Abstract: Techniques for preparing datasets for geo experiments and improving accuracy of geo experiments are presented herein. The system can access a dataset of a plurality of geographic pairs. Additionally, the system can calculate a first outcome estimate based on a difference in response data and a difference in input data for a first geographic pair. Moreover, the system can calculate a plurality of experimental uncertainty estimates associated with the plurality of geographic pairs during an experimental time interval. The system can access historical data associated with the plurality of geographic pairs. Furthermore, the system can determine a beta value and a trim rate that reduces a sum of the plurality estimates. Subsequently, the system can remove, based on the first outcome estimate and the beta value, the first geographic pair from the plurality of geographic pairs to generate the first subset of geographic pairs.

Abstract: Systems, methods and computer-readable storage media utilized to prepare datasets for geo experiments. One method includes receiving one or more input parameters. The method further includes extracting, from the data, training data. The method further includes calculating a difference in input data and a difference in response data of the training data. The method further includes determining a first plurality of geographic pairs. The method further includes extracting, from the data, evaluation data. The method further includes separating each geographic pair of the first plurality of geographic pairs into a treatment region or a control region for a plurality of simulations of a plurality of different simulation subsets for each of a plurality of different subsets of geographic pairs. The method further includes calculating a plurality of uncertainty estimates. The method further includes selecting a first subset of geographic pairs and providing the selected subset of geographic pairs.

Abstract: Systems, methods and computer-readable storage media utilized to prepare datasets for geo experiments. One method includes receiving one or more input parameters. The method further includes extracting, from the data, training data. The method further includes calculating a difference in input data and a difference in response data of the training data. The method further includes determining a first plurality of geographic pairs. The method further includes extracting, from the data, evaluation data. The method further includes separating each geographic pair of the first plurality of geographic pairs into a treatment region or a control region for a plurality of simulations of a plurality of different simulation subsets for each of a plurality of different subsets of geographic pairs. The method further includes calculating a plurality of uncertainty estimates. The method further includes selecting a first subset of geographic pairs and providing the selected subset of geographic pairs.

Abstract: Systems, methods and computer-readable storage media utilized to prepare experimental datasets for experimental analysis systems. One method includes identifying, by one or more processing circuits, a dataset of a plurality of geographic pairs associated with a geo experiment. The method further includes calculating, by the one or more processing circuits, a difference in input data and a difference in response data between the first geographic region and the second geographic region of each geographic pair. The method further includes calculating, by the one or more processing circuits, a plurality of outcome estimates. The method further includes selecting, by the one or more processing circuits, a first subset of geographic pairs of the plurality of different subsets of geographic pairs based a first outcome estimate of the plurality of outcome estimates that is about a prespecified value on the outcome estimates and providing the selected subset of geographic pairs.

Abstract: Systems and methods for assigning word fragments to lines of text in optical character recognition (OCR) extracted data can include at least one processor obtaining a plurality of word fragments from OCR generated data associated with an image. The at least one processor can determine vertical coordinates of each of the word fragments in the image. The at least one processor can cluster the plurality of word fragments into one or more clusters of word fragments based on the vertical coordinates of the plurality of word fragments. The at least one processor can assign each word fragment of a respective cluster to a corresponding text line based on the clustering.

Abstract: Systems and methods for assigning word fragments to lines of text in optical character recognition (OCR) extracted data can include at least one processor obtaining a plurality of word fragments from OCR generated data associated with an image. The at least one processor can determine vertical coordinates of each of the word fragments in the image. The at least one processor can cluster the plurality of word fragments into one or more clusters of word fragments based on the vertical coordinates of the plurality of word fragments. The at least one processor can assign each word fragment of a respective cluster to a corresponding text line based on the clustering.

Abstract: Systems, methods, and computer-readable storage media that may be used to generate causal models and calculate a selection bias in mixed media. In some embodiments, the selection bias calculation is in search sponsored content in the context of mixed media modeling. In some embodiments, a method for search bias correction is based on the back-door criterion from causal inference.

Abstract: Systems, methods, and computer-readable storage media that may be used to generate causal models and calculate a selection bias in mixed media. In some embodiments, the selection bias calculation is in search sponsored content in the context of mixed media modeling. In some embodiments, a method for search bias correction is based on the back-door criterion from causal inference.

Abstract: Systems and methods of directed item consumption recommendations are disclosed which include generating, with a server, empirical transition matrix data that includes row data for a first item and column data for a second item, and an entry in the empirical transition matrix data for a number of users that acquire the second item after the first item, generating, with the server, metadata transition matrix data by partitioning items for each item metadata type, setting a uniform transition probability for all items in the partition, and summing the uniform transition probabilities across all metadata types, generating, with the server, transition probability matrix data by multiplying the metadata transition matrix data with a weight parameter, adding the result to the empirical transition matrix data, and normalizing each row, and providing item recommendations to a user computing device communicatively coupled to the server according to the generated transition probability matrix data.

Abstract: Systems and methods of directed item consumption recommendations are disclosed which include generating, with a server, empirical transition matrix data that includes row data for a first item and column data for a second item, and an entry in the empirical transition matrix data for a number of users that acquire the second item after the first item, generating, with the server, metadata transition matrix data by partitioning items for each item metadata type, setting a uniform transition probability for all items in the partition, and summing the uniform transition probabilities across all metadata types, generating, with the server, transition probability matrix data by multiplying the metadata transition matrix data with a weight parameter, adding the result to the empirical transition matrix data, and normalizing each row, and providing item recommendations to a user computing device communicatively coupled to the server according to the generated transition probability matrix data.

