using a state array, the mapping being based on bit-shifting of bits associated with similarities between each character in the normalized query and each character in a field of the respective record in the set of records, each successive level of the state array indicating a stronger match between the query and the respective record than an immediately prior level in the state array. The processor may calculate a score associated with each record based on the respective mapping and may provide a subset of the set of records in response to the query based on the score associated with each record.

Abstract: Example implementations relate to database records associated with a trie. For example, a computing device may include at least one processor to receive a query string and determine, based on the query string, a lookup string associated with a plurality of words in an order. The at least one processor may identify, based on the lookup string and a trie associated with the lookup string, a set of records from a database of records, each record in the set being associated with a score that is associated with a degree to which each record matches the query string, wherein the set is identified based on a penalty value calculated for each record based on a hypothesized collection of typing errors sufficient to transform a prefix of a string associated with each record into the lookup string. The at least one processor may provide a result based on the set.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a group of most frequent elements in a dataset, calculating features of each of the most frequent elements in the dataset, applying a trained model to the features of each of the most frequent elements, and generating a list of predicted relevant elements from the list of most frequent elements. The method further comprises determining at least one element-chain group for each predicted relevant element and a group score for the element-chain-group, ordering a plurality of element-chain groups for the dataset based on the group score for each of the element-chain groups, and identifying a predetermined number of element-chain groups to be outputted to a user.

Abstract: An example method is of image processing provided in according with one implementation of the present disclosure. The method includes receiving an image, placing a window across the image, and computing a set of all occurring grayscale values within the window. The method further includes computing a threshold value based on the set of all occurring grayscale values within the window and determining an output pixel value of at least one pixel from the window based on the threshold value.

Abstract: An example method comprises performing for each class from a plurality of classes: constructing binary training set for the class, the binary training set including labeled cases for that class from the main training set other labeled cases from the main training set; training classifier for the class on the binary training set; computing a local calibration threshold using scores of the labeled cases in the binary training set; and adjusting all scores of the label cases in the binary training set with the local calibration threshold to meet a global decision threshold. The method also comprises determining, with the processor, a global hierarchical calibration threshold by using the adjusted scores for all classes to optimize a performance measurement of all trained classifiers. The method further comprises classifying, with the processor, a new case by using a previously trained classifier, a local calibration threshold, and the global hierarchical calibration threshold.

Abstract: Examples herein involve generating a spiral visualization of items of a dataset. Dimensions of item indicators corresponding to items of a dataset are analyzed. Starting coordinates for a spiral path for a spiral visualization of the items are determined based on the dimensions of the item indicators and a display area of a display device. A spiral path may be calculated based on the starting coordinates and the dimensions of the item indicators. The spiral visualization may be generated by rendering the item indicators on the spiral path to present a first item indicator of the item indicators from a corresponding first item of the list at the starting coordinates and a last item indicator of the item indicators of a corresponding last item on the list on the spiral path toward the center of the spiral.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises receiving a training dataset of cases, where each of a plurality of classes is associated with a set of labeled cases in the training dataset. The method also comprises defining a proper subset of classes in the training dataset, and training a first classifier model on the proper subset of classes in the training dataset. The method further comprises testing the first classifier model on at least one class in the training dataset that was excluded from the proper subset, and determining a performance measurement of the first classifier model.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a group of most frequent elements in a dataset, calculating features of each of the most frequent elements in the dataset, applying a trained model to the features of each of the most frequent elements, and generating a list of predicted relevant elements from the list of most frequent elements. The method further comprises determining at least one element-chain group for each predicted relevant element and a group score for the element-chain-group, ordering a plurality of element-chain groups for the dataset based on the group score for each of the element-chain groups, and identifying a predetermined number of element-chain groups to be outputted to a user.

Abstract: A tag is set as a tag in focus in response to detecting a user selection of the tag. An item in an item view of a user interface is associated with the tag in focus in response to the user selecting the item.

