758 research outputs found
Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond
We present a graphical and dynamic framework for binding and execution of
business) process models. It is tailored to integrate 1) ad hoc processes
modeled graphically, 2) third party services discovered in the (Inter)net, and
3) (dynamically) synthesized process chains that solve situation-specific
tasks, with the synthesis taking place not only at design time, but also at
runtime. Key to our approach is the introduction of type-safe stacked
second-order execution contexts that allow for higher-order process modeling.
Tamed by our underlying strict service-oriented notion of abstraction, this
approach is tailored also to be used by application experts with little
technical knowledge: users can select, modify, construct and then pass
(component) processes during process execution as if they were data. We
illustrate the impact and essence of our framework along a concrete, realistic
(business) process modeling scenario: the development of Springer's
browser-based Online Conference Service (OCS). The most advanced feature of our
new framework allows one to combine online synthesis with the integration of
the synthesized process into the running application. This ability leads to a
particularly flexible way of implementing self-adaption, and to a particularly
concise and powerful way of achieving variability not only at design time, but
also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation
Background. 
The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.

Description. 
SADI – Semantic Automated Discovery and Integration – is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services “stack”, SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers.

Conclusions.
SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behavior we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies
Fuzzy bilateral matchmaking in e-marketplaces
We present a novel Fuzzy Description Logic (DL) based approach to
automate matchmaking in e-marketplaces. We model tradersâ preferences with
the aid of Fuzzy DLs and, given a request, use utility values computed w.r.t.
Pareto agreements to rank a set of offers. In particular, we introduce an expressive
Fuzzy DL, extended with concrete domains in order to handle numerical, as well
as non numerical features, and to deal with vagueness in buyer/seller preferences.
Hence, agents can express preferences as e.g., I am searching for a passenger car
costing about 22000e yet if the car has a GPS system and more than two-year
warranty I can spend up to 25000e. Noteworthy our matchmaking approach,
among all the possible matches, chooses the mutually beneficial ones
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Usages of Semantic Web Services Technologies in IoT Ecosystems and its Impact in Services Delivery: A survey
Internet of things (IoT) has begun to emerge in our daily life through the huge number of smart services provided by the devices that deploy around us. Vague and uncertainty in attributes that using in describing services, different levels of quality of each service and the limitation in capabilities of IoT devices are affect and hinder the process of discovering or selecting services. The services in IoT need to be well described to enable users to receive their services that relevant to their query. This survey will investigate the most popular semantic services models and explore the use of these models in enhancing services discovery and services selection in IoT domain. Furthermore, the survey will investigate the evaluation metrics used by each study and compare the results that they obtained. 
Thinking outside the TBox multiparty service matchmaking as information retrieval
Service oriented computing is crucial to a large and growing number of computational
undertakings. Central to its approach are the open and network-accessible services
provided by many different organisations, and which in turn enable the easy creation
of composite workflows. This leads to an environment containing many thousands of
services, in which a programmer or automated composition system must discover and
select services appropriate for the task at hand. This discovery and selection process is
known as matchmaking.
Prior work in the field has conceived the problem as one of sufficiently describing
individual services using formal, symbolic knowledge representation languages. We
review the prior work, and present arguments for why it is optimistic to assume that
this approach will be adequate by itself. With these issues in mind, we examine
how, by reformulating the task and giving the matchmaker a record of prior service
performance, we can alleviate some of the problems. Using two formalismsâthe
incidence calculus and the lightweight coordination calculusâalong with algorithms
inspired by information retrieval techniques, we evolve a series of simple matchmaking
agents that learn from experience how to select those services which performed well in
the past, while making minimal demands on the service users. We extend this mechanism
to the overlooked case of matchmaking in workflows using multiple services, selecting
groups of services known to inter-operate well. We examine the performance of such
matchmakers in possible future services environments, and discuss issues in applying
such techniques in large-scale deployments
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Automatic message annotation and semantic interface for context aware mobile computing
This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the deviceâs file system and the message header information which is then accumulated with the messageâs tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved âContextual Ontology based Short Text Messages reasoning (SOIM)â. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.The Ministry of Higher Education and Scientific Research (IRAQ
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