International Business Machines Corporation

Utopus Insights, Inc.

Stuart A Siegel

Amith Singhee

Fook-Luen Heng

Zhiguo Li

Haijing Wang

Jayant R Kalagnanam

Yada Zhu

Amit Dhurandhar

Sameep Mehta

Markus Ettl

Himanshu Gupta

Toshiyuki Hama

Shubir Kapoor

Chandrasekhara K Reddy

Mary Elizabeth Helander

Daniel Patrick Connors

Sriram Lakshminarasimhan

Andrew J Davenport

Tanveer A Faruquie

A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Weather-driven multi-category infrastructure impact forecasting

Methods, systems, and computer program products for multi-level colocation and analytical processing of spatial data on MapReduce are provided herein. A method includes correlating multiple items of spatial data and multiple items of attribute data within a file system to generate multiple blocks of correlated data; colocating each of the multiple blocks of correlated data on a given node within the file system based on a data block placement policy; and clustering multiple replicas generated for each of the multiple data blocks at multiple levels of spatial granularity within the file system.

Multi-level colocation and processing of spatial data on MapReduce

A method for detecting anomalous energy usage of building or household entities. The method applies a number of successively stringent anomaly detection techniques to isolate households that are highly suspect for having engaged in electricity theft via meter tampering. The system utilizes historical time series data of electricity usage, weather, and household characteristics (e.g., size, age, value) and provides a list of households that are worthy of a formal theft investigation. Generally, raw utility usage data, weather history data, and household characteristics are cleansed, and loaded into an analytics data mart. The data mart feeds four classes of anomaly detection algorithms developed, with each analytic producing a set of households suspected of having engaged in electricity theft. The system allows a user to select households from each list or a set based on the intersection of all individual sets.

Detecting electricity theft via meter tampering using statistical methods

A system and method for detecting anomalous energy usage of building or household entities. The method applies a number of successively stringent anomaly detection techniques to isolate households that are highly suspect for having engaged in electricity theft via meter tampering. The system utilizes historical time series data of electricity usage, weather, and household characteristics (e.g., size, age, value) and provides a list of households that are worthy of a formal theft investigation. Generally, raw utility usage data, weather history data, and household characteristics are cleansed, and loaded into an analytics data mart. The data mart feeds four classes of anomaly detection algorithms developed, with each analytic producing a set of households suspected of having engaged in electricity theft. The system allows a user to select households from each list or a set based on the intersection of all individual sets.

A method and system for optimizing modules of a steel manufacturing process includes a plurality of manufacturing modules for a manufacturing process. Each of the modules have a plurality of steps. The plurality of modules include at least an upstream module, a casting module, and a downstream module. Each of the plurality of modules have parameters, and include at least one variable event. The variable event is adjustable for optimization of the manufacturing process while the parameters are being maintained for each of the plurality of modules. A communication system is used for exchanging information between the modules while the manufacturing process is occurring to adjust the at least one variable event for optimizing the manufacturing process.

Algorithmic framework for the integrated optimization of hot strip mill and upstream molten metal operations for a large-scale steel manufacturing plant

There are provided a system, a method and a computer program product for tracking an inventory distribution of a perishable product in a supply chain. The system receives shipment data and point of sale data for the product from every location in the supply chain. The system creates, based on the obtained shipment data and the point of sale data, a network graph that represents a flow of the product in the supply chain. The system identifies an age of unsold inventory of the product at each node of the graph based on the created graph. The system provides a notification to a user at each location that informs the age of the unsold inventory of the product at that location.

Non-instrumented perishable product tracking in a supply chain

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Millburn, NJ, United States of America

Stuart A Siegel