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    @Article{OJIS-v1i1n02_Kessler,
        title     = {Pattern-sensitive Time-series Anonymization and its Application to Energy-Consumption Data},
        author    = {Stephan Kessler and
                     Erik Buchmann and
                     Thorben Burghardt and
                     Klemens B{\"o}hm},
        journal   = {Open Journal of Information Systems (OJIS)},
        issn      = {2198-9281},
        year      = {2014},
        volume    = {1},
        number    = {1},
        pages     = {3--22},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194696},
        urn       = {urn:nbn:de:101:1-201705194696},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Time series anonymization is an important problem. One prominent example of time series are energy consumption records, which might reveal details of the daily routine of a household. Existing privacy approaches for time series, e.g., from the field of trajectory anonymization, assume that every single value of a time series contains sensitive information and reduce the data quality very much. In contrast, we consider time series where it is combinations of tuples that represent personal information. We propose (n; l; k)-anonymity, geared to anonymization of time-series data with minimal information loss, assuming that an adversary may learn a few data points. We propose several heuristics to obtain (n; l; k)-anonymity, and we evaluate our approach both with synthetic and real data. Our experiments confirm that it is sufficient to modify time series only moderately in order to fulfill meaningful privacy requirements.}
    }
    @Article{OJDB-2015v2i1n01_Buchmann,
        title     = {Deriving Bounds on the Size of Spatial Areas},
        author    = {Erik Buchmann and
                     Patrick Erik Bradley and
                     Klemens B{\"o}hm},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {1--16},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194566},
        urn       = {urn:nbn:de:101:1-201705194566},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Many application domains such as surveillance, environmental monitoring or sensor-data processing need upper and lower bounds on areas that are covered by a certain feature. For example, a smart-city infrastructure might need bounds on the size of an area polluted with fine-dust, to re-route combustion-engine traffic. Obtaining such bounds is challenging, because in almost any real-world application, information about the region of interest is incomplete, e.g., the database of sensor data contains only a limited number of samples. Existing approaches cannot provide upper and lower bounds or depend on restrictive assumptions, e.g., the area must be convex. Our approach in turn is based on the natural assumption that it is possible to specify a minimal diameter for the feature in question. Given this assumption, we formally derive bounds on the area size, and we provide algorithms that compute these bounds from a database of sensor data, based on geometrical considerations. We evaluate our algorithms both with a real-world case study and with synthetic data.}
    }
    @Article{OJIS-2014v1i2n01_Stackelberg,
        title     = {Detecting Data-Flow Errors in BPMN 2.0},
        author    = {Silvia von Stackelberg and
                     Susanne Putze and
                     Jutta M{\"u}lle and
                     Klemens B{\"o}hm},
        journal   = {Open Journal of Information Systems (OJIS)},
        issn      = {2198-9281},
        year      = {2014},
        volume    = {1},
        number    = {2},
        pages     = {1--19},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017052611934},
        urn       = {urn:nbn:de:101:1-2017052611934},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Data-flow errors in BPMN 2.0 process models, such as missing or unused data, lead to undesired process executions. In particular, since BPMN 2.0 with a standardized execution semantics allows specifying alternatives for data as well as optional data, identifying missing or unused data systematically is difficult. In this paper, we propose an approach for detecting data-flow errors in BPMN 2.0 process models. We formalize BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We define a set of anti-patterns representing data-flow errors of BPMN 2.0 process models. By employing the anti-patterns, our tool performs model checking for the unfolded Petri Nets. The evaluation shows that it detects all data-flow errors identified by hand, and so improves process quality.}
    }