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.
Highlights
Database management systems (DBMS) emerged as a flexible and cost-effective solution to information organization, maintenance and access problems found in organizations
Together with other approaches in the domain and in industry, we propose an approach and tool named ExSchema16 that enables the automatic discovery of schemata from polyglot persistence applications
Data management must be revisited for designing strategies that couple the characteristics of novel architectures with users’ preferences. In this context we identify three key scientific challenges: (i) data access and processing guided by Service Level Agreements (SLA) contracts, where data are produced by services and devices connected on heterogeneous networks; (ii) estimation and reduction in temporal, economic and energy consumption cost for accessing and processing data; (iii) optimization of data processing guided by SLA contracts expressed using cost models as reference
Summary
Database management systems (DBMS) emerged as a flexible and cost-effective solution to information organization, maintenance and access problems found in organizations (e.g., business, academia and government). The evolution of data models and the consolidation of distributed systems made it possible to develop mediation infrastructures [109] that enable transparent access to multiple data sources through querying, navigation and management facilities. Examples of such systems are multi-databases, data warehouses, Web portals deployed on Internet/Intranets, polyglot persistence solutions [78]. The DBMS of the future must enable the execution of algorithms and of complex processes (scientific experiments) that use huge data collections (e.g., multimedia documents, complex graphs with thousands of nodes) This calls for a thorough revision of the hypotheses underlying the algorithms, protocols and architectures developed for classic data management approaches [31].
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