International Business Machines Corporation

Sangeeta T Doraiswamy

Karen Wolfe Brannon

Tanveer Fathima Syeda-Mahmood

Arnon Amir

David James Beymer

David Allan Pease

Mark Andrew Smith

Jun Rao

Latha S Colby

Ryan John Minniear

Wenling Cai

Hetal Thakkar

Mir Hamid Pirahesh

Fatma Ozcan

Marc Hörsken

Embodiments relate to parallel data streaming between a first computer system and a second computer system. Aspects include transmitting a request to establish an authenticated connection between a processing job on the first computer system and a process on the second computer system and transmitting a query to the process on the second computer system over the authenticated connection. Aspects further include creating one or more tasks on the first computer system configured to receive data from the second computer system in parallel and reading data received by the one or more tasks by the processing job on the first computer system.

Parallel data streaming between cloud-based applications and massively parallel systems

Systems and methods providing automated extraction of information contained in video data and uses thereof are described. In particular, systems and associated methods are described that provide techniques for extracting data embedded in video, for example measurement-value pairs of medical videos, for use in a variety of applications, for example video indexing, searching and decision support applications.

Systems and methods for automated extraction of measurement information in medical videos

Processing archive content based on hierarchical classification levels is performed by indexing a first newly archived file of a current set of archive content at a first classification level defining a plurality of types of metadata to be derived, wherein the first classification level is selected from a hierarchy of classification levels having at least two classification levels, indexing a second newly archived file of the current set of archive content at a second classification level, wherein the second classification level includes fewer types of metadata than the first class of metadata, and flagging the second newly archived file of the current set of archive content. By classifying and flagging the second newly archived file differently than the first newly archived file, the system can easily retrieve such data at a later instance to update and/or change the data, re-index the data, and the like.

Processing archive content based on hierarchical classification levels

A method and system for cleansing anomalies from sequence-based data at query time. Sequence-based data such as RFID data is loaded into a database. One or more cleansing rules are received at a cleansing rules engine. The cleansing rule engine converts the cleansing rule(s) to a template that includes logic to compensate for anomalies in the sequence-based data. A query to retrieve the sequence-based data is received by a query rewrite engine. The query rewrite engine rewrites the query by applying the template logic. The rewritten query is executed at query time. The result of the rewritten query execution is identical to the result of executing the original query on a data set generated by applying the cleansing rule to all of the sequence-based data.

Method and system for cleansing sequence-based data at query time

A method for cleansing anomalies from sequence-based data at query time. Sequence-based data such as RFID data is loaded into a database. A cleansing rule is received at a cleansing rules engine. The cleansing rule engine converts the cleansing rule to a template that includes logic to compensate for anomalies in the sequence-based data. A query to retrieve the sequence-based data is received by a query rewrite engine. The query rewrite engine rewrites the query by applying the template logic and an expanded rewrite technique. The rewritten query is executed at query time. The result of the rewritten query execution is identical to the result of executing the original query on a data set generated by applying the cleansing rule to all of the sequence-based data.

Method for cleansing sequence-based data at query time

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San Jose, CA, United States of America

Sangeeta T Doraiswamy