Vishal Dwivedi

The Role of Service Granularity in a Successful SOA Realization A Case Study

This paper presents the case study of a leading US financial Institution International Financial and Brokerage Services (IFBS), which faced SOA realization issues while it followed an inappropriate SOA design strategy. While riding on the SOA hype wave, it implemented thousands of fine grained Web-services without paying much heed to issues like governance, and usage within its business processes. IFBSpsilas service portfolio comprises of a gamut of services which although on paper looked good, but presented a lot of challenges in their usage and maintenance. We address some of these issues in this paper and present our framework for SOA adoption (called INSOAP) which can be effective in a similar end- to-end SOA adoption exercise in an enterprise.

A Model Driven Service Identification Approach for Process Centric Systems

The raison dpsilaetre for Web-services is that they could be used to compose new services. As in process centric systems, wherein blocks of software implement a functional domain through a set of linked activities, a SOA based system modeled through Web-services, requires business services (of low granularity) which implement various business functions. Such business services could not only ensure a high degree of re-use but could also help in realizing service oriented architecture in its true sense. Although today quite a few approaches for the problem exist, but most of them are bottom up solutions, or mainly focus on domain decomposition and composite service formation. Not many of them have yet utilized the full potential of business processes, which form the backbone of any enterprise. In this work we define what a business service would be like, vis-a-vis its corresponding business function, and how it could be realized through data, utility, information and other services. We present our service identification approach which utilizes process maps and service hierarchies and towards the end we discuss some of our current work in building an automation tool (named SQUID) for service identification.

SORASCS: a case study in soa-based platform design for socio-cultural analysis

An increasingly important class of software-based systems is platforms that permit integration of third-party components, services, and tools. Service-Oriented Architecture (SOA) is one such platform that has been successful in providing integration and distribution in the business domain, and could be effective in other domains (e.g., scientific computing, healthcare, and complex decision making). In this paper, we discuss our application of SOA to provide an integration platform for socio-cultural analysis, a domain that, through models, tries to understand, analyze and predict relationships in large complex social systems. In developing this platform, called SORASCS, we had to overcome issues we believe are generally applicable to any application of SOA within a domain that involves technically naïve users and seeks to establish a sustainable software ecosystem based on a common integration platform. We discuss these issues, the lessons learned about the kinds of problems that occur, and pathways toward a solution.

Resolving Data Mismatches in End-User Compositions

Many domains such as scientific computing and neuroscience require end users to compose heterogeneous computational entities to automate their professional tasks. However, an issue that frequently hampers such composition is data-mismatches between computational entities. Although, many composition frameworks today provide support for data mismatch resolution through special-purpose data converters, end users still have to put significant effort in dealing with data mismatches, e.g., identifying the available converters and determining which of them meet their QoS expectations. In this paper we present an approach that eliminates this effort by automating the detection and resolution of data mismatches. Specifically, it uses architectural abstractions to automatically detect different types of data mismatches, model-generation techniques to fix those mismatches, and utility theory to decide the best fix based on QoS constraints. We illustrate our approach in the neuroscience domain where data-mismatches can be fixed in an efficient manner on the order of few seconds.

Information as a Service in a Data Analytics Scenario - A Case Study

In this work we present a case study of a SOA realization exercise at a business information provider firm, which deals with disparate sources of data in-order to provide reliable reports to its clients. Unlike typical enterprise scenarios, where applications are required to be service enabled, the key requirement here was to service enable its data acquisition, quality check, reporting and other processes which are either mostly manual or ETL based workflows. This paper also addresses how shared services, business processes, rules, and semantics are used to provide quality and agility to the internal processes many of which are entirely dependent on the type of data received. The case and the scenario are chosen specifically to emphasize the fact, that mere web-services implementation does not lead to service oriented architecture, but it is the appropriate usage of them.

