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Agility is considered the silver bullet for survival in the VUCA world. However, many organisations are afraid of endangering their ISO 9001 certificate when introducing agile processes. A joint research project of the University of Applied Sciences and Arts Hannover and the DGQ has set itself the goal of providing more security in this area. The findings were based on interviews with managers and team members from various organisations of different sizes and industries working in an agile manner as well as on common audit practices and a literature analysis. The outcome presents a clear distinction of agility from flexibility as well as useful guidelines for the integration of agile processes in QM systems - for QM practitioners and auditors alike.
Integrating distributional and lexical information for semantic classification of words using MRMF
(2016)
Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multirelational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.
In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics.
Intrusion detection systems and other network security components detect security-relevant events based on policies consisting of rules. If an event turns out as a false alarm, the corresponding policy has to be adjusted in order to reduce the number of false positives. Modified policies, however, need to be tested before going into productive use. We present a visual analysis tool for the evaluation of security events and related policies which integrates data from different sources using the IF-MAP specification and provides a “what-if” simulation for testing modified policies on past network dynamics. In this paper, we will describe the design and outcome of a user study that will help us to evaluate our visual analysis tool.
For anomaly-based intrusion detection in computer networks, data cubes can be used for building a model of the normal behavior of each cell. During inference an anomaly score is calculated based on the deviation of cell metrics from the corresponding normality model. A visualization approach is shown that combines different types of diagrams and charts with linked user interaction for filtering of data.
Hadoop is a Java-based open source programming framework, which supports the processing and storage of large volumes of data sets in a distributed computing environment. On the other hand, an overwhelming majority of organizations are moving their big data processing and storing to the cloud to take advantage of cost reduction – the cloud eliminates the need for investing heavily in infrastructures, which may or may not be used by organizations. This paper shows how organizations can alleviate some of the obstacles faced when trying to make Hadoop run in the cloud.
Our work is motivated primarily by the lack of standardization in the area of Event Processing Network (EPN) models. We identify general requirements for such models. These requirements encompass the possibility to describe events in the real world, to establish temporal and causal relationships among the events, to aggregate the events, to organize the events into a hierarchy, to categorize the events into simple or complex, to create an EPN model in an easy and simple way and to use that model ad hoc. As the major contribution, this paper applies the identified requirements to the RuleCore model.
With an increasing complexity and scale, sufficient evaluation of Information Systems (IS) becomes a challenging and difficult task. Simulation modeling has proven as suitable and efficient methodology for evaluating IS and IS artifacts, presupposed it meets certain quality demands. However, existing research on simulation modeling quality solely focuses on quality in terms of accuracy and credibility, disregarding the role of additional quality aspects. Therefore, this paper proposes two design artifacts in order to ensure a holistic quality view on simulation quality. First, associated literature is reviewed in order to extract relevant quality factors in the context of simulation modeling, which can be used to evaluate the overall quality of a simulated solution before, during or after a given project. Secondly, the deduced quality factors are integrated in a quality assessment framework to provide structural guidance on the quality assessment procedure for simulation. In line with a Design Science Research (DSR) approach, we demonstrate the eligibility of both design artifacts by means of prototyping as well as an example case. Moreover, the assessment framework is evaluated and iteratively adjusted with the help of expert feedback.
Complex Event Processing (CEP) has been established as a well-suited software technology for processing high-frequent data streams. However, intelligent stream based systems must integrate stream data with semantical background knowledge. In this work, we investigate different approaches on integrating stream data and semantic domain knowledge. In particular, we discuss from a software engineering per- spective two different architectures: an approach adding an ontology access mechanism to a common Continuous Query Language (CQL) is compared with C-SPARQL, a streaming extension of the RDF query language SPARQL.