Managing quality constraints in technology-managed learning content processes

Technology-enhanced learning content processes consist of individual activities related to the creation, composition, consumption and analysis of content facilitated through services. These service processes are often enacted across different boundaries such as organisations, countries or even languages. Specifically, looking at the quality of learning content and other artefacts and the governance of respective processed through services in this context is important to control quality requirements. We assume a partially automated workflow process for the content lifecycle. We suggest a rule-based constraints monitoring of learning content processes. A learning domain ontology shall capture the key data/content types, activities and constraints, which forms the basis of a rule-based policy monitoring solution that takes content provenance data into account

A configuration-based domain-specific rule generation framework for process model customization

In today’s changing world, there is an ever-increasing demand and need for software reuse in applications, where the process model needs to be reused in different applications in a domain-specific environment. The process model is required to adapt and implement changes promptly at run-time, in response of the end-user configuration requirements. Furthermore, reusability is emerging strongly as a necessary underlying capability, particularly for customization of business in a dynamic environment where end-users can select their requirements to achieve a specific goal. Such adaptations are in general, performed by non-technical end-users which can lead to losing a significant number of person-days and which can also open up possibilities to introduce errors into the system. These scenarios call for - indeed cry out for - a system with a configurable and customizable business process, operable by users with limited technical expertise. Research aims to provide a framework for generating the rule language and configuring domain constraints. This framework builds upon the core idea of Software Product Lines Engineering (SPLE) and Model-Driven Architecture (MDA). The SPLE provides a platform that includes the variability model. Variability models offer features where end-users can select features and customize possible changes in the domain template, which is the container for domain and process models. The user selects their requirements as a feature from feature models and generates rules from domain models using MDA. Then, the generated rules are translated from a high-level domain model, based on the requirements of the end-user. On the other hand, the weaving model is responsible for reflecting activation and de-activation of features of variabilities in the domain template. The usability of the proposed framework is evaluated with a user study in the area of Digital Content Technology. The results demonstrate that usability improvements can be achieved by using the proposed techniques. The framework can be used to support semi-automatic configuration that is efficient, effective and satisfactory

Business process model customisation using domain-driven controlled variability management and rule generation

Business process models are abstract descriptions and as such should be applicable in different situations. In order for a single process model to be reused, we need support for configuration and customisation. Often, process objects and activities are domain-specific. We use this observation and allow domain models to drive the customisation. Process variability models, known from product line modelling and manufacturing, can control this customisation by taking into account the domain models. While activities and objects have already been studied, we investigate here the constraints that govern a process execution. In order to integrate these constraints into a process model, we use a rule-based constraints language for a workflow and process model. A modelling framework will be presented as a development approach for customised rules through a feature model. Our use case is content processing, represented by an abstract ontology-based domain model in the framework and implemented by a customisation engine. The key contribution is a conceptual definition of a domain-specific rule variability language

Business process model customisation using domain-driven controlled variability management and rule generation

Business process models are abstract descriptions and as such should be applicable in different situations. In order for a single process model to be reused, we need support for configuration and customisation. Often, process objects and activities are domain-specific. We use this observation and allow domain models to drive the customisation. Process variability models, known from product line modelling and manufacturing, can control this customisation by taking into account the domain models. While activities and objects have already been studied, we investigate here the constraints that govern a process execution. In order to integrate these constraints into a process model, we use a rule-based constraints language for a workflow and process model. A modelling framework will be presented as a development approach for customised rules through a feature model. Our use case is content processing, represented by an abstract ontology-based domain model in the framework and implemented by a customisation engine. The key contribution is a conceptual definition of a domain-specific rule variability language

Big Data Guided Resources Businesses – Leveraging Location Analytics and Managing Geospatial-temporal Knowledge

Location data rapidly grow with fast-changing logistics and business rules. Due to fast-growing business ventures and their diverse operations locally and globally, location-based information systems are in demand in resource industries. Data sources in these industries are spatial-temporal, with petabytes in size. Managing volumes and various data in periodic and geographic dimensions using the existing modelling methods is challenging. The current relational database models have implementation challenges, including the interpretation of data views. Multidimensional models are articulated to integrate resource databases with spatial-temporal attribute dimensions. Location and periodic attribute dimensions are incorporated into various schemas to minimise ambiguity during database operations, ensuring resource data's uniqueness and monotonic characteristics. We develop an integrated framework compatible with the multidimensional repository and implement its metadata in resource industries. The resources’ metadata with spatial-temporal attributes enables business research analysts a scope for data views’ interpretation in new geospatial knowledge domains for financial decision support

Big Data guided Digital Petroleum Ecosystems for Visual Analytics and Knowledge Management

