885 research outputs found
Semantic discovery and reuse of business process patterns
Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse
Propelling the Potential of Enterprise Linked Data in Austria. Roadmap and Report
In times of digital transformation and considering the potential of the data-driven
economy, it is crucial that data is not only made available, data sources can be trusted,
but also data integrity can be guaranteed, necessary privacy and security mechanisms
are in place, and data and access comply with policies and legislation. In many cases,
complex and interdisciplinary questions cannot be answered by a single dataset and
thus it is necessary to combine data from multiple disparate sources. However, because
most data today is locked up in isolated silos, data cannot be used to its fullest
potential.
The core challenge for most organisations and enterprises in regards to data exchange
and integration is to be able to combine data from internal and external data sources
in a manner that supports both day to day operations and innovation. Linked Data is a
promising data publishing and integration paradigm that builds upon standard web
technologies. It supports the publishing of structured data in a semantically explicit
and interlinked manner such that it can be easily connected, and consequently becomes
more interoperable and useful.
The PROPEL project - Propelling the Potential of Enterprise Linked Data in Austria - surveyed technological challenges, entrepreneurial opportunities, and open research
questions on the use of Linked Data in a business context and developed a roadmap and a set of recommendations for policy makers, industry, and the research community.
Shifting away from a predominantly academic perspective and an exclusive focus on open data, the project looked at Linked Data as an emerging disruptive technology
that enables efficient enterprise data management in the rising data economy. Current market forces provide many opportunities, but also present several data and
information management challenges. Given that Linked Data enables advanced analytics and decision-making, it is particularly suitable for addressing today's data and
information management challenges. In our research, we identified a variety of highly promising use cases for Linked Data in an enterprise context. Examples of promising
application domains include "customization and customer relationship management", "automatic and dynamic content production, adaption and display", "data search, information
retrieval and knowledge discovery", as well as "data and information exchange and integration". The analysis also revealed broad potential across a large spectrum of
industries whose structural and technological characteristics align well with Linked
Data characteristics and principles: energy, retail, finance and insurance, government, health, transport and logistics, telecommunications, media, tourism, engineering, and research and development rank among the most promising industries for the adoption of Linked Data principles.
In addition to approaching the subject from an industry perspective, we also examined the topics and trends emerging from the research community in the field of Linked Data and the Semantic Web. Although our analysis revolved around a vibrant and active community composed of academia and leading companies involved in semantic technologies, we found that industry needs and research discussions are
somewhat misaligned. Whereas some foundation technologies such as knowledge representation and data creation/publishing/sharing, data management and system
engineering are highly represented in scientific papers, specific topics such as recommendations, or cross-topics such as machine learning or privacy and security are marginally
present. Topics such as big/large data and the internet of things are (still) on an
upward trajectory in terms of attention. In contrast, topics that are very relevant for
industry such as application oriented topics or those that relate to security, privacy
and robustness are not attracting much attention. When it comes to standardisation
efforts, we identified a clear need for a more in-depth analysis into the effectiveness of
existing standards, the degree of coverage they provide with respect the foundations
they belong to, and the suitability of alternative standards that do not fall under the
core Semantic Web umbrella.
Taking into consideration market forces, sector analysis of Linked Data potential, demand
side analysis and the current technological status it is clear that Linked Data
has a lot of potential for enterprises and can act as a key driver of technological, organizational,
and economic change. However, in order to ensure a solid foundation
for Enterprise Linked Data include there is a need for: greater awareness surrounding
the potential of Linked Data in enterprises, lowering of entrance barriers via education
and training, better alignment between industry demands and research activities,
greater support for technology transfer from universities to companies.
