Figure 1 - uploaded by Jehad Ibrahim
Content may be subject to copyright.
Computer Memory hierarchy, Retrieved from Wikipedia on 24 March 2016, from https://en.wikipedia.org/wiki/Memory_hierarchy.
Source publication
Increase amount of daily data that companies are dealing with, decrease the cost of computer RAM and increase of computer processing power, are the reasons that lead to a new type of databases called In-memory database (IMDB). IMDB now offers a very high speed big data management and processing of real time requests and responses by hosting the who...
Similar publications
This paper presents the use of three-dimensional indexing available in graphic processing units
(GPU), to accelerate algorithms for the approximate solution of systems described by partial
differential equations. These approximations use recurrent equations where dependence of the
neighbor data plays an important role in the computation speed. For...
Citations
Memory (RAM) sizes are increasing day by day with cheaper costs due to emerging technologies, and it will continue to increase with a more affordable price. Therefore, in-memory database (IMDB) system exists as suitable choice for databases due to the high demands for the high-speed distributed database system. Moreover, with growing demand for real-time analytics on big data, requirement of distributed in-memory database systems is growing. Although they may sound trouble-free, in-memory databases bring some issues, making it hard to gain widespread usage. We propose a novel and intrinsic in-memory database system called AxomDB. This paper presents an efficient distributed in-memory database with Online Analytical Processing (OLAP) and Online Transactional Processing (OLTP) query processing capability in simultaneous manner, thus eliminating the need for a data warehouse or batch processing of big-data for analytical purposes. Also, we demonstrate an IMDB system with minimal use of secondary storage devices to ensure data persistence.
With the maturation of technologies such as communication, sensors and networks, very large amounts of data are generated that are available for processing and analysis. With the popularizing process of the Internet of Things (IoT), available data resources will become even more plentiful and diversified in the near future. To provide feasible solutions in data science for the key features of the energy internet, such as energy interconnection and routing, a big data architecture could be utilized in the energy internet infrastructure to provide large-scale analysis of massive various types of data. In this chapter, the utilization of big data in the energy internet infrastructure is explored. A three-layer big data architecture for usage in the energy internet is presented. The characteristics of data utilized in the energy internet and the potential requirements of the energy internet for the big data architecture are studied. Then, analytics methods that could be executed in the energy internet big data infrastructure are introduced. Real-time and offline analyses, as two types of analysis modes for different requirements of application scenarios, are described. Several well-known open-source big data tools are discussed. In addition, the open challenges of utilizing big data in the energy internet are proposed.
