Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Adaptive Workflow Composition in Service-based Systems (Aanpasbare workflowcompositie in service-gebaseerde systemen)

The paradigm of service-oriented computing (SOC) has received a lot of attention over the last years and has changed the way software systems are designed. The promise of SOC is to provide flexibility and agility on the level of systems development. At the heart of service-based systems are services that provide platform- and technology-independent, computational elements that can be composed into loosely coupled networks of collaborating applications in order to reflect an organisation's business-level objectives. A popular approach for defining such business flows as a cooperation of services is workflow (or service) composition. The shift towards SOC has also impacted the delivery and consumption of software systems. Recently, cloud computing has become an emerging mechanism that fits into this evolution. The combination of fast expanding technologies with increasing competitiveness drives more and more companies to the outsourcing of parts of their business IT operations to third-party cloud providers. This enables them to take advantage of the cloud provider's expertise and to reduce their cost by sharing resources to exploit economies of scale. In the cloud, service-based systems can be deployed as Software-as-a-Service (SaaS) applications that can be purchased and consumed remotely over the internet.Due to increasing user demands and evolving requirements these service-based systems should be able to adapt to both dynamic changes in operational requirements and environmental conditions, while providing predictable behavior with respect to service qualities such as response time, availability, scalability, and security. State-of-the-art industry standards in workflow composition, however, exhibit major limitations regarding modularity and flexibility to support complex and highly dynamic service compositions. Hence, in this context three interesting research challenges appear that are addressed in this dissertation. First, traditional service composition solutions should be able to enforce policy (or rule) specifications that describe how the workflow composition must adapt to changing requirements and circumstances. Second, an important aspect of adaptive service composition is that it requires the capability to dynamically change the levels of quality of service (QoS) to satisfy customers demand by selecting the appropriate participating services. Third, with the trend of cloud computing, cloud providers want to make full use of economies of scale by hosting their services following a multi-tenant model, where a single application is used to serve multiple customers (called tenants). In such a multi-tenant setting, the cloud provider should be able to tailor its business processes to meet the functional and non-functional requirements of each tenant.This dissertation delivers four core contributions. First, we present a realistic application that has been developed using state-of-the-art technologies in service-oriented computing. This application is used as a practical case-study to expose some key limitations in current industry standards and technologies that motivate our remaining contributions. Second, we propose a portable framework for the enforcement of dynamic adaptation policies in business processes. The framework is inspired by the model-view-controller (MVC) pattern, commonly used for adding dynamism to web pages. Third, we present a theoretical approach to effectively deal with the composition of services that require certain levels of quality. As actual QoS support of participating services changes over time, the service composition problem must be treated as a decision problem under uncertainty. Therefore, we have developed an algorithm for predicting whether the QoS of a service composition execution will be compliant with a service level agreement (SLA) between a customer and the service (composition) provider. Fourth, we capitalize on the shift from supply to demand driven processes by proposing a middleware that provides the mechanisms to perform requirement-driven adaptation of shared process templates in a multi-tenant SaaS environment.We validate our work in several respects. To demonstrate the feasibility of our work, the proposed ideas have been prototyped. To measure the expressiveness of our adaptive workflow approach, we analyze its customization support by means of an extensive classification of change patterns and change support features. To analyze the performance of our prediction algorithm, used for selecting appropriate participating services in a composition, we introduce two performance indicators for comparing different QoS prediction algorithms. Our validation on both real as well as simulated QoS data shows that the proposed algorithms outperform existing approaches. To evaluate the effectiveness of tenant-specific customization in the context of Software as a Service in practice, we apply our approach on a case-study in the healthcare domain. Our measures confirm that the middleware improves the QoS of a composition with an acceptable performance overhead.status: publishe

Dynamic Reconfiguration Using Template Based Web Service Composition

Current workflow languages introduce limitations regarding modularity and flexibility. They are lacking support for reusability of primitive and structured activities. Designing processes often leads to duplication which makes the workflow descriptor complicated and unnecessarily large. Furthermore, due to the static character of the scripts, there is insufficient flexibility to model dynamic, evolvable and failsafe workflows. In this paper we present a framework that allows the design of WS-BPEL processes in a modular way based on reusable templates. In addition, we introduce an extra layer on top of WS-BPEL that allows template processing based on parameter values. This layer offers support for decision logic to adapt processes dynamically. The approach is based on the ”Ruby On Rails ” (RoR) framework, known for adding dynamism to static web pages. The proposed solution is portable with existing WS-BPEL engines

A MVC framework for policy-based adaptation of workflow processes: A case study on confidentiality

Most work on adaptive workflows offers insufficient flexibility to enforce complex policies regarding dynamic, evolvable and robust workflows. In addition, many proposed approaches require customized workflow engines. This paper presents a portable framework for realistic enforcement of dynamic adaptation policies in business processes. The framework is based on the Model-View-Controller (MVC) pattern, commonly used for adding dynamism to web pages. To enhance reusability, our approach supports separation of adaptation logic from the functional workflow and modularization of workflow tasks in reusable aspects. The main idea is to design a workflow process as a template, where tasks can be specified on an abstract level. Concrete implementations of the tasks, modeled as aspects, are then selected from a library according to a policy-based adaptation logic. This logic is implemented using a general purpose language that offers an extensible and flexible solution to enforce any type of policy. We evaluate by means of a case study on workflow confidentiality to what extent an approach using standards-based technologies allows application-specific adaptation of running workflow instances.status: publishe

Unifying and targeting cultural activities via events modelling and profiling

Today, people have a lot of spare time at their hands which they want to fill in according to their interests, whereas cultural temples are trying to attract interested communities to their carefully planned cultural programs. These cultural activities can be characterised as dynamic and distributed events in addition to which it is important to aggregate, enrich, recommend, and distribute these event items as targeted as possible. In this paper, we show how personalised recommendation and distribution of events, described using an RDF/OWL representation of the EventsML-G2 standard, can be enabled by automatically categorising and enriching events metadata via smart indexing and open linked datasets available on the web of data. As such, the ultimate goal of the CUPID-project is to provide an open, userfriendly platform that harnesses the end-user with a tool to access useful event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national cultural community with standardised mechanisms to describe/distribute event and profile information.status: publishe

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.status: publishe

Unifying and targeting cultural activities via events modelling and profiling

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.status: publishe

Unifying and targeting cultural activities via events modelling and profiling

Today, people have only limited, valuable spare time at their hands which they want to fill in as good as possible according to their interests. At the same time, cultural institutions are trying to attract interested communities to their carefully planned cultural programs. To distribute these cultural events to the right people, we developed a framework that will aggregate, enrich, recommend and distribute these events as targeted as possible. The aggregated events are published as Linked Open Data using an RDF/OWL representation of the EventsML-G2 standard. These event items are categorised and enriched via smart indexing and linked open datasets available on the Web of data. For recommending the events to the end-user, a global profile of the end-user is automatically constructed by aggregating his profile information from all user communities the user trusts and is registered to. This way, the recommendations take profile information into account from different communities, which has a detrimental effect on the recommendations. As such, the ultimate goal is to provide an open, user-friendly recommendation platform that harnesses the end-user with a tool to access useful event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national cultural community with standardised mechanisms to describe/distribute event and profile information