Microsoft Technology Licensing, LLC

Bryan Dove

Jonathan Alan Handler

Dongmei Zhang

He Huang

Xiao Bi Liang

Haidong Zhang

Umesh Madan

Mehul Y Shah

Eugene Lee

Zhitao Hou

Robert May

Michael Todd Gillam

Ali Reza Emami

Eric Gunnerson

Kishore Chaliparambil

Michael J Bortnick

Chi Kit Chan

Imran Mohiuddin

Steven J White

David Fusari

Sayan Dev Pathak

Duncan Paul Robertson

Khan Mohammad Siddiqui

Craig Frederick Feied

Pierre N Martin

Prabhu KrishnaMoorthy

Dongbai Guo

Dobroslav K Kolev

Kurt Arley Luke Thorne

Ben Chronister

Implementations are described related to users directly manipulating visual depictions of aspects of a data set displayed in a user interface to generate or modify visual representations of the data set, such as a chart. The direct manipulation may be either through a computer input device, (e.g., a keyboard or mouse), or based on other input data, such as a touch input. Aspects of a data set include dimensions, which may be used to filter the data set, and metrics, which are quantifiable elements within the data set. In various implementations, input such as drag-and-drop operations corresponding to visual depictions of aspects of a data set may be interpreted in generating or modifying charts.

Direct manipulation interface for data analysis

An example for processing patient image data workflows can temporarily serially arrange a set of semantic labeling modules in a patient image data workflow pipeline responsive to receiving an event trigger. The example can also remove the set of modules from the patient image data workflow pipeline responsive to receiving an event completion trigger.

Metadata extraction pipeline

Database storage of hierarchically structured messages is facilitated based on structural semantic reflection of the message and automatic reconciliation of the messages. The structural semantics of an incoming message may be assessed and database storage provisioned based on the structural semantic reflection of the message. The system may auto-adapt over time as incoming messages from a known source change and automatically generate code which applies the sequential logic to a stream of messages in order to represent the latest state for a given context. Furthermore, the hierarchical semantics of messaging formats may be applied to a flexible set of database structures that represent the raw contents of the messages.

Semantic reflection storage and automatic reconciliation of hierarchical messages

The described implementations relate to expressing terminologies in a hierarchical form. One implementation can receive a terminology that can include concept-code pairs. For example, each of the concept-code pairs can include a concept and a code that is assigned to the concept by the terminology. The implementation can map the concepts to levels of a hierarchical ontology, and associate some of the concepts across different levels of the hierarchical ontology. The implementation can also provide programmatic access to the concept-code pairs of the terminology via the hierarchical ontology.

Programmatic access to terminologies expressed in hierarchical form

The described implementations relate to updating views in a database system. One implementation can identify an active view that retrieves records from a database and determine one or more physical tables in the database that are referenced by the active view. The implementation can track one or more columns in the physical tables that are used by the active view and identify a change to the physical tables in a transaction log of the database. If the change does not affect the one or more columns, the change can be treated as an ignorable change for the active view.

Data change tracking and event notification

The described implementations relate to synchronizing records between databases. One implementation can cause historical data of a source database to be recorded as entries on a transaction log and can subsequently cause new data of the source database to be recorded on the transaction log in a same manner as the historical data. This implementation can also identify a distinct attribute associated with an individual entry and generate a message that reflects the individual entry.

Synchronizing records between databases

A de-identification system is described herein for converting original messages into de-identified messages. The de-identification system leverages original message-inception-functionality which operates as a gateway for providing original messages for use by a production environment. Namely, the de-identification system includes a transformation module that receives the original messages from the original message-inception functionality. The transformation module then converts instances of sensitive information contained in the original messages into non-sensitive information, to produce the de-identified messages. A de-identified environment can consume the de-identified messages with high confidence that the messages have been properly sanitized. This is because the de-identification work has been performed at a well-contained quarantine level of the message processing functionality.

Functionality for providing de-identified data

An information management system (IMS) is described herein that provides supplemental information to a user who is interacting with an application module. In one implementation, the IMS includes a collection system, an information extraction module, and a notification system. The collection system provides collected information from one or more information sources. The information extraction module extracts application information from the application module, even though the application module may lack a native interface for providing the application information. The analysis engine determines supplemental information to present to the user based on the collected information and the application information. And the notification information provides notification information which alerts the user to the existence of the supplemental information. In one scenario, the IMS can be applied in a healthcare-related domain.

Functionality for providing clinical decision support

A data consumer is provided with a system that enables automatic generation of a virtual database schema in order to support a customized pattern of data consumption across one or more source databases. In one embodiment, a metadata manager component is configured to facilitate creation of a first data tag and a second data tag. A schema definition manager component is configured to facilitate creation of a data entity that groups the first data tag with the second data tag. A virtual schema generation service component generates the virtual database schema by analyzing the data entity and, based on the grouping of the first data tag with the second data tag, automatically generating and storing a database query statement that, when executed, causes a retrieval of data consistent with data location indicators included in the first and second data tags.

Automatic generation of virtual database schemas

Technology is described for associating semantic meaning with data elements. The system can include a messaging module configured to receive a message having data elements. A storage module can store the data elements from the message in a structured format. A message dictionary can be configured to identify a type of the message received and to lexically identify data elements of the message using the message dictionary and the type of message. In addition, a taxonomy module can be configured to provide a semantic meaning for the data elements of the lexically identified portions of the message. Further, a data definition tag repository can store data definition tags and link the message dictionary, the taxonomy, and storage location of the data elements in the storage module. The data definition tags can enable the semantic meaning of data elements to be queried.

Association of semantic meaning with data elements using data definition tags

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Bryan Dove