Data Mining - Concepts and Techniques.

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.

Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services

Big data applications in operations/supply-chain management

https://hal.archives-ouvertes.fr/hal-01715576/document

A survey on health monitoring systems for health smart homes

Enterprise Architectures for the Digital Transformation in Small and Medium-sized Enterprises

Applications with Service-oriented Enterprise Architectures in the Cloud are emerging and will shape future trends in technology and communication. The development of such applications integrates Enterprise Architecture and Management with Architectures for Services & Cloud Computing, Web Services, Semantics and Knowledge-based Systems, Big Data Management, among other Architecture Frameworks and Software Engineering Methods. In the present work in progress research, we explore Service-oriented Enterprise Architectures and application systems in the context of Big Data applications in cloud settings. Using a Big Data scenario, we investigate the integration of Services and Cloud Computing architectures with new capabilities of Enterprise Architectures and Management. The underlying architecture reference model can be used to support semantic analysis and program comprehension of service-oriented Big Data Applications. Enterprise Services Computing is the current trend for powerful large-scale information systems, which increasingly converge with Cloud Computing environments. In this paper we combine architectures for services with cloud computing. We propose a new integration model for service-oriented Enterprise Architectures on basis of ESARC - Enterprise Services Architecture Reference Cube, which is our previous developed service-oriented enterprise architecture classification framework, with MFESA - Method Framework for Engineering System Architectures - for the design of service-oriented enterprise architectures, and the systematic development, diagnostics and optimization of architecture artifacts of service-oriented cloud-based enterprise systems for Big Data applications.

Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud

SmartLife ecosystems are emerging as intelligent user-centered systems that will shape future trends in technology and communication. Biological metaphors of living adaptable ecosystems provide the logical foundation for self-optimizing and self-healing run-time environments for intelligent adaptable business services and related information systems with service-oriented enterprise architectures. The present research in progress work investigates mechanisms for adaptable enterprise architectures for the development of service-oriented ecosystems with integrated technologies like Semantic Technologies, Web Services, Cloud Computing and Big Data Management. With a large and diverse set of ecosystem services with different owners, our scenario of service-based SmartLife ecosystems can pose challenges in their development, and more importantly, for maintenance and software evolution. Our research explores the use of knowledge modeling using ontologies and flexible metamodels for adaptable enterprise architectures to support program comprehension for software engineers during maintenance and evolution tasks of service-based applications. Our previous reference enterprise architecture model ESARC--Enterprise Services Architecture Reference Cube--and the Open Group SOA Ontology was extended to support agile semantic analysis, program comprehension and software evolution for a SmartLife applications scenario. The Semantic Browser is a semantic search tool that was developed to provide knowledge-enhanced investigation capabilities for service-oriented applications and their architectures.

Adaptable Enterprise Architectures for Software Evolution of SmartLife Ecosystems

The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.

Approaches to the Evolution of SOA Systems

This book presents emerging trends in the evolution of service-oriented and enterprise architectures. New architectures and methods of both business and IT are integrating services to support mobility systems, Internet of Things, Ubiquitous Computing, collaborative and adaptive business processes, Big Data, and Cloud ecosystems. They inspire current and future digital strategies and create new opportunities for the digital transformation of next digital products and services. Services Oriented Architectures (SOA) and Enterprise Architectures (EA) have emerged as a useful framework for developing interoperable, large-scale systems, typically implementing various standards, like Web Services, REST, and Microservices. Managing the adaptation and evolution of such systems presents a great challenge. Service-Oriented Architecture enables flexibility through loose coupling, both between the services themselves and between the IT organizations that manage them. Enterprises evolve continuously by transforming and extending their services, processes and information systems. Enterprise Architectures provide a holistic blueprint to help define the structure and operation of an organization with the goal of determining how an organization can most effectively achieve its objectives. The book proposes several approaches to address the challenges of the service-oriented evolution of digital enterprise and software architectures.

Emerging Trends in the Evolution of Service-Oriented and Enterprise Architectures

Software interoperability is a pressing need to allow governments and businesses to function efficiently. The most commonly recommended technology for interoperability is Services Oriented Architecture (SOA) implemented using web services. Several authors have argued that SOA systems may be particularly challenging to maintain, largely due to difficulties in program comprehension. Program comprehension for SOA could be aided by appropriate software tools to provide information to SOA maintainers. However, there is little experience regarding the questions that SOA maintainers will need to ask. This paper describes use of a prototype SOA search tool in an informal requirements elicitation study to gather feedback from practicing programmers about what SOA maintainers will want to know. Several specific information needs were identified, including the need for a compact way of representing data types used in services, and the need for ontology support to help understand the many different elements and attributes in web services descriptions.

Eman El-Sheikh

Papers

Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud

Adaptable Enterprise Architectures for Software Evolution of SmartLife Ecosystems

Approaches to the Evolution of SOA Systems

Emerging Trends in the Evolution of Service-Oriented and Enterprise Architectures

Understanding Interoperable Systems: Challenges for the Maintenance of SOA Applications