International Business Machines Corporation

Upendra D Chitnis

Levente Klein

Fernando Jimenez Marianno

Richard D Lawrence

Ildar Khabibrakhmanov

Prem Melville

Wei Zhang

Ehud Aharoni

Yada Zhu

Christoph Lingenfelder

Xiaodong Cui

Yan Liu

Kumar Bhaskaran

Edwin Peter Dawson Pednault

Yehuda Naveh

Wojciech Gryc

Cezar Pendus

Michael Harold Alcorn

Di Chen

Timothy R Bowden

A task-based learning using task-directed prediction network can be provided. Training data can be received. Contextual information associated with a task-based criterion can be received. A machine learning model can be trained using the training data. A loss function computed during training of the machine learning model integrates the task-based criterion, and minimizing the loss function during training iterations includes minimizing the task-based criterion.

Task-oriented machine learning and a configurable tool thereof on a computing environment

Described herein is a method of controlling soil moisture, water accumulation and fertilizer distribution in land. Elevation location data for land areas are extracted from two or more pixels of a topographic image including topographic data. Each pixel represents a land area. Wetness indices for the land areas are determined from the elevation and location data, based on the slope between two or more defined areas of land and an upslope contributing area per unit contour length. At risk defined areas of land, e.g., those at risk of accumulating water are identified based on wetness indices. Water is transported from the at risk defined areas of land to another location. The transporting of water reduces the risk of accumulating water in the at risk defined areas of land and improves crop growth potential in those areas.

Method of controlling soil moisture, water accumulation and fertilizer distribution in land

Techniques for using a scaling relationship between crop drymass and elevation at a farm level to redistribute crop yield data are provided. In one aspect, a method for analyzing crop yield is provided. The method includes the steps of: obtaining crop yield data for a farm; cleansing the crop yield data using a data filter(s), wherein one or more data points are eliminated from the crop yield data by the data filter; and redistributing a value of the data points eliminated from the crop yield data to data points remaining in the crop yield data to create a crop yield distribution for the farm.

Accurately determining crop yield at a farm level

A process of transforming data residing in databases, such as relational databases, into forms suitable as input to data analysis tools, such as predictive modeling tools includes the steps of defining a business process problem to be solved and identifying data requirements. For example, the business process problem may relate to predicting a customer's propensity to make purchases in the future or a store's requirements for inventory in the future. In the process, a computer implemented method is used for automatically transforming data for data analysis such as predictive modeling. Database metadata that describe database tables, their interrelationships, dimensional information, fact tables and measures are accessed. A mining transformation profile is created to encapsulate aggregations and transformation on data stored in relational databases in order to convert the data to forms suitable for predictive mining tools. The mining transformation profile specifies data transformations relative to the data base metadata. Executable data transformation codes is then generated from the database metadata and the mining transformation profile. Execution of this code results in aggregation and transformation of data residing in a database for input to a data analysis tool such as a predictive modeling tool. The data transformation code can be used by, for example, the predictive modeling tool to generate an output that provides a solution to a business process problem.

System and method of transforming data for use in data analysis tools

A method for automatically determining an Internet home page corresponding to a named entity identified by a specified descriptor including building a trained machine-learning model, generating candidate matches from the specified descriptor, wherein each candidate match includes an Internet address, extracting content-based features from websites associated with the Internet addresses of the candidate matches, determining a model score for each candidate match based on the content-based features using the trained machine-learning model, and determining a match from among the candidate matches according to the scores, wherein the match is returned as the Internet home page corresponding to the named entity.

Method and system using machine learning to automatically discover home pages on the internet

A method for identifying companies with specific business objectives that includes using existing sources of company firmographic data to identify a broad set of companies and associated websites, crawling the websites associated with the identified companies and indexing web site content for each of the identified companies with the specific business objective to realize indexed web content. The method further includes joining the company firmographic data with the indexed web content using a business objective common identifier to generate a store of joined structured firmographic data and indexed web content and presenting a display image representation of the store of joined structured firmographic data and indexed web content for user review. The display image further receives user input to score each of said companies identified therein, and using a search interface, querying the store of scored, joined structured firmographic data and indexed web content. The method further includes augmenting the search interface, or search results from a query, with predictive, machine-leaning processes that allow rapid identification of companies possibly missed in the query.

Method and system for identifying companies with specific business objectives

Mechanisms for evaluating at least one setting for a database system are disclosed. The mechanisms include providing at least one configuration derivative including the at least one setting. The configuration derivative is uncommitted. The mechanisms also include running the database system for a period of time. The database system is run using a committed configuration including a plurality of settings for the database system. The mechanisms also include collecting data on performance of the database system based on the at least one configuration derivative during the running of the database.

Performing a non-invasive runtime simulation for a database system

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Danbury, CT, United States of America

Upendra D Chitnis