Foundations and tools for end-user architecting

Within an increasing number of domains an important emerging need is the ability for technically naive users to compose computational elements into novel configurations. Examples include astronomers who create new analysis pipelines to process telescopic data, intelligence analysts who must process diverse sources of unstructured text to discover socio-technical trends, and medical researchers who have to process brain image data in new ways to understand disease pathways. Creating such compositions today typically requires low-level technical expertise, limiting the use of computational methods and increasing the cost of using them. In this paper we describe an approach -- which we term end-user architecting -- that exploits the similarity between such compositional activities and those of software architects. Drawing on the rich heritage of software architecture languages, methods, and tools, we show how those techniques can be adapted to support end users in composing rich computational systems through domain-specific compositional paradigms and component repositories, without requiring that they have knowledge of the low-level implementation details of the components or the compositional infrastructure. Further, we outline a set of open research challenges that the area of end-user architecting raises.

An architectural approach to end user orchestrations

Computations are pervasive across many domains, where end users have to compose various heterogeneous computational entities to perform professional activities. Service-Oriented Architecture (SOA) is a widely used mechanism that can support such forms of compositions as it allows heterogeneous systems to be wrapped as services that can then be combined with each other. However, current SOA orchestration languages require writing scripts that are typically too low-level for end users to write, being targeted at professional programmers and business analysts. To address this problem, this paper proposes a composition approach based on an end user specification style called SCORE. SCORE is an architectural style that uses high-level constructs that can be tailored for different domains and automatically translated into executable constructs by tool support. We demonstrate the use of SCORE in two domains - dynamic network analysis and neuroscience, where users are intelligence analysts and neuroscientists respectively, who use the architectural style based vocabulary in SCORE as a basis of their domain-specific compositions that can be formally analyzed.

Model-Based Assistance for Making Time/Fidelity Trade-Offs in Component Compositions

In many scientific fields, simulations and analyses require compositions of computational entities such as web-services, programs, and applications. In such fields, users may want various trade-offs between different qualities. Examples include: (i) performing a quick approximation vs. an accurate, but slower, experiment, (ii) using local slower execution environments vs. remote, but advanced, computing facilities, (iii) using quicker approximation algorithms vs. computationally expensive algorithms with smaller data. However, such trade-offs are difficult to make as many such decisions today are either (a) wired into a fixed configuration and cannot be changed, or (b) require detailed systems knowledge and experimentation to determine what configuration to use. In this paper we propose an approach that uses architectural models coupled with automated design space generation for making fidelity and timeliness trade-offs. We illustrate this approach through an example in the intelligence analysis domain.

What Ails End-User Composition: A Cross-Domain Qualitative Study

Across many domains, end-users need to compose computational elements into novel configurations to perform their day-to-day tasks. End-user composition is a common programming activity performed by such end-users to accomplish this composition task. While there have been many studies on end-user programming, we still need a better understanding of activities involved in end-user composition and environments to support them. In this paper we report a qualitative study of four popular composition environments belonging to diverse application domains, including: Taverna workflow environment for life sciences, Loni Pipeline for brain imaging, SimMan3G for medical simulations and Kepler for scientific simulations. We interview end-users of these environments to explore their experiences while performing common compositions tasks. We use “Content Analysis” technique to analyze these interviews to explore what are the barriers to end-user composition in these domains. Furthermore, our findings show that there are some unique differences in the requirements of naive end-users vs. expert programmers. We believe that not only are these findings useful to improve the quality of end-user composition environments, but they can also help towards development of better end-user composition frameworks.

End User Architecting

A large number of domains today require end users to compose various heterogeneous computational entities to perform their professional activities. However, writing such end user compositions is hard and error prone. My research explores an improved approach for design, analysis and execution of such end user compositions. I propose a new technique called ‘end user architecting’ that associates end user specifications in a particular domain as instances of architectural styles. This allows cross-domain analyses, systematic support for reuse and adaptation, powerful auxiliary services (e.g., mismatch repair), and support for execution, testing, and debugging. To allow a wider adoption of this technique, we have designed a framework that can be instantiated across a large number of domains, with composition models varying from dataflows, pub-sub, and workflows. This approach can reduce the cost of development of end user composition platforms (compared to developing them from scratch) and improve the quality of end user compositions.