Abstract: A system and method include input of data records to a first trained predictive model to obtain a predicted value associated with each input data record. A model region is then associated with each of the input data records based on the first trained predictive model, the input data records and the predicted values. Enhanced input data records are generated by, for each model region, adding derived values of engineered features associated with the model region to input data records associated with the model region and default values of the engineered features associated with the model region to input training records not associated with the model region. The enhanced input data records are input to a second trained predictive model to obtain an enhanced predicted value associated with each input data record.

Abstract: The present disclosure involves systems, software, and computer implemented methods for generating insights based on numeric and categorical data. One example method includes receiving a request for an insight analysis for a dataset that includes at least one continuous feature and at least one categorical feature. Continuous features can have any value within a range of numerical values and categorical features are enumerated features that can have a value from a predefined set of values. A selection of a first continuous feature for analysis is received, and at least one categorical feature is identified for analysis. A deviation factor and a relationship factor are determined for each identified categorical feature. An insight score is determined for each identified categorical feature that combines the deviation factor and the relationship factor for the categorical feature. The insight score is provided for at least some of the identified categorical features.

Abstract: The present disclosure involves systems, software, and computer implemented methods for ranking time dimensions. One example method includes receiving a request for an insight analysis for a dataset that includes a value dimension and a set of multiple date dimensions. Each date dimension is converted into a time series and a value quality factor is determined for each time series that represents a level of data quality for the time series. A time series informative factor is determined for each time series that represents how informative the time series is within a specified time window. An insight score is determined, for each time dimension, based on the determined value quality factors and the determined time series informative factors. The insight score for the time dimension is provided, for at least some of the time dimensions.

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically recommending one or more frequencies for time series data. One example method includes receiving a request for an insight analysis for an input time series included in a dataset. For each of multiple frequencies to analyze, the input time series is transformed into a frequency time series. An absolute percentage change impact factor and an absolute trend impact factor are determined for each frequency time series. A frequency interest score is determined based on the determined absolute percentage change factors and the determined absolute trend impact factors, for each time frequency time series. The frequency interest score is provided for at least some of the frequency time series.

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically detecting hot areas in heat map visualizations. One example method includes identifying a two-dimensional heat map. The identified two-dimensional heat map is converted to a one-dimensional heat map. Cells of the one-dimensional heat map are clustered using a density-based clustering algorithm to generate at least one dense region of cells. A mean value of cells in each dense region is calculated and the dense regions are sorted by mean value in descending order. An approach for identifying hot areas is selected and the selected approach is used to identify at least one dense region as a hot area of the one-dimensional heat map.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.

Abstract: Techniques for enabling secure access to data using data blocks is described. Computing device(s) can provide instruction(s) to a component associated with an entity, wherein the instruction(s) are associated with an identifier corresponding to a data block of a plurality of data blocks. The computing device(s) can receive, from the component, data associated with the component, wherein the data is associated with the identifier and is indicative of a state of the component. The computing device(s) can store the data in the data block and monitor, using rule(s), changes to the state of the component based at least partly on the data in the data block. As a result, techniques described herein enable near real-time—and in some examples, automatic—reporting and/or remediation for correcting changes to the state of the component using data that is securely accessed by use of data blocks.

Abstract: A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.

Abstract: Techniques for enabling secure access to data using data blocks is described. Computing device(s) can provide instruction(s) to a component associated with an entity, wherein the instruction(s) are associated with an identifier corresponding to a data block of a plurality of data blocks. The computing device(s) can receive, from the component, data associated with the component, wherein the data is associated with the identifier and is indicative of a state of the component. The computing device(s) can store the data in the data block and monitor, using rule(s), changes to the state of the component based at least partly on the data in the data block. As a result, techniques described herein enable near real-time—and in some examples, automatic—reporting and/or remediation for correcting changes to the state of the component using data that is securely accessed by use of data blocks.

Abstract: Techniques for monitoring user experience for cloud-based services, availed via an entity, are described. Server computing device(s) can receive, from a user device using a cloud-based service, data associated with component(s) of the user device, wherein the data is indicative of a user experience associated with the user device while using the cloud-based service. The server computing device(s) can monitor, using rule(s) and based at least partly on the data, changes to a state of the user device. The server computing device(s) can determine, based at least partly on the monitoring, an occurrence of a trigger event associated with a remedial action and can effectuate the remedial action. As a result, techniques described herein enable near real-time—and in some examples, automatic—reporting and/or remediation for preventing and/or correcting changes to the state of the user device that can cause poor user experiences.

Abstract: A centrifuge system including a centrifuge housing having a cavity at least partially surrounded by an outer shell, a central shaft extending into at least a portion of the cavity, a rotary motion source for rotating the centrifuge housing, and a filter concentrically positioned about the central shaft. The filter includes a first layer adjacent to a second layer, wherein the first and second layers are wound into a roll about a central longitudinal opening. The first layer may include at least one contaminant retention layer and the second layer may include at least one flow defining layer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.

Abstract: A system for concise display of query results via thumbnails consisting of images and keywords is disclosed. The query result display system has a query result display module, comprising computer-executable code stored in non-volatile memory, a processor, and a user interface device. The query result display module, the processor, and the user interface device are configured to perform a query of the plurality of indexed documents, determine one or more indicative images for each of a plurality of indexed documents, and provide a set of result documents drawn from the plurality of indexed documents, perform a distinguishing keyword analysis for the set of result documents based on a measure of differentiation, determine one or more differentiating terms for each result document based on the distinguishing keyword analysis, and depict each result document using a representative image incorporating the at least one distinguishing images along with the at least one differentiating terms.

Abstract: A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.

Abstract: A method includes receiving training data including sequential data, determining a plurality of future time points, generating a first prediction by applying a first forecasting algorithm to the training data, generating a second prediction by applying a second forecasting algorithm to the training data, extracting predicted values from the first prediction and the second prediction that corresponds to a future time point of the plurality of future time points, applying a regression model in sequence on each of the plurality of future time points to generate a final predicted value of each of the plurality of future time points, and outputting the final predicted values of the plurality of future time points.

Abstract: A method of arranging a group of adjacent gaming machines in a restricted space is disclosed, each said machine being, at a point of closest approach, a distance of one metre or less from a neighbouring machine in the group.