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Anomaly detection is considered an important data mining task, aiming at the discovery of elements (also known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the(More)
Top-k dominating queries use an intuitive scoring function which ranks multidimensional points with respect to their dominance power, i.e., the number of points that a point dominates. The k points with the best (e.g., highest) scores are returned to the user. Both top-k and skyline queries have been studied in a streaming environment, where changes to the(More)
Preference queries have received considerable attention in the recent past, due to their use in selecting the most preferred objects , especially when the selection criteria are contradictory. Nowadays, a significant number of applications require the manipulation of time evolving data and therefore the study of continuous query processing has recently(More)
Dominating queries are significant tools for preference-based query processing in databases and decision support applications. An important preference-based query is the top-k dominating query, which reports the k most important objects according to their domination capabilities (score). In this paper, we address the following issues to tackle two(More)
Trend analysis of time series data is an important research direction. In streaming time series the problem is more challenging, taking into account the fact that new values arrive for the series, probably in very high rates. Therefore, effective and efficient methods are required in order to classify a streaming time series based on its trend. Since new(More)
Anomaly detection is an important data mining task, aiming at the discovery of elements that show significant diversion from the expected behavior; such elements are termed as outliers. One of the most widely employed criteria for determining whether an element is an outlier is based on the number of neighboring elements within a fixed distance (<i>R</i>),(More)
Dense subgraph discovery is a key issue in graph mining, due to its importance in several applications, such as correlation analysis, community discovery in the Web, gene co-expression and protein-protein interactions in bioinformatics. In this work, we study the discovery of the top-k dense subgraphs in a set of graphs. After the investigation of the(More)
Trend analysis of time series is an important problem since trend identification enables the prediction of the near future. In streaming time series the problem is more challenging due to the dynamic nature of the data. In this paper, we propose a method to continuously clustering a number of streaming time series based on their trend characteristics. Each(More)
Skyline queries are important due to their usefulness in many application domains. However, by increasing the number of attributes, the probability that a tuple dominates another one is reduced significantly. To attack this problem, <i>k</i>-dominant skylines have been proposed, relaxing the definition of domination. In this paper, we study the problem of(More)