Web service composition : architecture, frameworks, and techniques

OASIS defines Service Oriented Architecture (SOA) as a paradigm for organizing and utilizing distributed capabilities that may be under the control of different ownership domains. One approach to realize SOA is Web services. A Web service is a software system that has a machine processable Web Services Description Language (WSDL) interface; other systems interact with it using SOAP messages in a manner prescribed by its description. Descriptions enable Web services to be discovered, used by other Web services, and composed into new Web services. Composition is a mechanism for rapid creation of new Web services by reusing existing ones. Web services have functional, behavioral, semantic, and non-functional characteristics. These characteristics have to be considered for composition, as they provide essential information about the services. In order to compose Web services with these characteristics, they have to be described appropriately. However, the existing techniques do not consider all these aspects together for description and composition. This thesis proposes a business model, also referred to as architecture, a description framework, and a composition framework for Web service composition. Techniques for matching, categorizing, and assembling the composite services are also proposed as a part of the composition framework. The architecture, frameworks, and techniques describe, discover, manipulate, and compose Web services by taking into account all their characteristics. The standard Web service business model is extended by the proposed business model to support Web service composition. In the model, based on their demand, the requested Web services are composed by the Web service composer. In the proposed architecture, Web services are described using the description framework languages. The proposed framework combines Semantic Annotations for WSDL and XML Schema (SAWSDL) for functional and semantic description, Message Sequence Charts (MSC) for behavioral description, and a simple and new Non Functional Specification Language (NFSL) for the non-functional properties description of Web services. It uses Higher Order Logic (HOL) for formalizing and integrating the three languages. The role of Web service composer in the architecture is realized by the composition framework. It essentially defines the architecture of the composer. In this framework, matchmaking, categorization, and assembly techniques are used to create the requested composite service. These techniques manipulate the Web services at HOL-level. The formal matchmaking technique discovers the primitive Web services by using a HOL theorem prover. The categorization and the assembly techniques manipulate the matched services and orchestrate the composite service. The concepts of the model, frameworks, and techniques are implemented, and their working is illustrated using case studies. Prototypes of the model's components (extended registry and extended requester) and the composition framework are developed, and their performance is analyzed. Case studies to illustrate the description and the composition frameworks are also presente

An Intermediate Data-driven Methodology for Scientific Workflow Management System to Support Reusability

Automatic processing of different logical sub-tasks by a set of rules is a workflow. A workflow management system (WfMS) is a system that helps us accomplish a complex scientific task through making a sequential arrangement of sub-tasks available as tools. Workflows are formed with modules from various domains in a WfMS, and many collaborators of the domains are involved in the workflow design process. Workflow Management Systems (WfMSs) have been gained popularity in recent years for managing various tools in a system and ensuring dependencies while building a sequence of executions for scientific analyses. As a result of heterogeneous tools involvement and collaboration requirement, Collaborative Scientific Workflow Management Systems (CSWfMS) have gained significant interest in the scientific analysis community. In such systems, big data explosion issues exist with massive velocity and variety characteristics for the heterogeneous large amount of data from different domains. Therefore a large amount of heterogeneous data need to be managed in a Scientific Workflow Management System (SWfMS) with a proper decision mechanism. Although a number of studies addressed the cost management of data, none of the existing studies are related to real- time decision mechanism or reusability mechanism. Besides, frequent execution of workflows in a SWfMS generates a massive amount of data and characteristics of such data are always incremental. Input data or module outcomes of a workflow in a SWfMS are usually large in size. Processing of such data-intensive workflows is usually time-consuming where modules are computationally expensive for their respective inputs. Besides, lack of data reusability, limitation of error recovery, inefficient workflow processing, inefficient storing of derived data, lacking in metadata association and lacking in validation of the effectiveness of a technique of existing systems need to be addressed in a SWfMS for efficient workflow building by maintaining the big data explosion. To address the issues, in this thesis first we propose an intermediate data management scheme for a SWfMS. In our second attempt, we explored the possibilities and introduced an automatic recommendation technique for a SWfMS from real-world workflow data (i.e Galaxy [1] workflows) where our investigations show that the proposed technique can facilitate 51% of workflow building in a SWfMS by reusing intermediate data of previous workflows and can reduce 74% execution time of workflow buildings in a SWfMS. Later we propose an adaptive version of our technique by considering the states of tools in a SWfMS, which shows around 40% reusability for workflows. Consequently, in our fourth study, We have done several experiments for analyzing the performance and exploring the effectiveness of the technique in a SWfMS for various environments. The technique is introduced to emphasize on storing cost reduction, increase data reusability, and faster workflow execution, to the best of our knowledge, which is the first of its kind. Detail architecture and evaluation of the technique are presented in this thesis. We believe our findings and developed system will contribute significantly to the research domain of SWfMSs

Gamers Telling Stories:Understanding Narrative Practices in an Online Community

Abstract / In this article, I introduce a theoretical framework, based on the philosophy of Paul Ricoeur, for grasping how and why members of online communities construct narratives in their communications with one another. This is exemplified through a study of how players from one particular game, World of Warcraft, make sense of their gaming experience, and how they build and uphold a community identity by telling stories online. I argue that in studying and conceptu-alizing these types of texts through the proposed theoretical framework, we can gain insights into the process of the formation of meaning and the building of identity and community in an online setting

Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.Comment: To be published in Conference on Fairness, Accountability, and Transparency FAT* '20, January 27-30, 2020, Barcelona, Spain. ACM, New York, NY, USA, 11 page