Filtern
Erscheinungsjahr
Dokumenttyp
- Ausgabe (Heft) zu einer Zeitschrift (132) (entfernen)
Schlagworte
- Bluetooth (4)
- computer clusters (4)
- ontology (4)
- Knowledge Compilation (3)
- parallel algorithms (3)
- Augmented Reality (2)
- Campus Information System (2)
- Customer Relationship Management (2)
- DOCHOUSE (2)
- Datensicherheit (2)
Institut
- Fachbereich 4 (132) (entfernen)
Integration von CRM-Systemen mit Kollaborations-Systemen am Beispiel von DocHouse und Lotus Quickr
(2012)
Der vorliegende Arbeitsbericht "Integration von CRM-Systemen mit Kollaborations-Systemen am Beispiel von DocHouse/ BRM und IBM Lotus Quickr" ist Ergebnis einer studentischen Projektarbeit. Ziel des Projekts war es Integrationsszenarien zwischen einem CRM-System und einem Kollaborati-onssystem zu erarbeiten und eine prototypische Schnittstelle mit entsprechender Funktion zwischen den Systemen DocHouse/ BRM und IBM Lotus Quickr zu implementieren.
Ein besonderer Dank geht in diesem Zusammenhang an Herr Wolfgang Brugger (Geschäftsführer der DocHouse GmbH), der die Idee einer solchen Entwicklung hatte und die FG BAS mit deren Durchführung betraute. Die Erstellung des Konzepts und des Prototyps wurde vom Winter 2010 bis Sommer 2011 von den Studenten Björn Lilge, Ludwig Paulsen, Marco Wolf, Markus Aldenhövel, Martin Surrey und Mike Reuthers im Rahmen ihres Projektpraktikums durchgeführt. Das Projektteam wurde bei der Konzeption und Implementierung inhaltlich und organisatorisch von Dipl.-Wirt.-Inform. Roland Diehl betreut.
Schema information about resources in the Linked Open Data (LOD) cloud can be provided in a twofold way: it can be explicitly defined by attaching RDF types to the resources. Or it is provided implicitly via the definition of the resources´ properties.
In this paper, we analyze the correlation between the two sources of schema information. To this end, we have extracted schema information regarding the types and properties defined in two datasets of different size. One dataset is a LOD crawl from TimBL- FOAF profile (11 Mio. triple) and the second is an extract from the Billion Triples Challenge 2011 dataset (500 Mio. triple). We have conducted an in depth analysis and have computed various entropy measures as well as the mutual information encoded in this two manifestations of schema information.
Our analysis provides insights into the information encoded in the different schema characteristics. It shows that a schema based on either types or properties alone will capture only about 75% of the information contained in the data. From these observations, we derive conclusions about the design of future schemas for LOD.
In this paper, we demonstrate by means of two examples how to work with probability propagation nets (PPNs). The fiirst, which comes from the book by Peng and Reggia [1], is a small example of medical diagnosis. The second one comes from [2]. It is an example of operational risk and is to show how the evidence flow in PPNs gives hints to reduce high losses. In terms of Bayesian networks, both examples contain cycles which are resolved by the conditioning technique [3].
Virtual Goods + ODRL 2012
(2012)
This is the 10th international workshop for technical, economic, and legal aspects of business models for virtual goods incorporating the 8th ODRL community group meeting. This year we did not call for completed research results, but we invited PhD students to present and discuss their ongoing research work. In the traditional international group of virtual goods and ODRL researchers we discussed PhD research from Belgium, Brazil, and Germany. The topics focused on research questions about rights management in the Internet and e-business stimulation. In the center of rights management stands the conception of a formal policy expression that can be used for human readable policy transparency, as well as for machine readable support of policy conformant systems behavior up to automatic policy enforcement. ODRL has proven to be an ideal basis for policy expressions, not only for digital copy rights, but also for the more general "Policy Awareness in the World of Virtual Goods". In this sense, policies support the communication of virtual goods, and they are a virtualization of rules-governed behavior themselves.
