From Big Data to Big Service

Xu, Xiaofei; Sheng, Quan Z.; Zhang, Liang-Jie; Fan, Yushun; Dustdar, Schahram

doi:10.1109/mc.2015.182

Cited by 85 publications

(41 citation statements)

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“…Zhang et al developed a MapReduce‐based IDPSO for QoS‐aware large scale service composition. Xu et al presented a novel approach based on MIP for QoS‐aware BSS. Wu et al proposed a MapReduce‐based skyline approach for QoS‐aware WSC.…”

Section: Related Workmentioning

confidence: 99%

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A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Bhaskar

Jatoth

Gangadharan

et al. 2019

Concurrency and Computation

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Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.

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Section: Related Workmentioning

confidence: 99%

“…Big services (BS) are collection of interrelated web services across virtual and physical domains, processing the Big data . By aggregating these BS from different domains, a service provider designs a service composition strategy to produce a composite service that addresses the requirements of a customer.…”

Section: Introductionmentioning

confidence: 99%

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Bhaskar

Jatoth

Gangadharan

et al. 2019

Concurrency and Computation

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“…Big service is an interdisciplinary field that requires the support of many technologies, such as cloud computing, business process management, big data, Internet of things, software engineering, service computing, and data mining. This kind of services is considered big in terms of functionalities and data‐processing capabilities, as well as their abilities to execute across not only different layers and clouds, but also different domains …”

Section: Other Issues In Multicloud Environmentmentioning

confidence: 99%

Multicloud service composition: A survey of current approaches and issues

Lahmar¹,

Mezni

2018

J Software Evolu Process

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During the last decade, cloud computing became a natural choice to host and provide various computing resources as on‐demand services. To better satisfy user requirements, cloud services may be combined while considering the constraints of the virtualized environment, including security policies, resources availability, and interoperability. Extensive surveys have been conducted to study the major issues related to the cloud service composition problem. However, very few works have studied such issues in a multicloud setting. To fill this gap, we provide in this paper a systematic literature review on multicloud service composition. We start with a background on service composition in single clouds. Then, we present the multicloud taxonomy, and we study how service composition was tackled by researchers in multicloud environments. Finally, we identify the challenges and the requirements of multicloud service composition, as well as the future directions.

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“…Data mining techniques aimed at discovering relevant patterns in datasets can be integrated into big data infrastructures, appearing additional open issues regarding distributed mining, time evolving data and visualization, among others [27], either at the data, model, or system levels [28]. In addition, progress in data analysis is moving the paradigm of big data to the concept of big service where complex service ecosystems can speed up data processing, scale up with data volume, improve adaptability and extensibility despite data diversity and uncertainty and turn raw, low-level data into actionable knowledge [29].…”

Section: A Proposal For Personalized Vibrotactile Feedback To Supportmentioning

confidence: 99%

Toward Personalized Vibrotactile Support When Learning Motor Skills

Santos

2017

Algorithms

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Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user's needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument.

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From Big Data to Big Service

Cited by 85 publications

References 0 publications

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Multicloud service composition: A survey of current approaches and issues

Toward Personalized Vibrotactile Support When Learning Motor Skills

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