Big Data Analytics With Service-Oriented Architecture

Big Data Analytics ◽

Dominant Mode ◽

Volume Velocity ◽

It Systems ◽

Trading Partners ◽

Integration Of Services ◽

Service Oriented ◽

Data Explosion

This chapter focuses on Big Data and its relation with Service-Oriented Architecture. We start with the introduction to Big Data Trends in recent times, how data explosion is not only faced by web and retail networks but also the enterprises. The notorious “V's” – Variety, volume, velocity and value can cause a lot of trouble. We emphasize on the fact that Big Data is much more than just size, the problem that we face today is neither the amount of data that is created nor its consumption, but the analysis of all those data. In our next step, we describe what service-oriented architecture is and how SOA can efficiently handle the increasingly massive amount of transactions. Next, we focus on the main purpose of SOA here is to meaningfully interoperate, trade, and reuse data between IT systems and trading partners. Using this Big Data scenario, we investigate the integration of Services with new capabilities of Enterprise Architectures and Management. This has had varying success but it remains the dominant mode for data integration as data can be managed with higher flexibility.

2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) ◽

Service-Oriented Architecture for Big Data Analytics in Smart Cities

10.1109/ccgrid.2018.00052 ◽

2018 ◽

Cited By ~ 6

Author(s):

Jameela Al-Jaroodi ◽

Nader Mohamed

Keyword(s):

Big Data ◽

Data Analytics ◽

Smart Cities ◽

Big Data Analytics ◽

Connecting the clinical IT infrastructure to a service-oriented architecture of medical devices

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2017-0021 ◽

2018 ◽

Vol 63 (1) ◽

pp. 57-68 ◽

Cited By ~ 3

Author(s):

Björn Andersen ◽

Martin Kasparick ◽

Hannes Ulrich ◽

Stefan Franke ◽

Jan Schlamelcher ◽

...

Keyword(s):

Medical Devices ◽

Point Of Care ◽

Qualitative Evaluation ◽

It Infrastructure ◽

It Systems ◽

Service Oriented ◽

Clinical Environments ◽

Integrated Operating Room ◽

Ieee 11073

AbstractThe new medical device communication protocol known as IEEE 11073 SDC is well-suited for the integration of (surgical) point-of-care devices, so are the established Health Level Seven (HL7) V2 and Digital Imaging and Communications in Medicine (DICOM) standards for the communication of systems in the clinical IT infrastructure (CITI). An integrated operating room (OR) and other integrated clinical environments, however, need interoperability between both domains to fully unfold their potential for improving the quality of care as well as clinical workflows. This work thus presents concepts for the propagation of clinical and administrative data to medical devices, physiologic measurements and device parameters to clinical IT systems, as well as image and multimedia content in both directions. Prototypical implementations of the derived components have proven to integrate well with systems of networked medical devices and with the CITI, effectively connecting these heterogeneous domains. Our qualitative evaluation indicates that the interoperability concepts are suitable to be integrated into clinical workflows and are expected to benefit patients and clinicians alike. The upcoming HL7 Fast Healthcare Interoperability Resources (FHIR) communication standard will likely change the domain of clinical IT significantly. A straightforward mapping to its resource model thus ensures the tenability of these concepts despite a foreseeable change in demand and requirements.

Analytics-as-a-Service (AaaS)

Web Services ◽

10.4018/978-1-5225-7501-6.ch065 ◽

2019 ◽

pp. 1262-1281

Author(s):

Chitresh Verma ◽

Rajiv Pandey

Keyword(s):

Big Data ◽

Case Studies ◽

Data Analytics ◽

Data Science ◽

Business Processes ◽

Big Data Analytics ◽

Major Branch ◽

Implementation Level ◽

Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Structuring SOA Governance

Business Strategy and Applications in Enterprise IT Governance ◽

10.4018/978-1-4666-1779-7.ch004 ◽

2012 ◽

pp. 56-73

Author(s):

Michael Niemann ◽

André Miede ◽

Wolfgang Johannsen ◽

Nicolas Repp ◽

Ralf Steinmetz

Keyword(s):

Holistic Approach ◽

Generic Model ◽

Legal Regulations ◽

It Infrastructure ◽

Market Conditions ◽

Service Oriented Architectures ◽

It Systems ◽

Companies’ IT Systems are confronted with constantly changing market conditions, new competitive threats and a growing number of legal regulations. The service-oriented architecture (SOA) paradigm provides a promising way to address these challenges at the level of a company’s IT infrastructure. These challenges, as well as the management of the newly introduced complexity and heterogeneity, are targeted by SOA Governance approaches. In recent years, a number of concrete frameworks for SOA Governance addressing these issues have been proposed. There is no holistic approach considering all proposed elements, consolidating them in order to form a universally applicable model. In this contribution, we motivate SOA Governance, investigate and compare different approaches, identify common concepts, and derive a generic model for governance of Service-oriented Architectures.

