Aarthi Natarajan

Service Component Architecture (SCA)

In this chapter, we introduce a framework known as Service Component Architecture (SCA) that provides a technology-agnostic capability for composing applications from distributed services. Building a successful SOA solution in practice can be complex, due to the lack of standards and specifications, especially when integrating many different technology environments. This chapter explores techniques for adopting a consensus on how to describe an assembly of services, and how to implement and access them regardless of the technology.

Web Service Composition: Control Flows

In this chapter, we present BPEL and BPMN as two main languages of Web service composition. Both BPEL and BPMN allow the codification of control flow logic of a composite service. We will introduce the core syntax elements of the two languages and their usage examples. The lab activities will show how to build a simple BPEL service by composing other services to implement a home loan-processing scenario.

Web Service Composition: Overview

In this chapter, we introduce the motivation behind Web service composition technologies – going from an atomic to a composite service. In doing so, we discuss the two main paradigms of multiple service interactions: Web service orchestration and Web service choreography. In the rest of the book, we will focus on Web service orchestration as the main paradigm behind Web service composition techniques.

Web Services – Data Services

In this chapter, we explore the concept of data services. After clarifying the main concepts, we introduce key enabling technologies for building data services, namely XSLT and XQuery. These two XML-based languages are used to transform and query potentially heterogeneous data into well-understood standard XML documents. The lab exercises included at the end of this chapter will guide you to learn the basic syntax and usage scenarios of XSLT and XQuery.

Web Service Composition: Data Flows

In this chapter, we examine the data-flow aspects of Web service composition, which specifies how data is exchanged between services. The data-flow description encapsulates the data movement from one service to another and the transformations applied on this data. We introduce two different paradigms based on the message passing style, namely, blackboard and explicit data flow. We conclude the chapter with a discussion of Mashup applications as a way to implement data-flow oriented service composition.

Introduction to Service Oriented Architecture

In this chapter, we begin by understanding Service Oriented Architecture (SOA), its key values and goals too modern and evolving business ecosystems. We then describe the SOA architectural stack in reference to software application integration layers. This is followed by an introduction to service composition and data-flow techniques, including end-user mashups. This chapter also presents the overall goals, structure and organization of the rest of this book.

Web Services – SOAP and WSDL

In this chapter, SOAP and WSDL are explained as important standards that lay the foundation for standardized descriptions of messages and operations of a Web service. We first describe the core elements of SOAP and WSDL standards with examples, then present how the two standards are fit together to form the common message communication styles, namely RPC and Document. The chapter concludes with a practical exercise covering activities that build a simple Web service and its client application.

A Semantic-Based Analytics Architecture and Its Application to Commodity Pricing

Over the past decade, several sophisticated analytic techniques such as machine learning, neural networks, and predictive modelling have evolved to enable scientists to derive insights from data. Data Science is characterised by a cycle of model selection, customization and testing, as scientists often do not know the exact goal or expected results beforehand. Existing research efforts which explore maximising automation, reproducibility and interoperability are quite mature and fail to address a third criterion, usability. The main contribution of this paper is to explore the development of more complex semantic data models linked with existing ontologies (e.g. FIBO) that enable the standardisation of data formats as well as meaning and interpretation of data in automated data analysis. A model-driven architecture with the reference model that capture statistical learning requirement is proposed together with a prototype based around a case study in commodity pricing.

User-oriented modelling of scientific workflows for high frequency event data analysis

Whether it is research scientists in computational physics, astronomy, genomics or financial services, all these varying disciplines have been challenged by the analysis of Big Data. They are all required to perform multi-step analysis tasks to turn this data into actionable insight, from which critical decisions can be made. Two data processing models that have rapidly evolved in the past decade to support data analysts are Complex Event Processing and Scientific Workflows. Our research proposes a hybrid approach, which extends scientific workflows, to incorporate the handling of event-streams. This model not only aims to provide a more efficient approach to analysing high frequency event streams, but also facilitates conceptual modelling of processes - to enable domain experts to build abstract, exploratory analysis processes in a user-friendly manner without the concerns of underlying technology, and transparently maps them to concrete solutions at run-time.