Auflistung nach Schlagwort "Heuristics"
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- KonferenzbeitragBenchmarking E-Government Quality - Whose Quality Are We Measuring?(Electronic Government and Electronic Participation - Joint Proceedings of Ongoing Research of IFIP EGOV and IFIP ePart 2016, 2013) Jansen, Arild; Ølnes, SveinThis paper analyses the results of several years of benchmarking of public online services in Norway. We compare these data, which are showing significant differences in measured quality between small and larger municipalities, with results from a comprehensive survey measuring citizens' satisfaction with public services. Finding that these observed differences are not supported by the user survey, we have to ask: whose quality are we really measuring? Many evaluation systems rely on similar heuristic methods, e.g. the EU's eGovernment benchmark 2012 framework, while the Danish benchmarking system has a different approach. The paper argues for a multi-dimensional approach to evaluation of public websites and gives some suggestions for this.
- ZeitschriftenartikelMathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview(Business & Information Systems Engineering: Vol. 61, No. 4, 2019) Fischetti, Martina; Pisinger, DavidWind energy is a fast evolving field that has attracted a lot of attention and investments in the last decades. Being an increasingly competitive market, it is very important to minimize establishment costs and increase production profits already at the design phase of new wind parks. This paper is based on many years of collaboration with Vattenfall, a leading wind energy developer and wind power operator, and aims at giving an overview of the experience of using Mathematical Optimization in the field. The paper illustrates some of the practical needs defined by energy companies, showing how optimization can help the designers to increase production and reduce costs in the design of offshore parks. In particular, the study gives an overview of the individual phases of designing an offshore wind farm, and some of the optimization problems involved. Finally it goes in depth with three of the most important optimization tasks: turbine location, electrical cable routing and foundation optimization. The paper is concluded with a discussion of future challenges.
- ZeitschriftenartikelOffenheit durch XAI bei ML-unterstützten Entscheidungen: Ein Baustein zur Optimierung von Entscheidungen im Unternehmen?(HMD Praxis der Wirtschaftsinformatik: Vol. 58, No. 2, 2021) Lossos, Christian; Geschwill, Simon; Morelli, FrankKünstliche Intelligenz (KI) und Machine Learning (ML) gelten gegenwärtig als probate Mittel, um betriebswirtschaftliche Entscheidungen durch mathematische Modelle zu optimieren. Allerdings werden die Technologien häufig in Form von „Black Box“-Ansätze mit entsprechenden Risiken realisiert. Der Einsatz von Offenheit kann in diesem Kontext mehr Objektivität schaffen und als Treiber für innovative Lösungen fungieren. Rationale Entscheidungen im Unternehmen dienen im Sinne einer Mittel-Zweck-Beziehung dazu, Wettbewerbsvorteile zu erlangen. Im Sinne von Governance und Compliance sind dabei regulatorische Rahmenwerke wie COBIT 2019 und gesetzliche Grundlagen wie die Datenschutz-Grundverordnung (DSGVO) zu berücksichtigen, die ihrerseits ein Mindestmaß an Transparenz einfordern. Ferner sind auch Fairnessaspekte, die durch Bias-Effekte bei ML-Systemen beeinträchtigt werden können, zu berücksichtigen. In Teilaspekten, wie z. B. bei der Modellerstellung, wird in den Bereichen der KI und des ML das Konzept der Offenheit bereits praktiziert. Das Konzept der erklärbaren KI („Explainable Artificial Intelligence“ – XAI) vermag es aber, das zugehörige Potenzial erheblich steigern. Hierzu stehen verschiedene generische Ansätze (Ante hoc‑, Design- und Post-hoc-Konzepte) sowie die Möglichkeit, diese untereinander zu kombinieren, zur Verfügung. Entsprechend müssen Chancen und Grenzen von XAI systematisch reflektiert werden. Ein geeignetes, XAI-basiertes Modell für das Fällen von Entscheidungen im Unternehmen lässt sich mit Hilfe von Heuristiken näher charakterisieren. Artificial Intelligence (AI) and Machine Learning (ML) are currently considered to be effective tools to optimize business decisions by applying mathematical models. However, they are often implemented as “black box” approaches with corresponding risks. In this context, the usage of openness can create more objectivity and act as a driver for innovative solutions. Rational decisions within the company serve the purpose of gaining competitive advantages in the sense of a means-ends relationship. In terms of governance and compliance, regulatory frameworks like COBIT 2019 and legal foundations such as the General Data Protection Regulation (GDPR) must be taken into account, which require a minimum level of transparency. Furthermore, fairness aspects, which can be affected by bias effects in ML models, have also to be considered. In some aspects, such as in model development, openness is already practiced in the areas of AI and ML. However, the concept of Explainable Artificial Intelligence (XAI) is able to significantly increase potentials. Various generic approaches (ante hoc, design and post-hoc concepts) are available for this purpose, as well as the possibility of combining them with each other. Accordingly, the opportunities and limitations of XAI must be systematically reflected upon. An appropriate XAI-based model for decision making in companies can be characterized by support of heuristics.
- ZeitschriftenartikelQuery Optimization in Heterogenous Event Processing Federations(Datenbank-Spektrum: Vol. 15, No. 3, 2015) Pinnecke, Marcus; Hoßbach, BastianContinuous processing of event streams evolved to an important class of data management over the last years and will become even more important due to novel applications such as the Internet of Things. Because systems for data stream and event processing have been developed independent of each other, often in competition and without the existence of any standards, the Stream Processing System (SPS) landscape is extremely heterogeneous today. To overcome the problems caused by this heterogeneity, a novel event processing middleware, the Java Event Processing Connectivity (JEPC), has been presented recently. However, despite the fact that SPSs can be accessed uniformly using JEPC, their different performance profiles caused by different algorithms and implementations remain. This gives the opportunity to query optimization, because individual system strengths can be exploited. In this paper, we present a novel query optimizer that exploits the technical heterogeneity in a federation of different unified SPSs. Taking into account different performance profiles of SPSs, we address query plan partitioning, candidate selection, and reducing inter-system communication in order to improve the overall query performance. We suggest a heuristic that finds a good initial mapping of sub-plans to a set of heterogenous SPSs. An experimental evaluation clearly shows that heterogeneous federations outperform homogeneous federations, in general, and that our heuristic performs well in practice.