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- ZeitschriftenartikelCrowd Work(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Durward, David; Blohm, Ivo; Leimeister, Jan Marco
- ZeitschriftenartikelProf. Dr. Dr. h.c. Norbert Szyperski(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Klein, Stefan; Loebbecke, Claudia
- ZeitschriftenartikelTheories in Business and Information Systems Engineering(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Bichler, Martin; Frank, Ulrich; Avison, David; Malaurent, Julien; Fettke, Peter; Hovorka, Dirk; Krämer, Jan; Schnurr, Daniel; Müller, Benjamin; Suhl, Leena; Thalheim, Bernhard
- ZeitschriftenartikelInterview with Michael Nilles on “What Makes Leaders Successful in the Age of the Digital Transformation?”(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Maedche, Alexander
- ZeitschriftenartikelPrescriptive Control of Business Processes(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Krumeich, Julian; Werth, Dirk; Loos, PeterThis paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.
- ZeitschriftenartikelCall for Papers: Issue 1/2018(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Horkoff, Jennifer; Jeusfeld, Manfred A.; Ralyté, Jolita; Karagiannis, Dimitris
- ZeitschriftenartikelDisciplinary Pluralism, Flagship Conferences, and Journal Submissions(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Heinzl, Armin; Bichler, Martin; Aalst, Wil
- ZeitschriftenartikelA Majority Vote Based Classifier Ensemble for Web Service Classification(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Qamar, Usman; Niza, Rozina; Bashir, Saba; Khan, Farhan HassanService oriented architecture is a glue that allows web applications to work in collaboration. It has become a driving force for the service-oriented computing (SOC) paradigm. In heterogeneous environments the SOC paradigm uses web services as the basic building block to support low costs as well as easy and rapid composition of distributed applications. A web service exposes its interfaces using the Web Service Description Language (WSDL). A central repository called universal description, discovery and integration (UDDI) is used by service providers to publish and register their web services. UDDI registries are used by web service consumers to locate the web services they require and metadata associated with them. Manually analyzing WSDL documents is the best approach, but also most expensive. Work has been done on employing various approaches to automate the classification of web services. However, previous research has focused on using a single technique for classification. This research paper focuses on the classification of web services using a majority vote based classifier ensemble technique. The ensemble model overcomes the limitations of conventional techniques by employing the ensemble of three heterogeneous classifiers: Naïve Bayes, decision tree (J48), and Support Vector Machines. We applied tenfold cross-validation to test the efficiency of the model on a publicly available dataset consisting of 3738 real world web services categorized into 5 fields, which yielded an average accuracy of 92 %. The high accuracy is owed to two main factors, i.e., enhanced pre-processing with focused feature selection, and majority based ensemble classification.