Abstract: The explosion-proof lamp provided includes a light source cavity and a drive cavity independent of each other. The drive cavity is has a power driver and a junction box, is connected to the light source cavity via a conducting wire, and drives and controls a light source in the light source cavity to be turned on. The light source cavity includes a heat sink, a glass cover, and the light source. The glass cover and the heat sink are connected to form the cavity, and the light source is mounted in the cavity. The heat sink includes a heat dissipation bottom plate and a plurality of heat dissipation fins annularly mounted on the heat dissipation bottom plate, absorbs heat generated in the light source cavity, and dissipates the heat into an external environment.

Abstract: A method for sharing access to data from a cloud-based analytics engine may include receiving, at a first client, a message from a second client. The message may include an image of a data presentation and metadata associated with the data presentation. The first client may respond to a selection of the image of the data presentation by accessing the cloud-based analytics engine to retrieve, based on the metadata associated with the data presentation, data associated with the data presentation. The data presentation may generated for display at the first client based on the data retrieved from the cloud-based analytics engine. The data presentation displayed at the first client may be interactive to enable an application of one or more modifications to the data presentation displayed at the first client. Related systems and articles of manufacture are also provided.

Abstract: A method for sharing access to data from a cloud-based analytics engine may include receiving, at a first client, a message from a second client. The message may include an image of a data presentation and metadata associated with the data presentation. The first client may respond to a selection of the image of the data presentation by accessing the cloud-based analytics engine to retrieve, based on the metadata associated with the data presentation, data associated with the data presentation. The data presentation may generated for display at the first client based on the data retrieved from the cloud-based analytics engine. The data presentation displayed at the first client may be interactive to enable an application of one or more modifications to the data presentation displayed at the first client. Related systems and articles of manufacture are also provided.

Abstract: A system includes reception of a request to display a data chart. In response to the request, a cached version of the data chart is retrieved from a memory, the cached version of the data chart is displayed, and while the cached version of the data chart is displayed, current data of the data chart is requested from a remote data source. Also while the cached version of the data chart is displayed, an active version of the data chart is rendered based on the current data. The displayed cached version is then replaced with a display of the rendered active version.

Abstract: A system includes reception of a request to display a data chart. In response to the request, a cached version of the data chart is retrieved from a memory, the cached version of the data chart is displayed, and while the cached version of the data chart is displayed, current data of the data chart is requested from a remote data source. Also while the cached version of the data chart is displayed, an active version of the data chart is rendered based on the current data. The displayed cached version is then replaced with a display of the rendered active version.

Abstract: A system and method for managing business intelligence data is described. In some example embodiments, the system extracts data and metadata from a business intelligence file, generates a data bundle of the data and metadata, generates an application bundle based on the date bundle, and generates an interactive document using the data bundle and application bundle.

Abstract: Techniques for obtaining data from unstructured data for a structured data collection include receiving unstructured data that includes text; identifying an attribute associated with a structured data collection; obtaining at least one of historical data associated with the attribute or additional data associated with a user of the computing system; identifying one or more terms from the unstructured data as being associated with the attribute based on at least one of the historical data or the additional data; and storing the identified one or more terms in a data record of the unstructured data collection.

Abstract: Techniques for processing semi-structured data include receiving semi-structured data, a value of the semi-structured data being associated with a key; identifying a structured data collection, the structured data collection being associated with a category and having a plurality of attributes; obtaining at least one of historical data associated with the plurality of attributes or additional data associated with a user of the computing system; mapping the key to an attribute of the plurality of attributes based on at least one of the historical data or the additional data; and storing the value in a cell of a data record of the structured data collection, the cell being associated with the attribute that was mapped to the key.

Abstract: Techniques for disambiguating data using contextual and historical information include determining that data is potentially associated with two or more classifications of a plurality of classifications; obtaining contextual information associated with the data; obtaining historical information associated with a user of the computing system; and determining that the data is more likely associated with one classification of the two or more classifications than other classifications of the two or more classifications based on the contextual information and the historical information.

Abstract: Techniques for obtaining data from unstructured data for a structured data collection include receiving unstructured data that includes text; identifying an attribute associated with a structured data collection; obtaining at least one of historical data associated with the attribute or additional data associated with a user of the computing system; identifying one or more terms from the unstructured data as being associated with the attribute based on at least one of the historical data or the additional data; and storing the identified one or more terms in a data record of the unstructured data collection.

Abstract: Techniques for disambiguating data using contextual and historical information include determining that data is potentially associated with two or more classifications of a plurality of classifications; obtaining contextual information associated with the data; obtaining historical information associated with a user of the computing system; and determining that the data is more likely associated with one classification of the two or more classifications than other classifications of the two or more classifications based on the contextual information and the historical information.

Abstract: Techniques for processing semi-structured data include receiving semi-structured data, a value of the semi-structured data being associated with a key; identifying a structured data collection, the structured data collection being associated with a category and having a plurality of attributes; obtaining at least one of historical data associated with the plurality of attributes or additional data associated with a user of the computing system; mapping the key to an attribute of the plurality of attributes based on at least one of the historical data or the additional data; and storing the value in a cell of a data record of the structured data collection, the cell being associated with the attribute that was mapped to the key.

Abstract: A system and method for managing business intelligence data is described. In some example embodiments, the system extracts data and metadata from a business intelligence file, generates a data bundle of the data and metadata, generates an application bundle based on the date bundle, and generates an interactive document using the data bundle and application bundle.

Abstract: A computer readable storage medium includes executable instructions to receive a report document at a report query engine. The report document includes a definition of a report. The report document is processed to extract a remote location for a data source driver for the report. A request for the data for the report is transmitted via a consumer across a communication channel to the remote location. The data is received. A report instance is created from the data. The report instance includes the data processed in accordance with the definition of the report.

Abstract: A computer readable medium storing executable instructions includes a metadata view module. The metadata view module has a data foundation module to facilitate data abstraction of enterprise data, where the enterprise data is stored in diverse native formats. A business element module facilitates the logical grouping of the enterprise data to form business elements and a business view module facilitates the logical grouping of business elements.