Abstract: Example implementations relate to database records associated with a trie. For example, a computing device may include at least one processor to receive a query string and determine, based on the query string, a lookup string associated with a plurality of words in an order. The at least one processor may identify, based on the lookup string and a trie associated with the lookup string, a set of records from a database of records, each record in the set being associated with a score that is associated with a degree to which each record matches the query string, wherein the set is identified based on a penalty value calculated for each record based on a hypothesized collection of typing errors sufficient to transform a prefix of a string associated with each record into the lookup string. The at least one processor may provide a result based on the set.

Abstract: Examples herein involve generating a spiral visualization of items of a dataset. Dimensions of item indicators corresponding to items of a dataset are analyzed. Starting coordinates for a spiral path for a spiral visualization of the items are determined based on the dimensions of the item indicators and a display area of a display device. A spiral path may be calculated based on the starting coordinates and the dimensions of the item indicators. The spiral visualization may be generated by rendering the item indicators on the spiral path to present a first item indicator of the item indicators from a corresponding first item of the list at the starting coordinates and a last item indicator of the item indicators of a corresponding last item on the list on the spiral path toward the center of the spiral.

Abstract: Example implementations relate to records based on bit-shifting. For example, a computing device may include at least one processor to receive a query, create a normalized query, and generate a mapping of the normalized query for each record in a set of records in a database using a state array, the mapping being based on bit-shifting of bits associated with similarities between each character in the normalized query and each character in a field of the respective record in the set of records, each successive level of the state array indicating a stronger match between the query and the respective record than an immediately prior level in the state array. The processor may calculate a score associated with each record based on the respective mapping and may provide a subset of the set of records in response to the query based on the score associated with each record.

Abstract: A technique for updating road maps is disclosed. A number of GPS traces can be matched with a number of roads in a map. Matched GPS traces may be processed by a matched segment module to produce proposed changes to the map. The map can be updated using a map updating module based on the proposed changes from the matched segment module. Unmatched GPS traces may be processed by an unmatched segment module to produce proposed changes to the map. The map can be updated using a map updating module based on the proposed changes from the unmatched segment module. The proposed changes to the map may include metadata defining new roads in the map, new intersections in the map, updates to turn restrictions in the map, updates to the allowable directional traffic flow on the roads within the map, updates to road closures in the map.

Abstract: According to an example, intent based clustering may include generating a plurality of clusters based on an analysis of categories of features of data used to generate the clusters with respect to an order of each of the features by determining whether a number of samples of the data for a category of the categories meets a specified criterion.

Abstract: According to an example, intent based clustering may include classifying objects based on training objects, and clustering the objects to determine initial clusters. The classification and initial clustering may be used to determine modified clusters.

Abstract: An example method is of image processing provided in according with one implementation of the present disclosure. The method includes receiving an image, placing a window across the image, and computing a set of all occurring grayscale values within the window. The method further includes computing a threshold value based on the set of all occurring grayscale values within the window and determining an output pixel value of at least one pixel from the window based on the threshold value.

Abstract: According to an example, term chain clustering may include receiving a set of training cases from a known category, and receiving a set of unlabeled cases that are to be analyzed with respect to the known category. A plurality of terms of the set of training cases from the known category, and the set of unlabeled cases that are to be analyzed, may be analyzed using a term scoring function to generate a score for each of the plurality of terms. A highest scoring term may be selected from the analyzed terms based on the score for each of the plurality of terms. A selected set that includes cases from the set of unlabeled cases that include the highest scoring term may be generated.

Abstract: The present disclosure provides a computer-implemented method of processing Web activity data. The method includes obtaining a collection of Web activity data generated by a plurality of users at a plurality of Webpages, wherein the Webpages are from a plurality of unaffiliated Websites. The method also includes extracting a plurality of search terms from the Web activity data and associating each of the plurality of search terms with a corresponding Webpage. The method also includes generating statistical data from the Web activity data based, at least in part, on the search terms, the statistical data corresponding to the online activity at one or more Webpages.