Abstract: Systems and techniques are provided for optimizing personalized recommendations with longitudinal data and a future objective. An identifier may be received for content items. A user content item history including a list identifying a previously acquired content item may be received. Content item metadata may be received including a correlation between the previously acquired content item and a content item for which an identifier was received, and a correlation between a content item for which an identifier was received and fulfillment of a future objective. A joint probability may be determined for each content item based on the user content item history and the content item metadata, including the probability that the content item will be acquired by the user after being recommended to the user and that a future objective will be fulfilled after the content item is acquired by the user.

Abstract: A method and computer product are presented for identifying Internet worm propagation based upon changes in packet arrival rates at a network connection. First, unsolicited (i.e., packets that were not requested by the receiver) traffic is separated from solicited traffic at the network connection. The unsolicited traffic arrival patterns are monitored and analyzed for any changes. Once changes in the unsolicited traffic arrival patterns are detected, the changes are mathematically analyzed to detect growth trends. The presence of growth trends that follow certain key characteristics indicate whether the changes are due to worm propagation.

Abstract: In one aspect, a processing device of an information processing system is operative to perform high-dimensional stratified sampling of a database comprising a plurality of records arranged in overlapping sub-groups. For a given record, the processing device determines which of the sub-groups the given record is associated with, and for each of the sub-groups associated with the given record, checks if a sampling rate of the sub-group is less than a specified sampling rate. If the sampling rate of each of the sub-groups is less than the specified sampling rate, the processing device samples the given record, and otherwise does not sample the given record. The determine, check and sample operations are repeated for additional records, and samples resulting from the sample operations are processed to generate information characterizing the database.

Abstract: In one embodiment, a counting method of the invention uses an adaptive sketching-update process to compress an unknown cardinality into a counter value that counts the number of binary ones in a hashed bitmap vector. The sketching-update process is probabilistic in nature and uses bit-flip probabilities that are adaptively decreased as the counter value increases. Parameters of the sketching-update process are selected so that the relative error of cardinality estimates obtained based on the counter values is relatively small and substantially constant over a relatively wide range of cardinalities, e.g., from one to about one million. Due to the latter property, the counting method can advantageously be implemented in the form of embedded software that relies on a relatively small, fixed amount of memory.

Abstract: The present invention provides a method of unsupervised learning and location for tracking users in a wireless communication system. One embodiment of the method includes forming a signal map of a geographic area using unlabeled values of one or more signals so that the signal map relates locations in the geographic area to values of the signal(s).

Abstract: A method and computer product are presented for identifying Internet worm propagation based upon changes in packet arrival rates at a network connection. First, unsolicited (i.e., packets that were not requested by the receiver) traffic is separated from solicited traffic at the network connection. The unsolicited traffic arrival patterns are monitored and analyzed for any changes. Once changes in the unsolicited traffic arrival patterns are detected, the changes are mathematically analyzed to detect growth trends. The presence of growth trends that follow certain key characteristics indicate whether the changes are due to worm propagation.

Abstract: A processing device of an information processing system is operative to obtain a plurality of records, documents, web pages or other data objects, and to construct a binary tree using a bipartition procedure in which subsets of the data objects are associated with respective nodes of the tree. Evaluation of a designated modularity for a given one of the nodes of the tree is used as a stopping criterion to prevent further partitioning of that node and to indicate designation of that node as a leaf node of the tree. The resulting leaf nodes of the tree provide a non-overlapping partitioning of the plurality of data objects. The processing device is further operative to perform a neighborhood search on the tree to identify pairs of the plurality of data objects that match the same entity, and to store an indication of the matching pairs of data objects.

Abstract: A method and apparatus are provided for incrementally tracking quantiles in the presence of multiple record types. A method for performing incremental quantile tracking includes receiving a first data record of a first record type and a second data record of a second record type, and updating a quantile probability for a quantile value, based on the first record type of the first data record and the second record type of the second data record, to obtain a new quantile probability for the quantile value.

Abstract: In one aspect, a processing device of an information processing system is operative to perform high-dimensional stratified sampling of a database comprising a plurality of records arranged in overlapping sub-groups. For a given record, the processing device determines which of the sub-groups the given record is associated with, and for each of the sub-groups associated with the given record, checks if a sampling rate of the sub-group is less than a specified sampling rate. If the sampling rate of each of the sub-groups is less than the specified sampling rate, the processing device samples the given record, and otherwise does not sample the given record. The determine, check and sample operations are repeated for additional records, and samples resulting from the sample operations are processed to generate information characterizing the database.

Abstract: In one aspect, a processing device of an information processing system is operative to perform high-dimensional stratified sampling of a database comprising a plurality of records arranged in overlapping sub-groups. For a given record, the processing device determines which of the sub-groups the given record is associated with, and for each of the sub-groups associated with the given record, checks if a sampling rate of the sub-group is less than a specified sampling rate. If the sampling rate of each of the sub-groups is less than the specified sampling rate, the processing device samples the given record, and otherwise does not sample the given record. The determine, check and sample operations are repeated for additional records, and samples resulting from the sample operations are processed to generate information characterizing the database.