TAKSONOMIJA METODA AKADEMSKOG PLAGIRANJA

The article gives an overview of the plagiarism domain, with focus on academic plagiarism. The article defines plagiarism, explains the origin of the term, as well as plagiarism related terms. It identifies the extent of the plagiarism domain and then focuses on the plagiarism subdomain of text documents, for which it gives an overview of current classifications and taxonomies and then proposes a more comprehensive classification according to several criteria: their origin and purpose, technical implementation, consequence, complexity of detection and according to the number of linguistic sources. The article suggests the new classification of academic plagiarism, describes sorts and methods of plagiarism, types and categories, approaches and phases of plagiarism detection, the classification of methods and algorithms for plagiarism detection. The title of the article explicitly targets the academic community, but it is sufficiently general and interdisciplinary, so it can be useful for many other professionals like software developers, linguists and librarians.Rad daje pregled domene plagiranja tekstnih dokumenata. Opisuje porijeklo pojma plagijata, daje prikaz definicija te objašnjava plagijatu srodne pojmove. Ukazuje na širinu domene plagiranja, a za tekstne dokumenate daje pregled dosadašnjih taksonomija i predlaže sveobuhvatniju taksonomiju prema više kriterija: porijeklu i namjeni, tehničkoj provedbi plagiranja, posljedicama plagiranja, složenosti otkrivanja i (više)jezičnom porijeklu. Rad predlaže novu klasifikaciju akademskog plagiranja, prikazuje vrste i metode plagiranja, tipove i kategorije plagijata, pristupe i faze otkrivanja plagiranja. Potom opisuje klasifikaciju metoda i algoritama otkrivanja plagijata. Iako cilja na akademskog čitatelja, može biti od koristi u interdisciplinarnim područjima te razvijateljima softvera, lingvistima i knjižničarima

Modelling a critical infrastructure-driven spatial database for proactive disaster management: A developing country context

The understanding and institutionalisation of the seamless link between urban critical infrastructure and disaster management has greatly helped the developed world to establish effective disaster management processes. However, this link is conspicuously missing in developing countries, where disaster management has been more reactive than proactive. The consequence of this is typified in poor response time and uncoordinated ways in which disasters and emergency situations are handled. As is the case with many Nigerian cities, the challenges of urban development in the city of Abeokuta have limited the effectiveness of disaster and emergency first responders and managers. Using geospatial techniques, the study attempted to design and deploy a spatial database running a web-based information system to track the characteristics and distribution of critical infrastructure for effective use during disaster and emergencies, with the purpose of proactively improving disaster and emergency management processes in Abeokuta. Keywords: Disaster Management; Emergency; Critical Infrastructure; Geospatial Database; Developing Countries; Nigeri

Reconciling Equational Heterogeneity within a Data Federation

Mappings in most federated databases are conceptualized and implemented as black-box transformations between source schemas and a federated schema. This approach does not allow specific mappings to be declared once and reused in other situations. We present an alternative approach, in which data-level mappings are represented independent of source and federated schemas as a network between “contexts”. This compendious representation expedites the data federation process via mapping reuse and automated mapping composition from simpler mappings. We illustrate the benefits of mapping reuse and composition by using an example that incorporates equational mappings and the application of symbolic equation solving techniques

Management of Multiply Represented Geographic Entities

Multiple representation of geographic information occurs when a real-world entity is represented more than once in the same or different databases. In this paper, we propose a new approach to the modeling of multiply represented entities and the relationships among the entities and their representations. A Multiple Representation Management System is outlined that can manage multiple representations consistently over a number of autonomous databases. Central to our approach is the Multiple Representation Schema Language that is used to configure the system. It provides an intuitive and declarative means of modeling multiple representations and specifying rules that are used to maintain consistency, match objects representing the same entity, and restore consistency if necessary

Algorithms for generation of path-methods in object-oriented databases

A path-method is a mechanism in object-oriented databases (OODBs) to retrieve or to update information relevant to one class that is not stored with that class but with some other class. A path-method is a method which traverses from one class through a chain of connections between classes to access information at another class. However, it is a difficult task for a user to write path-methods, because it might require comprehensive knowledge of many classes of the conceptual schema, while a typical user has often incomplete or even inconsistent knowledge of the schema. This dissertation proposes an approach to the generation of path-methods in an OODB to solve this problem. We have developed the Path-Method Generator (P MG) system, which generates path-methods according to a naive user\u27s requests. PMG is based on access weights which reflect the relative frequency of the connections and precomputed access relevance between every pair of classes of the OODB computed from access weights of the connections. We present specific rules for access weight assignment, efficient algorithms to compute access relevance in a single OODB, and a variety of traversal algorithms based on access weights and precomputed access relevance. Experiments with a university environment OODB and a sample of path-methods identify some of these algorithms as very successful in generating most of the desired path-methods. Thus, the PMG system is an efficient tool for aiding the user with the difficult task of querying and updating a large OODB. The path-method generation in an interoperable multi object-oriented database (IM-OODB) is even more difficult than for a single OODB, since a user has to be familiar with several OODBs. We use a hierarchical approach for deriving efficient online algorithms for the computation of access relevance in an IM-OODB, based on precomputed access relevance for each autonomous OODB. In an IM-OODB the access relevance is used as guide in generating path-methods between the classes of different OODBs

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Managing long-term access to digital data objects: a metadata approach

As society becomes increasingly reliant on information technology for data exchange and long-term data storage the need for a system of data management to document and provide access to the 'societal memory' is becoming imperative. An examination of both the literature and current 'best Practice' underlines the absence to date of a proven universal conceptual basis to digital data preservation. The examination of differences in nature and sources of origin, between traditional 'print-based' and digital objects leads to a re-appraisal of current practices of data selection and preservation. The need to embrace past, present and future metadata developments in a rapidly changing environment is considered. Various hypotheses were formulated and supported regarding; the similarities and differences required in selection criteria for different types of Digital Data Objects (DDOs), the ability to define universal threshold standards for a framework of metadata for digital data preservation, and the role of selection criteria in such a framework. The research uses Soft Systems Methodology to investigate the potential of the metadata concept as the key to universal data management. Semi-structured interviews were conducted to explore the attitudes of information professionals in the United Kingdom towards the challenges facing information-dependent organisations attempting to preserve digital data over the long-term. In particular, the nature of DDOs being encountered by stakeholders, the reasons, policies, and procedures for preserving them, together with a range of specific issues such as; the role of metadata, access to, and rights management of DDOs. The societal need for selection to ensure efficient long-term access is considered. Drawing on - SSM modelling, this research develops a flexible, long-term management framework for digital data at a level higher than metadata, with selection as an essential component. The framework's conceptual feasibility has been examined from both financial and societal benefit perspectives, together with the recognition of constraints. The super-metadata framework provides a possible systematic approach to managing a wide range of digital data in a variety of formats, created/owned by a spectrum of information-dependent organisations