The PROPEL roadmap recommends concrete measures in order to propel the adoption
of Linked Data in Austrian enterprises. These measures are structured around five
fields of activities: "awareness and education", "technological innovation, research gaps,
standardisation", "policy and legal", and "funding". Key short-term recommendations include the clustering of existing activities in order to raise visibility on an international level, the funding of key topics that are under represented by the community, and the setup of joint projects. In the medium term, we recommend the strengthening
of existing academic and private education efforts via certification and to establish flagship projects that are based on national use cases that can serve as blueprints for transnational initiatives. This requires not only financial support, but also infrastructure support, such as data and services to build solutions on top. In the long term, we
recommend cooperation with international funding schemes to establish and foster a European level agenda, and the setup of centres of excellence
Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project
Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic
Professor Text: University Fundraising Optimization
University fundraising campaigns are a unique type of cause-related marketing with its own challenges and opportunities. Campaigns like this typically last an extended period, such as five or more years, and goals exist beyond the dollar amount raised. These supplemental goals, such as awareness among potential future donators or brand reputation within the local community, are important to consider and strategize. There can also be unique limitations, such as requiring advertising specifically on recent large gifts or endowment programs. This research explores how machine learning techniques such as natural language processing can be used to optimize a fundraising campaign strategy, execution, and overall performance
Requirements and Use Cases ; Report I on the sub-project Smart Content Enrichment
In this technical report, we present the results of the first milestone phase
of the Corporate Smart Content sub-project "Smart Content Enrichment". We
present analyses of the state of the art in the fields concerning the three
working packages defined in the sub-project, which are aspect-oriented
ontology development, complex entity recognition, and semantic event pattern
mining. We compare the research approaches related to our three research
subjects and outline briefly our future work plan
Implementing ERPII in customer facing organisations, an investigation of critical success factors
There has been a growing trend for customer facing organisations (CFOs) to turn to highly demanding information systems such as enterprise resource planning (ERP) in order to improve their interaction with customers. ERPII has the specific capabilities to deliver extended enterprise opportunities; however there have been widespread accounts of implementation failure leading to costly delays and even on occasion, bankruptcy.
There is a lack of research available to business practitioners in terms of how to deliver a successful implementation in these situations and this research aims to address this issue. To achieve this, research has been undertaken using critical success factor (CSF) analysis.
A case study was undertaken comprising of a project team placement within an ERPII implementation environment and follow-up interviews with the project team members were undertaken. In addition, a third piece of empirical research was undertaken consisting of interviews with consultant practitioners of supplier organisations.
This research shows that CFOs implementing ERPII require specific CSFs to be addressed at different points within the implementation lifecycle. ‘Critical pathway steps’ have been recommended which emphasise the importance of post implementation training
Spatial ontologies for architectural heritage
Informatics and artificial intelligence have generated new requirements for digital archiving, information, and documentation. Semantic interoperability has become fundamental for the management and sharing of information. The constraints to data interpretation enable both database interoperability, for data and schemas sharing and reuse, and information retrieval in large datasets. Another challenging issue is the exploitation of automated reasoning possibilities. The solution is the use of domain ontologies as a reference for data modelling in information systems. The architectural heritage (AH) domain is considered in this thesis. The documentation in this field, particularly complex and multifaceted, is well-known to be critical for the preservation, knowledge, and promotion of the monuments. For these reasons, digital inventories, also exploiting standards and new semantic technologies, are developed by international organisations (Getty Institute, ONU, European Union). Geometric and geographic information is essential part of a monument. It is composed by a number of aspects (spatial, topological, and mereological relations; accuracy; multi-scale representation; time; etc.). Currently, geomatics permits the obtaining of very accurate and dense 3D models (possibly enriched with textures) and derived products, in both raster and vector format. Many standards were published for the geographic field or in the cultural heritage domain. However, the first ones are limited in the foreseen representation scales (the maximum is achieved by OGC CityGML), and the semantic values do not consider the full semantic richness of AH. The second ones (especially the core ontology CIDOC – CRM, the Conceptual Reference Model of the Documentation Commettee of the International Council of Museums) were employed to document museums’ objects. Even if it was recently extended to standing buildings and a spatial extension was included, the integration of complex 3D models has not yet been achieved. In this thesis, the aspects (especially spatial issues) to consider in the documentation of monuments are analysed. In the light of them, the OGC CityGML is extended for the management of AH complexity. An approach ‘from the landscape to the detail’ is used, for considering the monument in a wider system, which is essential for analysis and reasoning about such complex objects. An implementation test is conducted on a case study, preferring open source applications
DRIVER Technology Watch Report
This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field
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