Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System

Resource Discovery (RD) is a key issue in Internet-based distributed sytems such as grid. RD is about locating an appropriate resource/service type that matches the user's application requirements. This is very important, as resource reservation and task scheduling are based on it. Unfortunately, RD in grid is very challenging as resources and users are distributed, resources are heterogeneous in their platforms, status of the resources is dynamic (resources can join or leave the system without any prior notice) and most recently the introduction of a new type of grid called intergrid (grid of grids) with the use of multi middlewares. Such situation requires an RD system that has rich interoperability, scalability, decentralization and dynamism features. However, existing grid RD systems have difficulties to attain these features. Not only that, they lack the review and evaluation studies, which may highlight the gap in achieving the required features. Therefore, this work discusses the problem associated with intergrid RD from two perspectives. First, reviewing and classifying the current grid RD systems in such a way that may be useful for discussing and comparing them. Second, propose a novel RD framework that has the aforementioned required RD features. In the former, we mainly focus on the studies that aim to achieve interoperability in the first place, which are known as RD systems that use semantic information (semantic technology). In particular, we classify such systems based on their qualitative use of the semantic information. We evaluate the classified studies based on their degree of accomplishment of interoperability and the other RD requirements, and draw the future research direction of this field. Meanwhile in the latter, we name the new framework as semantic-based scalable decentralized dynamic RD. The framework further contains two main components which are service description, and service registration and discovery models. The earlier consists of a set of ontologies and services. Ontologies are used as a data model for service description, whereas the services are to accomplish the description process. The service registration is also based on ontology, where nodes of the service (service providers) are classified to some classes according to the ontology concepts, which means each class represents a concept in the ontology. Each class has a head, which is elected among its own class I nodes/members. Head plays the role of a registry in its class and communicates with I the other heads of the classes in a peer to peer manner during the discovery process. We further introduce two intelligent agents to automate the discovery process which are Request Agent (RA) and Description Agent (DA). Eaclj. node is supposed to have both agents. DA describes the service capabilities based on the ontology, and RA I carries the service requests based on the ontology as well. We design a service search I algorithm for the RA that starts the service look up from the class of request origin first, then to the other classes. We finally evaluate the performance of our framework ~ith extensive simulation experiments, the result of which confirms the effectiveness of the proposed system in satisfying the required RD features (interoperability, scalability, decentralization and dynamism). In short, our main contributions are outlined new key taxonomy for the semantic-based grid RD studies; an interoperable semantic description RD component model for intergrid services metadata representation; a semantic distributed registry architecture for indexing service metadata; and an agent-qased service search and selection algorithm. Vll

On distributed data processing in data grid architecture for a virtual repository

The article describes the problem of integration of distributed, heterogeneous and fragmented collections of data with application of the virtual repository and the data grid concept. The technology involves: wrappers enveloping external resources, a virtual network (based on the peer-topeer technology) responsible for integration of data into one global schema and a distributed index for speeding-up data retrieval. Authors present a method for obtaining data from heterogeneously structured external databases and then a procedure of integration the data to one, commonly available, global schema. The core of the described solution is based on the Stack-Based Query Language (SBQL) and virtual updatable SBQL views. The system transport and indexing layer is based on the P2P architecture

A customized semantic service retrieval methodology for the digital ecosystems environment