Iterative Signing of RDF(S) Graphs, Named Graphs, and OWL Graphs: Formalization and Application
(2013)
When publishing graph data on the web such as vocabulariesrnusing RDF(S) or OWL, one has only limited means to verify the authenticity and integrity of the graph data. Today's approaches require a high signature overhead and do not allow for an iterative signing of graph data. This paper presents a formally defined framework for signing arbitrary graph data provided in RDF(S), Named Graphs, or OWL. Our framework supports signing graph data at different levels of granularity: minimum self-contained graphs (MSG), sets of MSGs, and entire graphs. It supports for an iterative signing of graph data, e. g., when different parties provide different parts of a common graph, and allows for signing multiple graphs. Both can be done with a constant, low overhead for the signature graph, even when iteratively signing graph data.
This paper presents a method for the evolution of SHI ABoxes which is based on a compilation technique of the knowledge base. For this the ABox is regarded as an interpretation of the TBox which is close to a model. It is shown, that the ABox can be used for a semantically guided transformation resulting in an equisatisfiable knowledge base. We use the result of this transformation to effciently delete assertions from the ABox. Furthermore, insertion of assertions as well as repair of inconsistent ABoxes is addressed. For the computation of the necessary actions for deletion, insertion and repair, the E-KRHyper theorem prover is used.
Various best practices and principles guide an ontology engineer when modeling Linked Data. The choice of appropriate vocabularies is one essential aspect in the guidelines, as it leads to better interpretation, querying, and consumption of the data by Linked Data applications and users.
In this paper, we present the various types of support features for an ontology engineer to model a Linked Data dataset, discuss existing tools and services with respect to these support features, and propose LOVER: a novel approach to support the ontology engineer in modeling a Linked Data dataset. We demonstrate that none of the existing tools and services incorporate all types of supporting features and illustrate the concept of LOVER, which supports the engineer by recommending appropriate classes and properties from existing and actively used vocabularies. Hereby, the recommendations are made on the basis of an iterative multimodal search. LOVER uses different, orthogonal information sources for finding terms, e.g. based on a best string match or schema information on other datasets published in the Linked Open Data cloud. We describe LOVER's recommendation mechanism in general and illustrate it alongrna real-life example from the social sciences domain.
E-KRHyper is a versatile theorem prover and model generator for firstorder logic that natively supports equality. Inequality of constants, however, has to be given by explicitly adding facts. As the amount of these facts grows quadratically in the number of these distinct constants, the knowledge base is blown up. This makes it harder for a human reader to focus on the actual problem, and impairs the reasoning process. We extend E-Hyper- underlying E-KRhyper tableau calculus to avoid this blow-up by implementing a native handling for inequality of constants. This is done by introducing the unique name assumption for a subset of the constants (the so called distinct object identifiers). The obtained calculus is shown to be sound and complete and is implemented into the E-KRHyper system. Synthetic benchmarks, situated in the theory of arrays, are used to back up the benefits of the new calculus.
Im Laufe der letzten Jahre hat sich der typische Komplex an kritischen Erfolgsfaktoren für Unternehmen verändert, infolgedessen der Faktor Wissen eine wachsende Bedeutung erlangt hat. Insofern kann man zum heutigen Zeitpunkt von Wissen als viertem Produktionsfaktor sprechen, welcher die Faktoren Arbeit, Kapital und Boden als wichtigste Faktoren eines Unternehmens ablöst (vgl. Keller & Yeaple 2013, S. 2; Kogut & Zander 1993, S. 631). Dies liegt darin begründet, dass aktive Maßnahmen zur Unterstützung von Wissenstransfer in Unternehmen höhere Profite und Marktanteile sowie bessere Überlebensfähigkeit gegenüber Wettbewerbern ohne derartige Maßnahmen nach sich ziehen (vgl. Argote 1999, S. 28; Szulanski 1996, S. 27; Osterloh & Frey 2000, S. 538). Der hauptsächliche Vorteil von wissensbasierten Entwicklungen liegt dabei in deren Nachhaltigkeit, da aufgrund der immateriellen Struktur (vgl. Inkpen & Dinur 1998, S. 456; Spender 1996a, S. 65 f.; Spender 1996b, S. 49; Nelson & Winter 1982, S. 76 ff.) eine Nachahmung durch Wettbewerber erschwert wird (vgl. Wernerfelt 1984, S. 173; Barney 1991, S. 102).