International Business Machines Corporation

Alexander Lang

Andreas Brodt

Oliver Schiller

Thomas F Boehme

Reinhold Geiselhart

Michael Wurst

Christoph Lingenfelder

Christoph H Sieb

Bernhard Mitschang

Ruben Pulido De Los Reyes

Felix Hamborg

Knut Stolze

Peter Gerstl

Verena Henrich

Ulrich Hofmann

Magdalena Leshtanska

A method selectively activates a resource to accommodate an advanced emotion. A supervisor computer receives a first piece of content, and then applies an emotion classifier to the first piece of content in order to create a first concept/emotion/sentiment/time tuple. The supervisor computer creates a second concept/emotion/sentiment/time tuple for a second piece of content, and compares the first and second tuples. If the concept in the first piece of content matches the concept in the second piece of content but that at least one of the emotion, sentiment, and time of the first piece of content does not match the emotion, sentiment, and time of the second piece of content, the supervisor computer determines that the emotion of the second piece of content is an advanced emotion that is not expressed by the first or second pieces of content, and activates a resource that accommodates the advanced emotion.

Selectively activating a resource by detecting emotions through context analysis

A computer-implemented method, a computer program product, and a computer system for refining an initial search request to a content provider. The computer-implemented method includes receiving the initial search request from a user, submitting the initial search request to the content provider, receiving from the content provider a set of sample documents and source identifiers for respective ones of the sample documents, applying a topic model to the set of the sample documents to obtain a topic representation, presenting the topic representation to the user, receiving from the user topic relevance scores for respective ones of the topics, and classifying the data sources according to the topic relevance scores to obtain source relevance scores for respective ones of the data sources. The computer-implemented method further includes, based on the source relevance scores, determining a refined search request having an increased selectivity on documents covering a highest-scoring one of the topics.

Refining a search request to a content provider

A mechanism is provided for updating attribute value information for a data table. The data table involves a set of attributes. The data table has multiple records associated with multiple data blocks. Attribute value information is associated with each data block of the multiple data blocks. The attribute value information is descriptive of at least one attribute of the set of attributes. A first data block may be marked as modified in response to a delete process for deleting from a first data block of the multiple data blocks a record having at least one attribute value that is used for generating the attribute value information of the first data block. In response to scanning the marked first data block, the attribute value information associated with the first data block may be updated based on the scan results.

Self-maintaining effective value range synopsis in presence of deletes in analytical databases

Aspects include operating a distributed database that includes multiple computing nodes that are connected via a network for exchanging data, and a database management system for managing a source dataset and a target dataset. The target dataset is stored in a distributed fashion across the multiple computing nodes. The method includes repeatedly, using the multiple computing nodes, checking for a matching condition between a source element selected from a source dataset and one of multiple target elements in the target dataset. The multiple computing nodes are used to calculate a command set of a merged command list of commands to merge the source element with the target element using the matching condition. The command set is executed to merge the source element with the target dataset before checking for the matching condition between a next source element selected from the multiple source element and one of the multiple target elements.

Merging of distributed datasets

Techniques are disclosed for determining reasons underlying insights gleaned from multi-dimensional data. In one embodiment, a contingency table is accessed that represents multiple dimensions of the data, in order to identify one or more insights. One or more dimensions, other than the represented dimensions, are evaluated to identify one or more reasons underlying a first insight of the one or more insights, and the one or more reasons are output.

Insight determination and explanation in multi-dimensional data sets

A new data mining model (DMM) is created having at least one of the following characteristics: quality and complexity. The new DMM is handled as a candidate for storing in a storage device if a predefined criterion for the characteristics is met. The sum of the sizes of the new DMM and already stored DMMs is determined. In response to the sum falling below a storage limit, the new DMM is stored in the storage device. In response to the sum exceeding the storage limit, a decision is taken based on priorities of the DMMs which DMMs to store in the storage device.

Selective storing of mining models for enabling interactive data mining

Computerized methods, data processing systems, and computer program products for storing of data mining models (DMMs) are provided. A new DMM is created having at least one of the following characteristics: quality and complexity. The new DMM is handled as a candidate for storing in a storage device if a predefined criterion for the characteristics is met. The sum of the sizes of the new DMM and already stored DMMs is determined. In response to the sum falling below a storage limit, the new DMM is stored in the storage device. In response to the sum exceeding the storage limit, a decision is taken based on priorities of the DMMs which DMMs to store in the storage device. The priorities depend at least on access frequencies of the DMMs. Upon a data mining request, a corresponding DMM is determined and a user is requested to confirm that data mining is to proceed if quality of the determined DMM does not fulfill a further predefined criterion.

A computerized method and system of document analysis. The method and system categorise documents according to a taxonomy. This is accomplished by rating training documents on a lower level by associating either of the following predicates to a training document: either correct, inbound, outbound, or unassigned, Rating categories are established on a lower level by determining precision/recall values for each category, and generating higher level category rating attributes from the lower-level rating steps. This is done by associating one or more of: aa) weak category, bb) existing source/sink relationship between categories, cc) close categories to the categories, and deriving an overall quality measure for the training base from the lower-level and higher-level rating step. The lower-level and higher-level evaluation results are stored. The quality measure is used to determine action proposals to improve the training base as either one or more of: aa) modifying the number of categories by adding a new category or deleting an existing category, or bb) splitting a category in one or more new categories, or cc) merging a category with another one, or dd) modifying the number of training documents of a category by adding or removing some of them, and optionally, providing a means to automatically carry out the above steps and review the results including the ability to restore the previous state.

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Stuttgart, Germany

Alexander Lang