De la gestion de bases de données à la gestion de grands espaces de données

Collet, Christine; Amann, Bernd; Bidoit, Nicole; Bouzeghoub, Mokkrane; Doucet, Anne; Gross-Amblard, David; Petit, Jean-Marc; Hacid, Mohand-Saïd; Vargas-Solar, Genoveva

doi:10.3166/isi.18.4.11-31

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This calls for a thorough revision of the hypotheses underlying the algorithms, protocols and architectures developed for classic data management approaches [31]. In the following sections we discuss some of these issues focusing mainly on the way data management services of different granularities are delivered by different DBMS architectures.…”

Section: Introductionmentioning

confidence: 99%

Big Data Management: What to Keep from the Past to Face Future Challenges?

2017

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%