Big Data Management

This paper explores what a Database Management System (DBMS) suited to the future may look like based on issues that can be seen today, as well as emerging trends and how this system may be created. An apt example includes a system that allows efficient and continuous querying and mining of data flows that can be employed on media with different computing capacities. What human-to-machine communication and interoperability do you think was most beneficial? Consider how, for example, an individual embedded medical device will be included in DBMS as processes get more complex and storage facilities become more distributed. What are some key aspects of DBMS that could benefit future architectures?

Abstract

The emergence of new hardware architectures, and the continuous production of data open new challenges for data management. It is no longer pertinent to reason with respect to a predefined set of resources (i.e., computing, storage and main memory). Instead, it is necessary to design data processing algorithms and processes considering unlimited resources via the "pay-as-you-go" model. According to this model, resources provision must consider the economic cost of the processes versus the use and parallel exploitation of available computing resources. In consequence, new methodologies, algorithms and tools for querying, deploying and programming data management functions have to be provided in scalable and elastic architectures that can cope with the characteristics of Big Data aware systems (intelligent systems, decision making, virtual environments, smart cities, drug personalization). These functions, must respect QoS properties (e.g., security, reliability, fault tolerance, dynamic evolution and adaptability) and behavior properties (e.g., transactional execution) according to application requirements. Mature and novel system architectures propose models and mechanisms for adding these properties to new efficient data management and processing functions delivered as services. This paper gives an overview of the different architectures in which efficient data management functions can be delivered for addressing Big Data processing challenges.

Source: G. Vargas-Solar, J. L. Zechinelli-Martini, and J. A. Espinosa-Oviedo, https://link.springer.com/article/10.1007/s41019-017-0043-3
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