Community Detection (CD) in weighted social networks is a highly active research field, celebrated for its profound practical implications across a multitude of disciplines. Genetic algorithms (GAs) are frequently explored to tackle CD problems, leveraging their capability to navigate the extensive discrete search space effectively. Throughout the evolutionary process, genetic operators such as crossover and mutation assume pivotal roles in effectively exploring the vast solution space. Nonetheless, prevailing GA-based approaches often ignore crucial topology information, particularly information regarding edge weights, resulting in compromised algorithm performance. In light of this, this paper introduces Edge Information-based GA (EIGA) to effectively solve CD problems in weighted networks. This is achieved specifically through the innovative designs of edgeweight-aware crossover and mutation operators. These novel edge-weight-aware operators improve the extraction of meaningful community structures, advancing knowledge discovery from social networks. Empirical findings demonstrate the superior performance of EIGA over numerous state-of-the-art algorithms across various real-world and synthetic benchmark networks.

Genetic Programming Hyper-heuristics (GPHH) have been successfully used to evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other challenging combinatorial optimization problems. The method of sampling training instances has a significant impact on the generalization ability of GPHH, yet they are rarely addressed in existing research. This article aims to fill this gap by proposing a GPHH algorithm with a sampling strategy to thoroughly investigate the impact of six instance sampling strategies on algorithmic generalization, including one rotation strategy, three mini-batch strategies, and two hybrid strategies. Experiments across four scenarios with varying settings reveal that: (1) mini-batch with random sampling can outperform rotation in generalizing to unseen workflow scheduling problems under the same computational cost; (2) employing a hybrid strategy that combines rotation and mini-batch further enhances the generalization ability of GPHH; and (3) mini-batch and hybrid strategies can effectively enable heuristics trained on small-scale training instances generalizing well to large-scale unseen ones. These findings highlight the potential of mini-batch strategies in GPHH, offering improved generalization performance while maintaining diversity and suggesting promising avenues for further exploration in GPHH domains.

The geometrically enhanced ER model (GERM) addresses conceptual geometric modelling on two levels. On the surface level GERM is an extended ER model, in which attributes are associated with types with sets of values as domains. On the internal level some of the types, the geometric types, are further associated with geometric domains, which define point sets by means of algebraic curves. For query handling the operations on geometric types give rise to operations on point sets, which can be realized by a small set of operations on algebraic curves. The core problem is then to obtain a surface representation as the result. We show that symbolic computation is essential for solving this problem.

The emergence of service-oriented computing and cloud computing advocate the development of new software applications on the basis of existing services. To make effectively and efficiently use of existing services, it is essential to describe services so that developers can easily discover and correctly exploit them for their own software development endeavours. In this paper, we present a framework in which existing services available through a cloud are modelled by Abstract State Services. For specification, we use TBox and ABox statements bound to XML Schema and XML. Given such a representation, XQuery can be used for service discovery.

The current transition from Web 1.0 to Web 2.0 is not so much a change of the world wide web itself, but a change of usage. While Web 1.0 is mainly provider-driven, Web 2.0 is user-driven and content-centred. This also implies a fast change of content turning the web from a rather static medium into an active communication medium. Furthermore, the web is subject to even more changes emphasising content, commerce, community and context, personalization, and vertical search, which is branded as the next generation Web 3.0.In this situation the design of Web Information Systems (WISs) requires adaptations to the new developments, if not completely new methods. The tutorial wants to show that the co-design method for WISs did already support many of the features required in Web 2.0 applications. This is particularly stressed by the focus of user contributions to a community or service and the incorporation of collaboration. It will further outline, how the method has to be extended to be fully functional as a Web 2.0 applications development method.The tutorial comprises three parts, the first one dealing with the transition from Web 1.0 to Web 2.0 and beyond, the second one dealing with the co-design methodology with particular emphasis on service orientation, personalisation and contribution to communities, and the final one devoted to the composition of services and collaboration.Part I will give a brief description of three stages that websites have gone through and still will go through: author-driven Web 1.0, user-driven and content-centred Web 2.0, and Web 3.0, which is characterised by (4C+P+VS), where 4C means Content, Commerce, Community and Context, P means Personalization, and VS denotes Vertical Search. In particular, Enterprise 2.0, cloud computing, mashups and Web-Oriented architecture are discussed in detail in order to show the problems in WIS development and the need for WIS development methods. Part II presents the major blocks of our WIS development methodology dealing with strategic modelling of WISs, usage modelling of WISs by means of storyboarding, conceptual modelling of WISs by means of media types, and semantics and pragmatics of storyboarding. Particular attention is paid to services and contributions to communities. Part III focuses on service composition and collaboration showing how the WIS co-design techniques presented in Part II can be adopted to solutions to various Web 2.0 applications including E-Business, Enterprise 2.0, Mashups, thus picking up on the challenges that were highlighted in part I.

Data warehouses integrate and aggregate data from various sources to support decision making within an enterprise. Usually, it is assumed that data are extracted from operational databases used by the enterprise. Web warehousing relaxes this view permitting data sources to be located anywhere on the world-wide web. In this paper we even go one step further and propose that web warehousing should exploit data-intensive web services. For this we adopt the view of the recently introduced model of abstract state services (ASSs). An ASS combines a hidden database layer with an operation-equipped view layer, and can be anything from a simple function to a full-fledged Web Information System, thus provides an abstraction of web services that can be made available for use by other systems, e.g. via the web. We demonstrate how data warehouses and OLAP systems can be considered in a natural way as ASSs, i.e. selected data and functionality of a data warehouse may be made available in the form of a generalised web service, and consequently ASS composition can be used for web warehousing. In order to become workable the model of ASSs has to be extended in a way that allows services to be searched for. For this we propose federations of services together with an ontology that specifies their semantics.