Abstract: Techniques for configuring a Building Energy Management System (BEMS) using knowledge encoded in BMS point lists are provided. In one aspect, a method for configuring a BEMS of a site is provided. The method includes the following steps. A knowledge base is derived from subject matter expertise. The knowledge base is used to extract one or more building characteristics from a point list of a building management system (BMS) that are not directly available from the BMS point list. The BEMS is configured using the one or more building characteristics extracted from the BMS point list.

Abstract: Techniques for configuring a Building Energy Management System (BEMS) using knowledge encoded in BMS point lists are provided. In one aspect, a method for configuring a BEMS of a site is provided. The method includes the following steps. A knowledge base is derived from subject matter expertise. The knowledge base is used to extract one or more building characteristics from a point list of a building management system (BMS) that are not directly available from the BMS point list. The BEMS is configured using the one or more building characteristics extracted from the BMS point list.

Abstract: A method for selecting an analysis procedure for a value series, including displaying a value series on a computer display monitor, receiving one or more sequences of user provided annotations, where the annotations overlay at least a sub-interval of the value series on the computer display monitor, using the sequences of user provided annotations to select an optimal value series analysis method from a set of value series analysis methods, where selecting an optimal value series analysis method includes determining parameter values for the optimal value series analysis method, and presenting the selected optimal value series analysis method and parameters, and the optimal reconstruction of the annotation sequences to the user.

Abstract: A method for accelerating time series data base queries includes segmenting an original time series of signal values into non-overlapping chunks, where a time-scale for each of the chunks is much less than the time scale of the entire time series, representing time series signal values in each chunk as a weighted superposition of atoms that are members of a shape dictionary to create a compressed time series, storing the original time series and the compressed time series into a database, determining whether a query is answerable using the compressed time series or the original time series, and whether answering the query using the compressed time series is faster. If answering the query is faster using the compressed representation, the query is executed on weight coefficients of the compressed time series to produce a query result, and the query result is translated back into an uncompressed representation.

Abstract: A method for accelerating time series data base queries includes segmenting an original time series of signal values into non-overlapping chunks, where a time-scale for each of the chunks is much less than the time scale of the entire time series, representing time series signal values in each chunk as a weighted superposition of atoms that are members of a shape dictionary to create a compressed time series, storing the original time series and the compressed time series into a database, determining whether a query is answerable using the compressed time series or the original time series, and whether answering the query using the compressed time series is faster. If answering the query is faster using the compressed representation, the query is executed on weight coefficients of the compressed time series to produce a query result, and the query result is translated back into an uncompressed representation.

Abstract: Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

Abstract: Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

Abstract: Embodiments of the disclosure include a method for journey planning including receiving a journey planning request. The request includes an origin and a destination in a transportation network. The method also includes calculating an optimized journey plan based on the journey planning request and a route map corresponding to the transportation network and identifying a potential event in the transportation. The method further includes monitoring the transportation network for the potential event and based on detecting an execution of the potential event, calculating an improved journey plan.

Abstract: Embodiments of the disclosure include a method for journey planning including receiving a journey planning request, the journey planning request having an origin and a destination in a transportation network. The method also includes calculating an optimized journey plan by identifying a plurality of routes through the transportation network from the origin to the destination and determining an uncertainty associated with each of the plurality of routes. Calculating an optimized journey plan also includes evaluating a robustness of each of the plurality of routes to the uncertainty associated with each of the plurality of routes and selecting the optimized journey plan based on the journey planning request and the robustness of each of the plurality of routes.

Abstract: Embodiments of the disclosure include a computer system for journey planning, the computer system includes a journey planning server configured to perform a method. The method includes receiving a journey planning request, the journey planning request having an origin and a destination in a transportation network. The method also includes calculating an optimized journey plan by identifying a plurality of routes through the transportation network from the origin to the destination and determining an uncertainty associated with each of the plurality of routes. Calculating an optimized journey plan also includes evaluating a robustness of each of the plurality of routes to the uncertainty associated with each of the plurality of routes and selecting the optimized journey plan based on the journey planning request and the robustness of each of the plurality of routes.

Abstract: According to an exemplary embodiment of the present invention, a method to configure a network of classifiers includes configuring a plurality of classifiers in a network of classifiers, such that the configuring associates a plurality of operating points with each output branch, associating a different quality profile with each output point for each output branch in the plurality of classifiers, and storing the configured network of classifiers and quality profile associations in the computer system to be used for classifying future input data according to the network configuration and quality profile association. Each classifier comprises executable code that classifies the data. Each of said classifiers has a plurality of the output branches that each output the data to another classifier. Each operating point comprises a probability of determining that a data item input to the classifier has a particular characteristic and a probability that said determination falsely detected the characteristic.

Abstract: Disclosed are a method and a computer program storage product for dynamically stabilizing a stream processing system. The method includes receiving at least one computing resource allocation target. A plurality of downstream processing elements and an upstream processing element are associated with at least one input buffer. Each of the downstream processing elements consumes data packets produced by the upstream processing element received on an output stream associated with the upstream processing element. A fastest input rate among each downstream processing element in the plurality of downstream processing elements is identified. An output rate of the upstream processing element is set to the fastest input rate that has been determined for the plurality of downstream processing elements.

Abstract: A computer-implemented method for configuring a classifier structure includes receiving a classifier structure for identifying a feature of input data, adding a plurality of operating points to a parent binary classifier of the classifier structure, and setting the operating points to minimize a total cost based on resources available to the classifier structure. Each operating point controls how data is classified by the parent binary classifier for distribution to the child binary classifiers.

Abstract: A technique for identifying a defect in an object produced by a controllable process is disclosed. A first type of data generated as a result of production of the object by the controllable process is obtained. A second type of data generated as a result of production of the object by the controllable process is obtained. The first type of data and the second type of data are jointly analyzed. A defect is identified in the object based on the joint analysis of the first type of data and the second type of data.

Abstract: Disclosed are a method and a computer program storage product for dynamically stabilizing a stream processing system. The method includes receiving at least one computing resource allocation target. A plurality of downstream processing elements and an upstream processing element are associated with at least one input buffer. Each of the downstream processing elements consumes data packets produced by the upstream processing element received on an output stream associated with the upstream processing element. A fastest input rate among each downstream processing element in the plurality of downstream processing elements is identified. An output rate of the upstream processing element is set to the fastest input rate that has been determined for the plurality of downstream processing elements.

Abstract: Processing test results from a plurality of individual semiconductor testers by analyzing each test result at an adaptive test engine. A centralized system jointly analyzes all the test results from the plurality of individual semiconductor testers. The adaptive test engine or the centralized system identifies, based on the analysis of each test result or the joint analysis of all the test results, one or more of: a test environmental issue, a tester variability issue, a tester calibration issue, a product variability issue, and a manufacturing process variability issue. The adaptive test engine or the centralized system determines whether one or more of the plurality of individual semiconductor testers causes one or more of the identified issues or whether semiconductor products tested by the plurality of individual semiconductor testers causes one or more of the identified issues.

Abstract: A system, method and computer program product for classification of an analog electrical signal using statistical models of training data. A technique is described to quantize the analog electrical signal in a manner which maximizes the compression of the signal while simultaneously minimizing the diminution in the ability to classify the compressed signal. These goals are achieved by utilizing a quantizer designed to minimize the loss in a power of the log-likelihood ratio. A further technique is described to enhance the quantization process by optimally allocating a number of bits for each dimension of the quantized feature vector subject to a maximum number of bits available across all dimensions.

Abstract: A technique for identifying a defect in an object produced by a controllable process. A first type of data generated as a result of production of the object by the controllable process is obtained. A second type of data generated as a result of production of the object by the controllable process is obtained. The first type of data and the second type of data are jointly analyzed. A defect is identified in the object based on the joint analysis of the first type of data and the second type of data. By way of example, the controllable process comprises a semiconductor manufacturing process such as a silicon wafer manufacturing process and the object produced by the semiconductor manufacturing process comprises a processed wafer. The first type of data comprises tool trace data and the second type of data comprises wafer image data. The tool trace data is generated by a photolithographic tool.

Abstract: Disclosed are a method, upstream processing node, and computer readable medium for dynamically stabilizing a stream processing system. The method includes receiving at least one computing resource allocation target. The method further includes determining that an input data flow rate of at least one upstream processing element varies. The computing resource is dynamically allocated to the upstream processing element in response to the input rate of the upstream processing element varying. Data flow is dynamically controlled between the upstream processing element and at least one downstream processing element.

Abstract: A technique for analyzing two or more data streams respectively generated from two or more components of a controllable process includes the following steps. In a first step, a statistical analysis is performed on each of the two or more data streams to generate first analysis results in the form of respective statistical results for the two or more data streams. In a second step, at least a portion of the statistical results from at least one of the two or more data streams is combined with at least a portion of the statistical results from at least another one of the two or more data streams to yield second analysis results. The controllable process is adjustable based on at least one of the first analysis results and the second analysis results.