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There is an exponential growth of connected Internet of Things (IoT) devices. These have given rise to applications that rely on real time data to make critical decisions quickly. Enterprises today are adopting cloud at a rapid pace. There…

In the contemporary world of dynamic digital solutions and services, the significance of effective and stable cloud solutions cannot be overestimated. The cloud adaptation is becoming more popular due to mobile advantages, including…

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that…

Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent…

Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance,…

The combination of cloud technology, machine learning, and data visualization techniques allows hybrid enterprise networks to hold massive volumes of data and provide employees and customers easy access to these cloud data. These massive…

The number of mobile devices (e.g., smartphones, wearable technologies) is rapidly growing. In line with this trend, a massive amount of spatial data is being collected since these devices allow users to geo-tag user-generated content.…

Emerging technologies that generate a huge amount of data such as the Internet of Things (IoT) services need latency aware computing platforms to support time-critical applications. Due to the on-demand services and scalability features of…

Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client…

Blockchain and Cloud Computing are two of the main topics related to the distributed computing paradigm, and in the last decade, they have seen exponential growth in their adoption. Cloud computing has long been established as the main…

The growth of large, programatically accessible bibliometrics databases presents new opportunities for complex analyses of publication metadata. In addition to providing a wealth of information about authors and institutions, databases such…

Cloud storage is a widely utilized service for both personal and enterprise demands. However, despite its advantages, many potential users with enormous amounts of sensitive data (big data) refrain from fully utilizing the cloud storage…

Cloud computing is recognized as one of the most promising solutions to information technology, e.g., for storing and sharing data in the web service which is sustained by a company or third party instead of storing data in a hard drive or…

Modern machine learning algorithms need large datasets to be trained. Crowdsourcing has become a popular approach to label large datasets in a shorter time as well as at a lower cost comparing to that needed for a limited number of experts.…

Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which most typically involve collecting data from public-use APIs, often encounter issues when relying on…

At present moment, there is a great interest in development of information systems operating in cloud infrastructures. Generally, many of tasks remain unresolved such as tasks of optimization of large databases in a hybrid cloud…

Hadoop and Spark are widely used distributed processing frameworks for large-scale data processing in an efficient and fault-tolerant manner on private or public clouds. These big-data processing systems are extensively used by many…

Over the past two decades, we have witnessed an exponential increase of data production in the world. So-called big data generally come from transactional systems, and even more so from the Internet of Things and social media. They are…

- Current infrastructures for developing big-data applications are able to process --via big-data analytics-huge amounts of data, using clusters of machines that collaborate to perform parallel computations. However, current infrastructures…

This paper focuses the attention on privacy-preserving identity and access management in multiple Cloud environments, which is an annoying problem in the modern big data era. Within this conceptual context, the paper describes…