The North West Shelf (NWS) interpreted as a Total Petroleum System (TPS), is Super Westralian Basin with active onshore and offshore basins through which shelf, - slope and deep-oceanic geological events are construed. In addition to their data associativity, TPS emerges with geographic connectivity through phenomena of digital petroleum ecosystem. The super basin has a multitude of sub-basins, each basin is associated with several petroleum systems and each system comprised of multiple oil and gas fields with either known or unknown areal extents. Such hierarchical ontologies make connections between attribute relationships of diverse petroleum systems. Besides, NWS has a scope of storing volumes of instances in a data-warehousing environment to analyse and motivate to create new business opportunities. Furthermore, the big exploration data, characterized as heterogeneous and multidimensional, can complicate the data integration process, precluding interpretation of data views, drawn from TPS metadata in new knowledge domains. The research objective is to develop an integrated framework that can unify the exploration and other interrelated multidisciplinary data into a holistic TPS metadata for visualization and valued interpretation. Petroleum digital ecosystem is prototyped as a digital oil field solution, with multitude of big data tools. Big data associated with elements and processes of petroleum systems are examined using prototype solutions. With conceptual framework of Digital Petroleum Ecosystems and Technologies (DPEST), we manage the interconnectivity between diverse petroleum systems and their linked basins. The ontology-based data warehousing and mining articulations ascertain the collaboration through data artefacts, the coexistence between different petroleum systems and their linked oil and gas fields that benefit the explorers. The connectivity between systems further facilitates us with presentable exploration data views, improvising visualization and interpretation. The metadata with meta-knowledge in diverse knowledge domains of digital petroleum ecosystems ensures the quality of untapped reservoirs and their associativity between Westralian basins

On Developing Sustainable Digital Ecosystems and their Spatial-temporal Knowledge Management

The research aims to assess the sustainment of multiple ecosystems with viable and adaptable models. We propose an Information System (IS) modelling approach and examine the sustainment between ecosystems through connectable multidimensional IS artefacts. For example, humans survive in healthy and hassle-free environments for long-term economic benefits. We conceptualize human, healthcare, and environmental ecosystems are connectable, and the interconnectivity depends on how the ecologies are supportive together and with each other. The ecosystems emerge and grow with data heterogeneity challenges, which can disorganize ecological connectivity, impeding the implementation of resilient digital ecosystems. The development of multidimensional repositories is added motivation to explore connectivity, for which Attribute Journey Mapping and Modelling (AJMM) method is sought. Map views are computed to successfully interpret and establish connectivity, including coherency between attributes of multiple digital ecosystems. Besides, Big Data has changed the ecological research direction with which the coexistence between human-healthcare-environment ecosystems is assessed

Digital Web Ecosystem Development for Managing Social Network Data Science

The World Wide Web (WWW) unfolds with diverse domains and associated data sources, complicating the network data science. In addition, heterogeneity and multidimensionality can make data management, documentation, and even integration more challenging. The WWW emerges as a complex digital ecosystem on Big Data scale, and we conceptualize the web network as a Digital Web Ecosystem (DWE) in an analytical space. The purpose of the research is to develop a framework, explore the association between attributes of social networks and assess their strengths. We have experimented network users and usability attributes of social networks and tools, including misgivings. We construe new insights from data views of DWE metadata. For leveraging the usability and popularity-sentiment attribute relationships, we compute map views and several regressions between instances of technology and society dimensions, interpreting their strengths and weaknesses. Visual analytics adds values to the DWE meta-knowledge, establishing cognitive data usability in the WWW

Development of Multidimensional Eating Disorder Inventory Information System Framework - Managing Digital Adolescent Healthcare Ecosystem

Data sources associated with Eating Disorder (ED) events are heterogeneous. They intensely influence the lives of millions of teenagers. The EDs can lead to obesity or vice versa and succumb to many linked chronic illnesses. We examine the existing research on Eating Disorder Inventory (EDI) to explore connectivity between multiple domains of the healthcare ecosystem. The present study identifies various attribute dimensions of EDI – M , interpreted as multidimensional , an egghead idiom to model and integrate with an integrated conceptual framework. The research aims to develop a Multidimensional Eating Disorder Inventory Information System (MEDIIS) to manage the EDI-M attributes, interpreted in various data sources and domains. We further evaluate the EDI metadata to explore the connectivity between multiple attributes dimensions of EDs. The phenomenon of eating-disorder attribute connectivity is established with overweight, obesity and diabetic conditions, articulating EDI-M applicability in the MEDIIS framework

Information System Guided Supply Chains and their Visual Analytics in Integrated Project Management

From a digital ecosystem perspective, sustainability is a manifestation of a composite entity with multiple data attribute dimensions. The data relationships may emerge between geographically distributed supply chain management ecosystems and their linked human, economic and environment ecologies. The ecosystems may exhibit inherent connections and interactions. For making connections more resilient, we characterize models that serve multiple industries through numerous data associations, even in Big Data scales. In the context of Integrated Project Management (IPM), the knowledge of boundaries between systems is mysterious, analysing diverse ecosystems through a sustainable framework can uncover new insights of inherent connections. The purpose of this research is to develop a holistic information system approach, in which multidimensional data and their connectivity are analysed, recognizing the ontological cogency, uniqueness of ecosystems and their data sources. The research outcome has facilitated the tactical development of strategies for ameliorating the sustainability challenges in the IPM contexts