As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. In this work, we propose two resources: (i) a software framework (Resource URL of the framework: https://doi.org/10.5281/zenodo.2109469 ) able to acquire, prepare, and perform a graph-based analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets (Resource URL of the datasets: https://doi.org/10.5281/zenodo.1214433 ) from the LOD Cloud with values for 28 graph measures computed with the framework. We present a preliminary analysis based on the proposed resources and point out implications for synthetic dataset generators. Finally, we identify a set of measures, that can be used to characterize graphs in the Semantic Web.

Several approaches for computing semantic similarity and relatedness measures between terms have been developed. This paper proposes a new semantic similarity measure between two nodes concentrating on nouns as well as their hypernym/hyponym relationships based on the structure of Wordnet. In particular, the similarity of two given nouns depends not only on their positions but also on their relevancy connections in the hierarchy. We evaluate our measure and the other ones on dataset of Miller-Charles and then compute the correlation coefficients to the human judgments. The experimental results show that our method outperforms edge-counting methods.

This paper addresses the issue of analyzing time series with temporal association rule mining techniques. Since originally association rule mining was developed for the analysis of transactional data, as it occurs for instance in market basket analysis, algorithms and time series have to be adapted in order to apply these techniques gainfully to the analysis of time series in general. Continuous time series of different origins can be discretized in order to mine several temporal association rules, what reveals interesting coherences in one and between pairs of time series. Depending on the domain, the knowledge about these coherences can be used for several purposes, e.g. for the prediction of future values of time series. We present a short review on different standard and temporal association rule mining approaches and on approaches that apply association rule mining to time series analysis. In addition to that, we explain in detail how some of the most interesting kinds of temporal association rules can be mined from continuous time series and present an prototype implementation. We demonstrate and evaluate our implementation on two large datasets containing river level measurement and stock data.

This paper presents an application of data mining to the medical domain sleep research, i.e. an approach for automatic sleep stage scoring and apnea-hypopnea detection. By several combined techniques (Fourier and wavelet transform, DDTW and waveform recognition), our approach extracts meaningful features (frequencies and special patterns) from EEG, ECG, EOG and EMG data, on which a decision trees classifier is built for classifying epochs into their sleep stages (according to the rules by Rechtschaffen and Kales) and annotating occurrences of apnea-hypopnea (total or partial cessation of respiration). After that, case-based reasoning is applied to improve quality. We evaluated our approach on 3 large public databases from PhysioBank, which showed an overall accuracy of 95.2% for sleep stage scoring and 94.5% for classifying apneic/non-apneic minutes.

In this work, we describe an approach which aims to make typed texts comparable with temporal data mining methods. This proposal was made in earlier work [11], but to our knowledge no significant research on this subject has been done yet. The basic idea is to derive artificial time series from texts by counting the occurrences of relevant keywords in a sliding window applied to them, and these time series can be compared with techniques of time series analysis. In this particular case the Dynamic Time Warping distance [3] was used. By extensive testing adequate parameters for time series calculation were derived, and we show that this approach might aid in the recognition of similar texts since the observed distances between similar documents are significantly lower than those between unrelated texts. Our idea might also be especially suitable for comparison in different languages since only the keyword translations must be known.

This paper considers the problem of integrating heterogeneous semi-structured data sources with the purpose of estimating integration quality (IQ). Integration of such data sources leads to results with unpredictable trustworthiness and none of the existing methods is capable of accounting for the uncertainty which is accumulated over all of the integration steps and which affects integration quality. To compute the uncertainties we suggest using a well-established statistical method Latent Class Analysis (LCA). This method allows to analyze the influence of the latent factors associated with the real-world entities on the set of data. We show on examples how the proposed approach can be used for evaluating and improving IQ giving an important tool to the users concerned with the data's trustworthiness.

Techniques for the development of reliable information systems on the basis of their formal specification are the main concern in the project. Our work focuses on the specification language TROLL light which allows one to describe the part of the world to be modeled as a community of concurrently existing and communicating objects. Our specification language comes with an integrated, open development environment. The task of this environment is to give support for the creation of correct information systems. Two important ingredients of the environment are the animator and the proof support system.>

Within the compound project KorSo our team is concerned with the research on techniques and methods for the development of reliable information systems on the basis of formal specifications. Our work focuses on the specification language TROLL light which allows to describe the part of the world which is to be modeled as a community of concurrently existing and communicating objects by determining their structure as well as their behavior. Moreover we develop and implement a computer aided specification environment for TROLL light which permits a prototyping animation as well as the proof of properties of specifications. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.