Abstract: Techniques are disclosed for methods and apparatuses for determining when to perform or trigger events. The technique comprises determining a first cost of false positives and a second cost of missed true positives. A Receive Operating Characteristic (ROC) of a prediction model is determined for the occurrence of one or more events. The operational area on the ROC is determined based on the first costs and second costs. A threshold is determined from the ROC and is applied to a detection or prediction function. An event is triggered based on the threshold.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.

Abstract: Systems, methods, and computer-readable media for building ensemble members of a Support Vector Machine (SVM) ensemble in parallel and executing processing in parallel on data allocated to each ensemble member are disclosed. The parallel construction and processing of data of each ensemble member allows a single large SVM calculation to be replaced with many smaller SVM calculations performed in parallel, and thus, may reduce the computational resources required to classify datasets.

Abstract: Some embodiments of the invention provide a mobile device with a novel route prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for the device's user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user.

Abstract: Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.

Abstract: According to an embodiment, a detection apparatus detects a partial series similar to a search pattern from a parameter series including a sequence of parameters. The apparatus includes a local score acquirer, a difference score calculator, an accumulative score calculator, and a determiner. The local score acquirer is configured to acquire a local score representing a likelihood of the parameter in the search pattern for each of the parameters. The difference score calculator is configured to calculate a difference score by subtracting a threshold from the local score for each of the parameters. The accumulative score calculator is configured to calculate an accumulative score by accumulating the difference scores. The determiner is configured to compare the accumulative score with a reference value in size to determine whether the partial series is similar to the search pattern.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.

Abstract: A system includes an interface configured to receive time series data representing information from a plurality of sensors, and a processor configured to construct a behavior model based on the time series data. The processor identifies features in the time series data, divides the time series data of each of the identified features into segments, and extracts feature components from the segments. The processor further constructs a plurality of state graphs, each state graph including components connected by weighted edges, constructs a behavior graph, wherein the state graphs form vertices of the behavior graph, clusters the state graphs in the behavior graph; and selects a representative state graph from each cluster, wherein the behavior model includes the selected state graphs.

Abstract: Techniques for customizing knowledge representation systems including identifying, based on a plurality of concepts in a knowledge representation (KR), a group of one or more concepts relevant to user context information, and providing the identified group of one more concepts to a user. The KR may include a combination of modules. The modules may include a kernel and a customized module customized for the user. The kernel may accessible via a second KR.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.

Abstract: Systems and methods for alerting a third party device according to a result of automated evaluation of a received response packet according to at least one pattern in the data packet are disclosed herein. The system can include a memory having a pattern database and a model database. The system can further include a user device and a content management server. The content management server can receive a response packet and identify one or several patterns within the response packet. Based on the presence or absence of these one or several patterns in the response packet, the content management server can generate an evaluation of the response packet and generate and send an alert with the results of the evaluation.

Abstract: A computer readable medium for analyzing and predicting the future behavior of Organizations, where an embodiment of this invention is comprised of one or more repositories of data which involve comments or other actions by actors with some kind of relationship to a target organization, a repository of metadata relating to this data, a repository of updatable models of organizations, a natural language parsing engine, an engine for generating and comparing the organizational models, and presentations of avatars.

Abstract: Methods, systems and apparatus for assessing the likely status of an operator of a computing device interacting with a server as a human operator or an autonomic computer application, such as a “bot” are described herein. By monitoring at least some data, e.g., biometric data, generated at the client computing device, a comparison can be made between the monitored data and model data relating to human interaction with the computing device. The results of the comparison can lead to a value that represents the likelihood that the monitored data results from human interaction.

Abstract: A method, system, and non-transitory compute readable medium for vector representation of a sequence of items, including receiving a sequence of items from a source, producing a first distributed representation for each item of the sequence, wherein the distributed representation comprises a word vector and a class vector, partitioning the sequence of items into classes, and training the received sequence using the first distributed representation, such that a new distributed representation is produced for which the vector entries of the new distributed representation are amplified when the vector entries of each item correspond to a class of an item to be explained and fractionalizing vector entries of each item that do not correspond to the class of the item to be explained.

Abstract: Methods and systems for refining a process model include determining whether the process model is too dense or too sparse. A predictive model is learned from execution traces to predict an outcome. The predictive model is modified responsive to the determination of whether the process model is too dense or too sparse. A refined process model is refined from updated traces based on attributes present in the modified predictive model.

Abstract: A method is provided for analyzing data storage applied on at least one data storage type. The data to be stored is transmitted in at least one data stream between at least one application instance and the at least one data storage type. The method includes accessing, from the at least one data stream, data selected according to pre-defined rules. Data access patterns are aggregated on the basis of the selected data, where the data access patterns are indicative of the at least one storage type applied. Classifiers are obtained by applying trained classifiers to the aggregated data access patterns. Differences between the obtained classifiers and the trained classifiers are analyzed to determine, that the obtained classifiers are indicative of at least one data storage type other than a presently used data storage type, in case at least one predefined threshold value is exceeded when analyzing the differences.

Abstract: Systems, methods, and apparatus for time series data adaptation, including sensor fusion, are disclosed. For example, a system includes a variational inference machine, a sequential data forecast machine including a hidden state, and a machine learning model. The sequential data forecast machine exports a version of the hidden state. The variational inference machine receives as inputs time series data and the version of the hidden state, and outputs a time dependency infused latent distribution. The sequential data forecast machine obtains the version of the hidden state, receives as inputs the time series data and the time dependency infused latent distribution, and updates the hidden state based on the time series data, the time dependency infused latent distribution, and the version of the hidden state to generate a second version of the hidden state. The time dependency infused latent distribution is input into the machine learning model, which outputs a result.

Abstract: Disclosed are a system and a method for analyzing a result of clustering massive data. An open-source map/reduce framework named Hadoop is used to calculate a silhouette coefficient corresponding to a significance verification index capable of evaluating a result of clustering massive data. To implement the system and the method for analyzing a result of clustering massive data, clustered data is divided into blocks. For all of the blocks, input splits are generated. Then, the generated input splits are assigned to multiple computers. Each computer stores only data of blocks included in an input split assigned in a memory, and calculates a silhouette coefficient for each record. Each computer provides only the calculated silhouette coefficient to an index coefficient calculation apparatus, and enables the index coefficient calculation apparatus to calculate a silhouette coefficient for a cluster. Therefore, the result of clustering the massive data can be rapidly and objectively analyzed.

Abstract: A method includes performing, by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence, converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words, applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document, associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document, and adjusting the course of action responsive to predicting the future subject matter.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve decision tree execution. An example method includes retrieving, with a processor, a decision tree logic expression in a sum-of-products (SOP) form, the decision tree logic expression consuming a first duration to evaluate a dataset, eliminating, with the processor, redundant variables of the decision tree logic expression by transforming the decision tree logic expression into a product-of-sums (POS) form, and evaluating, with the processor, the data set with the decision tree logic expression in the POS form, the decision tree logic expression in the POS form consuming a second duration to evaluate the data set that is less than the first duration.