This paper presents a survey of data replication strategies in cloud systems. Based on the survey and reviews of existing classifications, we propose another classification of replication strategies based on the following five dimensions: (i) static vs. dynamic, (ii) reactive vs. proactive workload balancing, (iii) provider vs. customer centric, (iv) optimal number vs. dynamic adjustment of the replica factor and (v) objective function. Ideally, a good replication strategy must simultaneously consider multiple criteria: (i) the reduction of access time, (ii) the reduction of the bandwidth consumption, (iii) the storage resource availability, (iv) a balanced workload between replicas and (v) a strategic placement algorithm including an adjusted number of replicas. Therefore, selecting a data replication strategy is a classic example of multiple criteria decision making problems. The taxonomy we present can be a useful guideline for IT managers to select the data replication strategy for their organization.

Cloud providers aim to maximise their profits while satisfying tenant requirements, e.g., performance. The relational database management systems face many obstacles in achieving this goal. Therefore, the use of NoSQL databases becomes necessary when dealing with heterogeneous workloads and voluminous data. In this context, we propose a new data replication strategy that balances the workload of nodes and dynamically adjusts the number of replicas while the provider profit is taken into account. Result analysis shows that the proposed strategy reduces the resource consumption, which improves the provider profit while satisfying the tenant performance requirement.

Purpose – The overwhelming speed and scale of digital media production greatly outpace conventional indexing methods by humans. The management of Big Data for e-library speech resources requires an automated metadata solution. The paper aims to discuss these issues. Design/methodology/approach – A conceptual model called semantic ontologies for multimedia indexing (SOMI) allows for assembly of the speech objects, encapsulation of semantic associations between phonic units and the definition of indexing techniques designed to invoke and maximize the semantic ontologies for indexing. A literature review and architectural overview are followed by evaluation techniques and a conclusion. Findings – This approach is only possible because of recent innovations in automated speech recognition. The introduction of semantic keyword spotting allows for indexing models that disambiguate and prioritize meaning using probability algorithms within a word confusion network. By the use of AI error-training procedures, optimization is sought for each index item. Research limitations/implications – Validation and implementation of this approach within the field of digital libraries still remain under development, but rapid developments in technology and research show rich conceptual promise for automated speech indexing. Practical implications – The SOMI process has been preliminarily tested, showing that hybrid semantic-ontological approaches produce better accuracy than semantic automation alone. Social implications – Even as testing proceeds on recorded conference talks at the University of Tebessa (Algeria), other digital archives can look toward similar indexing. This will mean greater access to sound file metadata. Originality/value – Huge masses of spoken data, unmanageable for a human indexer, can prospectively find semantically sorted and prioritized indexing – not transcription, but generated metadata – automatically, quickly and accurately.

Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support the decision process. This paper aims to elaborate an intelligent decision support system (IDSS) that provides relevant assistance to urban planners in urban projects. This research addresses the use of new techniques that contribute to intelligent decision making: machine learning classifiers, naïve Bayes classifier, and agglomerative clustering. Finally, a prototype is being developed to concretize the proposition.

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