Gwendolin Wilke

Integrating an enterprise architecture ontology in a case-based reasoning approach for project knowledge

The retrieval of historical project knowledge is still a challenge for enterprises nowadays. This paper introduces a case-based reasoning (CBR) approach for project knowledge. This approach improves the case-based reasoning by reusing enterprise specific domain knowledge that is defined in an enterprise ontology. Since the retrieval of relevant project knowledge from historical cases is a knowledge intensive task that relies heavily on enterprise specific domain knowledge, we represent both, historical cases as well as the necessary domain knowledge, in an enterprise ontology structure. The contribution of the paper is the introduction of a novel case retrieval mechanism that emphasizes enterprise specific domain knowledge by reusing an enterprise ontology named ArchiMEO. This ontology is a representation of the enterprise architecture ArchiMate® and other integrated standards. This work is based on a real-world scenario elicited from a business partner of the Swiss CTI research project [sic!]. The approach tackles the information need that might occur during a prospective project.

Combining Bottom-Up and Top-Down Generation of Interactive Knowledge Maps for Enterprise Search

Our research project develops an intranet search engine with concept-browsing functionality, where the user is able to navigate the conceptual level in an interactive, automatically generated knowledge map. This knowledge map visualizes tacit, implicit knowledge, extracted from the intranet, as a network of semantic concepts. Inductive and deductive methods are combined; a text analytics engine extracts knowledge structures from data inductively, and the enterprise ontology provides a backbone structure to the process deductively. In addition to performing conventional keyword search, the user can browse the semantic network of concepts and associations to find documents and data records. Also, the user can expand and edit the knowledge network directly. As a vision, we propose a knowledge-management system that provides concept-browsing, based on a knowledge warehouse layer on top of a heterogeneous knowledge base with various systems interfaces. Such a concept browser will empower knowledge workers to interact with knowledge structures.

Equality in Approximate Tolerance Geometry

The framework of Approximate Tolerance Geometry (ATG) has been proposed in [1] as an approach to handling large and heterogeneous imperfections in geometric data in vector-based geographic information systems. Here, different types of positional error can often only be subsumed as possibilistic location constraints. The application of the ATG framework to a classical geometry provides a calculus for the propagation of this error type in geometric reasoning. As a first step towards an implementation of an ATG geometry, the paper applies the framework to the geometric equality relation. It thereby lays the basis for the application of ATG to the other axioms of classical geometry.

Intuitive Knowledge Connectivity: Design and Prototyping of Cross-Platform Knowledge Networks

Individual users are overwhelmed with a flood of data. Current big-data strategies focus mainly on organizational uses of data analytics. To address this gap, we focus on personal data management (PDM) in the era of big data and cloud computing. We are developing and testing a PDM software that enables individuals to construct a cross-platform knowledge network by semi-automatically connecting new relevant data to an existing network of interlinked digital objects. Because the cloud-based services that support our knowledge work are currently fragmented, we suggest an integrated federated platform for editing and searching the personal-knowledge context as a network. This forms a directed edge-labeled property multigraph that spans over all of the cloud-based data silos. We present a design and a proof-of-concept implementation of a PDM tool that allows the creation of a personal-knowledge network that incorporates digital objects from different cloud services.

Towards a Timely Root Cause Analysis for Complex Situations in Large Scale Telecommunications Networks

Abstract Large scale telecommunications networks need to be continuously monitored to detect problems and react accordingly to ensure the networks stability. Current monitoring systems are well capable to monitor such large scale installations for simple situations like failing routers, links or abnormal link utilization. However, current systems fail to provide near real-time in-depth root cause analysis for nation scale networks. This is particularly important in cases where the monitoring dashboard is flooded with notifications caused by side-effects of one bigger failure. In our project we develop a processing system capable of providing this root cause analysis in a timely fashion even for large telecommunications networks. To cope with these challenges we defined a specialized processing model for the detection and analysis of complex error situations which is largely based on event stream processing mechanisms. In this paper we discuss the first stage of this process and its initial implementation.

Ereignisverarbeitung zur Flexiblen Dynamischen Informationsverarbeitung in Smart Cities

ZusammenfassungEin Pfeiler des Smart City Konzeptes ist die Verfügbarkeit von großen Datenmengen, sowie die Fähigkeit diese flexibel und intelligent in Nahe-Echtzeit analysieren und verarbeiten zu können. Als technologische Grundlage für diese oft unterschätze Datenanalyse und -verarbeitungsfähigkeit werden vielfach Technologien aus dem Bereich der Ereignisverarbeitung genannt. In diesem Kapitel werden zentrale Konzepte der Ereignisverarbeitung kurz vorgestellt und deren Nutzen, Grenzen und zukünftige Entwicklungen anhand eines Szenarios aus dem Bereich ad-hoc Car-Sharings beschrieben.AbstractA pillar of the Smart City concept is the availability of big data, together with the ability to process and analyse it flexibly and intelligently in near real time. Event processing technologies are often referred to as a technical basis of the—often underestimated—big data processing and analysis capability. The chapter briefly introduces its central concepts and ideas. We outline the advantages, limits, and future developments of the technology using the example of an ad-hoc car sharing scenario in a Smart City.

Fuzzy logic with evaluated syntax for sound geometric reasoning with geographic information

Many approaches to fuzzifying classical geometries have been proposed and elaborated in the literature. From the field of geographic information science arises the need for yet another approach to the problem, which requires the generalization of ideal points and lines as two-dimensional possibilistic location constraints. We show that geometric reasoning with these location constraints does not satisfy the classical axioms of geometry. As a consequence, geometric reasoning in geographic information systems is not sound and often produces erroneous output. An error propagation calculus for possibilistic location constraints is needed. The paper proposes a framework for defining such an error calculus based on an axiomatic approach to geometric reasoning using fuzzy logic with evaluated syntax as a similarity logic.

A market-based smart grid approach to increasing power grid capacity without physical grid expansion

The continuous increase of competitiveness of renewable energy in combination with the necessity of fossil fuel substitution leads to further electrification of the global energy system and therefore a need for large-scale power grid capacity increase. While physical grid expansion is not feasible for many countries, grid-driven energy management in the Smart Grid often interferes in customer processes and free access to the energy market. The paper solves this dilemma by proposing a market-based load schedule management approach that increases power grid capacity without physical grid expansion. This is achieved by allocating for a certain class of non-critical flexible loads called “conditional loads” the currently unused grid capacity dedicated to ensuring

Granular computing as a basis of human–data interaction: a cognitive cities use case

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