FRINK Linked Data Fragments server

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• W2185309163 abstract "In-Memory analytics has brought a paradigm shift in storage and data management in facilitating instant reporting for decision making. Revolution in advanced memory technology, drastic decline in price of memory, and evolution of multi-core processors have changed the orientation of business intelligence query and fetching of data along with the way data is stored and transferred. This article discusses the adoption of in-memory technology, its architecture, and few enabling software of in-memory computing. It also discusses the scope and benefi ts of In-Memory approach. They are associative model, in-memory OLAP,Excel in-memory add-in, in-memory accelerator, and in-memory visual analytics. In associative model, associations are based on the relationships between various data elements .When a user clicks on an item within a data set, the selected items turn green and all associated values turn white. This facilitates users to quickly query all relevant data without the dependency of a predefi ned hierarchy or query path, and are not limited to navigate the analytical data in a predetermined way. Similarly, Excel in-memory add-in allows users to load large volumes of data into Microsoft excel using in-memory. Once the data is within excel, relationships are automatically inferred between the data sets, permitting users to perform on-the fl y sorting, fi ltering, slicing, and dicing of huge data sets, which overcomes some of the technical data volume limits of excel. This approach improves self-service capabilities as it reduces dependency on IT, and lessens the needs for business users to become expert in multi-dimensional structures and techniques. This add-in is dependent on a particular back end data management and portal platform which helps sharing of data and collaboration. In-memory OLAP Approach functions by loading data in-memory, which allows complicated calculations and queries to be computed on-demand resulting in fast response times. If write-back is supported then users can change assumptions on the fl y to support what-if scenarios, which is a specifi c requirement in forecasting and fi nancial planning. In-memory visual analytics combines an in-memory database with a visual data exploration tool allowing users to quickly query data, and reports within a visual and interactive analytics ambience. In-Memory accelerator approach improves query performance within an existing business environment. This accelerator functions by loading data into memory and leveraging pre built indexes to support super fast query response times (2) . There are many In-Memory computing enabling softwares like In-Memory analytics and event processing, In-Memory messaging, In-Memory application platforms, and In-Memory data management. These softwares provide new business ideas and IT challenges. A comparison of traditional data analytics technology and in-memory data analytics technology is given below (Fig. 2). In-Memory Application Platforms" @default.
• W2185309163 created "2016-06-24" @default.
• W2185309163 creator A5091657982 @default.
• W2185309163 date "2013-04-13" @default.
• W2185309163 modified "2023-09-23" @default.
• W2185309163 title "Adoption of In-Memory Analytics" @default.
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