Big Data Analytic Service Discovery Using Social Service Network with Domain Ontology and Workflow Awareness

2016 ◽  
Author(s):  
T.H. Akila S. Siriweera ◽  
Incheon Paik ◽  
Jia Zhang ◽  
Banage T.G.S. Kumara
Keyword(s):  
Big Data ◽  
Social Service ◽  
Service Discovery ◽  
Domain Ontology ◽  
Service Network ◽  
Data Analytic

Interlinking Distributed Services for Workflow-as-a-Service Based on Linked Data

2013 ◽  
Vol 17 (4) ◽  
pp. 561-572
Author(s):  
Wuhui Chen ◽  
◽  
Incheon Paik ◽  
Tetsuya Tashiro
Keyword(s):  
Social Services ◽  
Social Service ◽  
Linked Data ◽  
Service Discovery ◽  
Global Scale ◽  
Service Network ◽  
Related Services ◽  
Distributed Services ◽  
Composition Problem ◽  
Publishing Services

Services are considered to have had a tremendous impact on the Web as a potential silver bullet for supporting a distributed service-based economy on a global scale. However, despite the outstanding progress, their uptake on a Web scale has been significantly less than initially anticipated. Isolated service islands without links to related services have hampered service discovery and composition. In this paper, we propose a methodology to drive innovation from isolated service islands into a global social service network to connect the service islands for Workflow-as-a-Service. First, we propose Linked social service-specific principles based on Linked data principles for publishing services on the open Web as linked social services, and suggest a new platform for constructing global social service network. We then propose an approach to enable exploiting the global social service network, providing Workflow-as-a-Service. Finally, experimental results show that Linked social service can solve the service composition problem by enabling providing Workflow-as-a-Service based on the global social service network, and has the potential to be the next wave of services.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

PARALLEL COMPUTING OF NUMERICAL SCHEMES AND BIG DATA ANALYTIC FOR SOLVING REAL LIFE APPLICATIONS

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Norma Alias ◽  
Nadia Nofri Yeni Suhari ◽  
Hafizah Farhah Saipan Saipol ◽  
Abdullah Aysh Dahawi ◽  
Masyitah Mohd Saidi ◽  
...  
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Parallel Algorithm ◽  
Sparse Matrices ◽  
Real Life ◽  
Poor Performance ◽  
Equation System ◽  
Numerical Schemes ◽  
Linear Equation System ◽  
Data Analytic

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance.

Sign in / Sign up

Export Citation Format

Share Document