Maria Kontaki

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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)
In many application domains, data can be represented as a series of values (time series). Examples include stocks, seismic signals, audio, and many more. Similarity search in time series databases is an important research direction. Several methods have been proposed in order to provide algorithms for efficient query processing in the case of static time(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)
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)
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)
Performing data mining tasks in streaming data is considered a challenging research direction, due to the continuous data evolution. In this work, we focus on the problem of clustering streaming time series, based on the sliding window paradigm. More specifically, we use the concept of subspace a-clusters. A subspace a-cluster consists of a set of streams,(More)
Anomaly detection is considered an important data mining task, aiming at the discovery of elements (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)