With the emergence of the Web and its pervasive intrusion on individuals, organizations, businesses etc., people now realize that they are living in a digital environment analogous to the ecological ecosystem. Consequently, no individual or organization can ignore the huge impact of the Web on social well-being, growth and prosperity, or the changes that it has brought about to the world economy, transforming it from a self-contained, isolated, and static environment to an open, connected, dynamic environment. Recently, the European Union initiated a research vision in relation to this ubiquitous digital environment, known as Digital (Business) Ecosystems. In the Digital Ecosystems environment, there exist ubiquitous and heterogeneous species, and ubiquitous, heterogeneous, context-dependent and dynamic services provided or requested by species. Nevertheless, existing commercial search engines lack sufficient semantic supports, which cannot be employed to disambiguate user queries and cannot provide trustworthy and reliable service retrieval. Furthermore, current semantic service retrieval research focuses on service retrieval in the Web service field, which cannot provide requested service retrieval functions that take into account the features of Digital Ecosystem services. Hence, in this thesis, we propose a customized semantic service retrieval methodology, enabling trustworthy and reliable service retrieval in the Digital Ecosystems environment, by considering the heterogeneous, context-dependent and dynamic nature of services and the heterogeneous and dynamic nature of service providers and service requesters in Digital Ecosystems.The customized semantic service retrieval methodology comprises: 1) a service information discovery, annotation and classification methodology; 2) a service retrieval methodology; 3) a service concept recommendation methodology; 4) a quality of service (QoS) evaluation and service ranking methodology; and 5) a service domain knowledge updating, and service-provider-based Service Description Entity (SDE) metadata publishing, maintenance and classification methodology.The service information discovery, annotation and classification methodology is designed for discovering ubiquitous service information from the Web, annotating the discovered service information with ontology mark-up languages, and classifying the annotated service information by means of specific service domain knowledge, taking into account the heterogeneous and context-dependent nature of Digital Ecosystem services and the heterogeneous nature of service providers. The methodology is realized by the prototype of a Semantic Crawler, the aim of which is to discover service advertisements and service provider profiles from webpages, and annotating the information with service domain ontologies.The service retrieval methodology enables service requesters to precisely retrieve the annotated service information, taking into account the heterogeneous nature of Digital Ecosystem service requesters. The methodology is presented by the prototype of a Service Search Engine. Since service requesters can be divided according to the group which has relevant knowledge with regard to their service requests, and the group which does not have relevant knowledge with regard to their service requests, we respectively provide two different service retrieval modules. The module for the first group enables service requesters to directly retrieve service information by querying its attributes. The module for the second group enables service requesters to interact with the search engine to denote their queries by means of service domain knowledge, and then retrieve service information based on the denoted queries.The service concept recommendation methodology concerns the issue of incomplete or incorrect queries. The methodology enables the search engine to recommend relevant concepts to service requesters, once they find that the service concepts eventually selected cannot be used to denote their service requests. We premise that there is some extent of overlap between the selected concepts and the concepts denoting service requests, as a result of the impact of service requesters’ understandings of service requests on the selected concepts by a series of human-computer interactions. Therefore, a semantic similarity model is designed that seeks semantically similar concepts based on selected concepts.The QoS evaluation and service ranking methodology is proposed to allow service requesters to evaluate the trustworthiness of a service advertisement and rank retrieved service advertisements based on their QoS values, taking into account the contextdependent nature of services in Digital Ecosystems. The core of this methodology is an extended CCCI (Correlation of Interaction, Correlation of Criterion, Clarity of Criterion, and Importance of Criterion) metrics, which allows a service requester to evaluate the performance of a service provider in a service transaction based on QoS evaluation criteria in a specific service domain. The evaluation result is then incorporated with the previous results to produce the eventual QoS value of the service advertisement in a service domain. Service requesters can rank service advertisements by considering their QoS values under each criterion in a service domain.The methodology for service domain knowledge updating, service-provider-based SDE metadata publishing, maintenance, and classification is initiated to allow: 1) knowledge users to update service domain ontologies employed in the service retrieval methodology, taking into account the dynamic nature of services in Digital Ecosystems; and 2) service providers to update their service profiles and manually annotate their published service advertisements by means of service domain knowledge, taking into account the dynamic nature of service providers in Digital Ecosystems. The methodology for service domain knowledge updating is realized by a voting system for any proposals for changes in service domain knowledge, and by assigning different weights to the votes of domain experts and normal users.In order to validate the customized semantic service retrieval methodology, we build a prototype – a Customized Semantic Service Search Engine. Based on the prototype, we test the mathematical algorithms involved in the methodology by a simulation approach and validate the proposed functions of the methodology by a functional testing approach

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges