Semi-automatic distribution pattern modeling of web service compositions using semantics

Enterprise systems are frequently built by combining a number of discrete Web services together, a process termed composition. There are a number of architectural configurations or distribution patterns, which express how a composed system is to be deployed. Previously, we presented a Model Driven Architecture using UML 2.0, which took existing service interfaces as its input and generated an executable Web service composition, guided by a distribution pattern model. In this paper, we propose using Web service semantic descriptions in addition to Web service interfaces, to assist in the semi-automatic generation of the distribution pattern model. Web services described using semantic languages, such as OWL-S, can be automatically assessed for compatibility and their input and output messages can be mapped to each other

Investigations into the model driven design of distribution patterns for web service compositions

Increasingly, distributed systems are being used to provide enterprise level solutions with high scalability and fault tolerance These solutins are often built using Web servces that are composed to perform useful business functions Acceptance of these composed systems is often constrained by a number of non-functional properties of the system such as availability, scalability and performance There are a number of drstribution patterns that each exhibit different non-functional charactmstics These patterns are re-occuring distribution schemes that express how a system is to be assembled and subsequently deployed. Traditional approaches to development of Web service compositions exhibit a number of Issues Firstly, Web service composition development is often ad-hoc and requires considerable low level coding effort for realisatlon Such systems often exhibit fixed architectures, making maintenance difficult and error prone Additionally, a number of the non-funchonal reqwements cannot be easily assessed by exammng low level code. In this thesis we explicitly model the compositional aspects of Web service compositions usmg UML Activity diagrams Ths approach uses a modehng and transformation framework, based on Model Dnven Software Development (MDSD), going from high level models to an executable system The framework is guided by a methodological framework whose primary artifact is a distribution pattern model, chosen from the supplied catalog. Our modelling and transfomation framework improves the development process of Web service compositions, with respect to a number of criteria, when compared to the traditional handcrafted approach Specifically, we negate the coding effort traditionally associated with Web service composition development Maintenance overheads of the solution are also slgnificantly reduced, while improved mutability 1s achieved through a flexible architecture when compared with existing tools We also improve the product output from the development process by exposing the non-functional runtime properties of Web service compositlons using distribution patterns

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

An Intelligent Expert System for Decision Analysis and Support in Multi-Attribute Layout Optimization

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc

Decision-Making with Multi-Step Expert Advice on the Web

This thesis deals with solving multi-step tasks by using advice from experts, which are algorithms to solve individual steps of such tasks. We contribute with methods for maximizing the number of correct task solutions by selecting and combining experts for individual task instances and methods for automating the process of solving tasks on the Web, where experts are available as Web services. Multi-step tasks frequently occur in Natural Language Processing (NLP) or Computer Vision, and as research progresses an increasing amount of exchangeable experts for the same steps are available on the Web. Service provider platforms such as Algorithmia monetize expert access by making expert services available via their platform and having customers pay for single executions. Such experts can be used to solve diverse tasks, which often consist of multiple steps and thus require pipelines of experts to generate hypotheses. We perceive two distinct problems for solving multi-step tasks with expert services: (1) Given that the task is sufficiently complex, no single pipeline generates correct solutions for all possible task instances. One thus must learn how to construct individual expert pipelines for individual task instances in order to maximize the number of correct solutions, while also taking into account the costs adhered to executing an expert. (2) To automatically solve multi-step tasks with expert services, we need to discover, execute and compose expert pipelines. With mostly textual descriptions of complex functionalities and input parameters, Web automation entails to integrate available expert services and data, interpreting user-specified task goals or efficiently finding correct service configurations. In this thesis, we present solutions to both problems: (1) We enable to learn well-performing expert pipelines assuming available reference data sets (comprising a number of task instances and solutions), where we distinguish between centralized and decentralized decision-making. We formalize the problem as specialization of a Markov Decision Process (MDP), which we refer to as Expert Process (EP) and integrate techniques from Statistical Relational Learning (SRL) or Multiagent coordination. (2) We develop a framework for automatically discovering, executing and composing expert pipelines by exploiting methods developed for the Semantic Web. We lift the representations of experts with structured vocabularies modeled with the Resource Description Framework (RDF) and extend EPs to Semantic Expert Processes (SEPs) to enable the data-driven execution of experts in Web-based architectures. We evaluate our methods in different domains, namely Medical Assistance with tasks in Image Processing and Surgical Phase Recognition, and NLP for textual data on the Web, where we deal with the task of Named Entity Recognition and Disambiguation (NERD)