Advances in Business Information Systems and Analytics - Exploring Enterprise Service Bus in the Service-Oriented Architecture Paradigm ◽

Analytics-as-a-Service (AaaS)

10.4018/978-1-5225-2157-0.ch003 ◽

2017 ◽

pp. 26-45

Author(s):

Chitresh Verma ◽

Rajiv Pandey

Keyword(s):

Big Data ◽

Case Studies ◽

Data Analytics ◽

Data Science ◽

Business Processes ◽

Big Data Analytics ◽

Major Branch ◽

Implementation Level ◽

Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

A Service-Oriented Foundation for Big Data

International Journal of Systems and Service-Oriented Engineering ◽

10.4018/ijssoe.2020010101 ◽

2020 ◽

Vol 10 (1) ◽

pp. 1-17

Author(s):

Zhaohao Sun

Keyword(s):

Big Data ◽

Business Intelligence ◽

Data Analytics ◽

Big Data Analytics ◽

Business Analytics ◽

Technological Characteristics ◽

Service Oriented ◽

Technological Level ◽

Multi Level ◽

Big Service

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

Journal of Cases on Information Technology ◽

10.4018/jcit.2020100104 ◽

2020 ◽

Vol 22 (4) ◽

pp. 60-74

Author(s):

Emmanuel Wusuhon Yanibo Ayaburi ◽

Michele Maasberg ◽

Jaeung Lee

Keyword(s):

Cloud Computing ◽

Big Data ◽

Competitive Advantage ◽

Data Analytics ◽

Big Data Analytics ◽

Unique Contribution ◽

Data Services ◽

Business Environments ◽

Digital Era ◽

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

Advances in Data Mining and Database Management - Handbook of Research on Big Data and the IoT ◽

Big Data and IoT Opportunities for Small and Medium-Sized Enterprises (SMEs)

10.4018/978-1-5225-7432-3.ch005 ◽

2019 ◽

pp. 77-88

Author(s):

Siti Aishah Mohd Selamat ◽

Simant Prakoonwit ◽

Reza Sahandi ◽

Wajid Khan

Keyword(s):

Big Data ◽

Internet Of Things ◽

21St Century ◽

Data Analytics ◽

New Technologies ◽

Big Data Analytics ◽

Technological Advancement ◽

New Wave ◽

Advanced Technologies ◽

Data Explosion

The advancement of technology and emergence of internet of things (IoT) has exponentially caused a data explosion in the 21st century era. As such, the arrival of IoT is set to revolutionize the development of the small and medium-sized enterprise (SME) organizations by shaping it into a more universal and integrated ecosystem. Despite evidential studies of the potential of advanced technologies for businesses, the SMEs are apprehensive towards new technologies adoption such as big data analytics and IoT. Therefore, the aim of this chapter is to provide a holistic study of big data and IoT opportunities, challenges, and applications within the SMEs context. The authors hope that the outcome of this study would provide foundational information on how the SMEs can partake with the new wave technological advancement and in turn, spurring more SMEs for adoption.

Advances in Business Information Systems and Analytics - Enterprise Information Systems for Business Integration in SMEs ◽

Challenges and Trends Towards an Effective Application of ERP and SCM Systems in SMEs

10.4018/978-1-60566-892-5.ch021 ◽

2010 ◽

pp. 376-399

Author(s):

Dimitrios Gagalis ◽

Panayiotis Tahinakis ◽

Nicolaos Protogeros ◽

Dimitrios Ginoglou

Keyword(s):

Service Providers ◽

Background Information ◽

Future Trends ◽

Application Service Providers ◽

Organizational Issues ◽

International Trends ◽

Application Service ◽

It Systems ◽

Small and medium-sized enterprises (SMEs) are considered as both the backbone and the main driving force of economic development and innovation. Technology is playing an increasingly significant role in the success or failure of SMEs. The purpose of this chapter is to present international trends and challenges on the field of ERP and SCM systems, thus to: (a) record background information on legacy and current supply chain IT systems for SMEs, (b) discuss the importance of both ERP and SCM systems and the complementarities of ERP and SCM systems, (c) present survey conclusions of ERP and SCM systems adoption in various industries and countries, mainly in Europe and reveal the most prominent trends and barriers, (d) identify the technologies that are used to provide integrated view of information for SMEs, with emphases on both technological and organizational dimensions and recommendations to SMEs and (e) provide future trends, possible future areas of work and conclusions. Contemporary SMEs must carefully examine integration approaches and their technological and organizational issues such as hidden integration costs and management of change considered with human organizational concerns, cultures and business objectives. Application Service Providers, Web Services and Service Oriented Architecture as well as ERP and SCM application’s maturity and open source software solutions, especially for SMEs requirements, are amongst the anticipating future trends in the field.