An experimental survey on big data frameworks

Inoubli, Wissem; Aridhi, Sabeur; Mezni, Haithem; Maddouri, Mondher; Nguifo, Engelbert Mephu

doi:10.1016/j.future.2018.04.032

Cited by 111 publications

(74 citation statements)

References 48 publications

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“…Researchers in [26] provide a valuable overview on Big Data processing frameworks and their efficiency in Smart City domain. They categorize Big Data processing frameworks according to their programming model, type of data sources, and supported programming languages.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

2019

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Large number of embedded devices, massive volumes of data, users and applications are driving the digital world to move faster than ever. To be competitive in today's digital economy companies have to process large volumes of dynamically changing data at real-time. There are many industries from health-care, e-commerce, insurance and telecommunications with various use cases such as DNA sequencing, capturing customer insights, real-time offers, high-frequency trading, and real-time intrusion detection that have taken the use of Big Data analytics into account to make critical decisions that impact their business [1]. On the other hand, the Internet of Things (IoT) is becoming the primary grounds for data mining and Big Data analytics [2]. With the rapid growth of IoT and its use cases in different domains such as Smart City, Mobile e-Health and Smart Grid, streaming applications are driving a new wave of data revolutions. In most IoT applications the resulting analytics give some feedbacks to the system to improve it [3]. Compared to the other Big Data domains, there is a low-latency cycle between system

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Section: Related Workmentioning

confidence: 99%

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

2019

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“…Currently, Hadoop and Spark are the most used big data processing frameworks. 49 Even using the EMR, we have used a few resources, especially in statistical tools to analyze the data and storage.…”

Section: The Cloud Platformmentioning

confidence: 99%

Integrated system architecture for decision-making and urban planning in smart cities

Pires

Mendes

Quiñonez

2019

International Journal of Distributed Sensor Networks

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Research and development of applications for smart cities are extremely relevant considering the various problems that population growth will bring to large urban centers in the next few years. Although research on cyber-physical systems, cloud computing, embedded devices, sensor and actuator networks, and participatory sensing, among other paradigms, is driving the growth of solutions, there are a lot of challenges that need to be addressed. Based on these observations, in this work, we present an integrated system architecture for decision-making support and urban planning by introducing its building blocks (termed components): sensing/actuation, local processing, communication, cloud platform, and application components. In the sensing/actuation component, we present the major relevant resources for data collection, identification devices, and actuators that can be used in smart city solutions. Sensing/actuation component is followed by the local processing component, which is responsible for processing, decision-making support, and control in local scale. The communication component, as the connection element among all these components, is presented with an emphasis on the open-access metropolitan area network and cellular networks. The cloud platform is the essential component for urban planning and integration with electronic governance legacy systems, and finally, the application component, in which the government administrator and users have access to public management tools, citizen services, and other urban planning resources.

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“…The coprocessor is a native mechanism that can be triggered by inserting new data, deleting old data or updating existing data. The typical implementation of secondary index includes Hindex [29,30] and complementary clustering index (CCI) [31]. …”

Section: Related Workmentioning

confidence: 99%

A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications

Shen

Liao

Dan

et al. 2018

Sensors

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Big data gathered from real systems, such as public infrastructure, healthcare, smart homes, industries, and so on, by sensor networks contain enormous value, and need to be mined deeply, which depends on a data storing and retrieving service. HBase is playing an increasingly important part in the big data environment since it provides a flexible pattern for storing extremely large amounts of unstructured data. Despite the fast-speed reading by RowKey, HBase does not natively support multi-conditional query, which is a common demand and operation in relational databases, especially for data analysis of ubiquitous sensing applications. In this paper, we introduce a method to construct a linear index by employing a Hilbert space-filling curve. As a RowKey generating schema, the proposed method maps multiple index-columns into a one-dimensional encoded sequence, and then constructs a new RowKey. We also provide a R-tree-based optimization to reduce the computational cost of encoding query conditions. Without using a secondary index mode, experimental results indicate that the proposed method has better performance in multi-conditional queries.

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An experimental survey on big data frameworks

Cited by 111 publications

References 48 publications

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

Integrated system architecture for decision-making and urban planning in smart cities

A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications

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