2,192 research outputs found
The Role of Ontologies for Designing Accounting Information Systems
The accounting ontologies were conceptualized as a framework for building accounting information systems in a shared data environment, within enterprises or between different enterprises. The modelâs base feature was an object pattern consisting of two mirror-image that represented conceptual the input and output components of a business process. The REA acronym derives from that patternâs structure, which consisted of economic resources, economic events, and economic agents. The REA model was proposed as a means for an organization to capture the signification of economic exchanges between two business partners. The REA ontology provides an alternative for modelling an enterpriseâs economic resources, economic events, economic agents, and their relationships. Resources are considerate organization assets that are able to generate revenue for implicated parties. Events provide a source of detailed data in this approach. Agents participate in events and can affect some resources. They can be an individual or organization inside or outside the organization that is capable of controlling economic resources and interacting with other agents. The objective of this work is to offer an understandable of this framework and to explain how this model can help us via the identification of the afferent concepts.REA ontology, accounting information systems, business process, economic exchange
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Semantic discovery and reuse of business process patterns
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In modern organisations business process modelling has become fundamental due to the
increasing rate of organisational change. As a consequence, an organisation needs to
continuously redesign its business processes on a regular basis. One major problem
associated with the way business process modelling (BPM) is carried out today is the
lack of explicit and systematic reuse of previously developed models. Enabling the reuse of previously modelled behaviour can have a beneficial impact on the quality and
efficiency of the overall information systems development process and also improve the effectiveness of an organisationâs business processes. In related disciplines, like software engineering, patterns have emerged as a widely accepted architectural mechanism for reusing solutions. In business process modelling the use of patterns is quite limited apart from few sporadic attempts proposed by the literature. Thus, pattern-based BPM is not commonplace. Business process patterns should ideally be discovered from the empirical analysis of organisational processes. Empiricism is currently not the basis for the discovery of patterns for business process modelling and no systematic methodology for collecting and analysing process models of business organisations currently exists.
The purpose of the presented research project is to develop a methodological framework for achieving reuse in BPM via the discovery and adoption of patterns. The framework is called Semantic Discovery and Reuse of Business Process Patterns (SDR). SDR
provides a systematic method for identifying patterns among organisational data assets
representing business behaviour. The framework adopts ontologies (i.e., formalised
conceptual models of real-world domains) in order to facilitate such discovery. The
research has also produced an ontology of business processes that provides the
underlying semantic definitions of processes and their constituent parts. The use of
ontologies to model business processes represents a novel approach and combines
advances achieved by the Semantic Web and BPM communities. The methodological
framework also relates to a new line of research in BPM on declarative business
processes in which the models specify what should be done rather than how to
âprescriptivelyâ do it. The research follows a design science method for designing and
evaluating SDR. Evaluation is carried out using real world sources and reuse scenarios
taken from both the financial and educational domains
The Role of Ontologies for Designing Accounting Information Systems
The accounting ontologies were conceptualized as a framework for building accounting information systems in a shared data environment, within enterprises or between different enterprises. The modelâs base feature was an object pattern consisting of two mirror-image that represented conceptual the input and output components of a business process. The REA acronym derives from that patternâs structure, whichconsisted of economic resources, economic events, and economic agents. The REA model was proposed as a means for an organization to capture the signification of economic exchanges between two business partners. The REA ontology provides an alternative for modelling an enterpriseâs economic resources, economic events, economic agents, andtheir relationships. Resources are considerate organization assets that are able to generate revenue for implicated parties. Events provide a source of detailed data in this approach. Agents participate in events and can affect some resources. They can be anindividual or organization inside or outside the organization that is capable of controlling economic resources and interacting with other agents. The objective of this work is to offer an understandable of this framework and to explain how this model can help us via the identification of the afferent concepts
Towards Building a Knowledge Base of Monetary Transactions from a News Collection
We address the problem of extracting structured representations of economic
events from a large corpus of news articles, using a combination of natural
language processing and machine learning techniques. The developed techniques
allow for semi-automatic population of a financial knowledge base, which, in
turn, may be used to support a range of data mining and exploration tasks. The
key challenge we face in this domain is that the same event is often reported
multiple times, with varying correctness of details. We address this challenge
by first collecting all information pertinent to a given event from the entire
corpus, then considering all possible representations of the event, and
finally, using a supervised learning method, to rank these representations by
the associated confidence scores. A main innovative element of our approach is
that it jointly extracts and stores all attributes of the event as a single
representation (quintuple). Using a purpose-built test set we demonstrate that
our supervised learning approach can achieve 25% improvement in F1-score over
baseline methods that consider the earliest, the latest or the most frequent
reporting of the event.Comment: Proceedings of the 17th ACM/IEEE-CS Joint Conference on Digital
Libraries (JCDL '17), 201
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
Resources-Events-Agents Design Theory: A Revolutionary Approach to Enterprise System Design
Enterprise systems typically include constructs such as ledgers and journals with debit and credit entries as central pillars of the systemsâ architecture due in part to accountants and auditors who demand those constructs. At best, structuring systems with such constructs as base objects results in the storing the same data at multiple levels of aggregation, which creates inefficiencies in the database. At worst, basing systems on such constructs destroys details that are unnecessary for accounting but that may facilitate decision making by other enterprise functional areas. McCarthy (1982) proposed the resources-events-agents (REA) framework as an alternative structure for a shared data environment more than thirty years ago, and scholars have further developed it such that it is now a robust design theory. Despite this legacy, the broad IS community has not widely researched REA. In this paper, we discuss REAâs genesis and primary constructs, provide a history of REA research, discuss REAâs impact on practice, and speculate as to what the future may hold for REA-based enterprise systems. We invite IS researchers to consider integrating REA constructs with other theories and various emerging technologies to help advance the future of information systems and business research
Metaphysics of Internal Controls
A quality internal control system has been seen as a remedy for various corporate governance issues. Two pieces of legislation, the Foreign Corrupt Practices Act (FCPA) and the Sarbanes-Oxley Act (SOX) deal with very different corporate governance issues, but each argue for a similar remedy. Both the FCPA and the SOX legislation argue that improved (or proper) internal controls are necessary to root out bribery of foreign officials, in the case of the FCPA, and (in the case of SOX) to support the accurate preparation of financial statements. An issue that has yet to be resolved is that the quality of internal control systems is subject to subjective assessments of the internal control deficiencies and their impact. This paper presents a mathematical model of internal controls based on GÓ§del number of axioms. This results in the representation of quality internal controls in terms of an integer. This approach also allows for inferences about financial statements and various auditing judgements
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
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