Gold Embroidery: A Sophisticated Technique for Early Mycenaean Swords and Daggers

One of the advanced decorative techniques of the Late Bronze Age Aegean is so-called gold embroidery, restricted only to luxurious weapons of the Early Mycenaean period. The technique consists in the dense placement of minute twisted gold filaments next to each other in order to give the impression of a mosaic. In the final stage, the craftsman decorated the whole with engraved designs, usually spirals. The present paper presents a detailed discussion on the history of research, the context, chronology and typology of the known examples, and a technical analysis based on archaeometric and experimental data. It is suggested that the use of the technique, which extends across the LH I–IIIA1 period, was reserved for burials of the highest status and was associated with other exclusive metal-working techniques, like inlaid decoration. The technique is unknown in Minoan Crete and the Eastern Mediterranean and, so far, the only possible parallels are to be found in the Wessex and Armorican cultures

A Rule-based Skyline Computation over a dynamic database

Skyline query which relies on the notion of Pareto dominance filters the data items from a database by ensuring only those data items that are not worse than any others are selected as skylines. However, the dynamic nature of databases in which their states and/or structures change throughout their lifetime to incorporate the current and latest information of database applications, requires a new set of skylines to be derived. Blindly computing skylines on the new state/structure of a database is inefficient, as not all the data items are affected by the changes. Hence, this paper proposes a rule-based approach in tackling the above issue with the main aim at avoiding unnecessary skyline computations. Based on the type of operation that changes the state/structure of a database, i.e. insert/delete/update a data item(s) or add/remove a dimension(s), a set of rules are defined. Besides, the prominent dominance relationships when pairwise comparisons are performed are retained; which are then utilised in the process of computing a new set of skylines. Several analyses have been conducted to evaluate the performance and prove the efficiency of our proposed solution

Data mining and query processing methods in data streams

This thesis studies the problem of data management in data streams in order to reduce the response time and therefore to improve quality of services taking account the memory requirements which is a major limitation in data streams. More specifically, we study data mining problems such as clustering and classification in data streams and we develop incremental algorithm with low memory requirements. We examine similarity queries between different data streams and we propose an indexing scheme suitable for dynamic data. We also propose algorithms for continuous preference queries evaluation problems in sensor networks. We introduce queries for knowledge discovery and we propose algorithms for their evaluation.Η παρούσα διατριβή μελετά προβλήματα διαχείρισης δεδομένων σε ροές δεδομένων με σκοπό την ελάττωση του χρόνου απόκρισης και κατά συνέπεια τη βελτίωση εξυπηρέτησης των χρηστών λαμβάνοντας υπόψη τις απαιτήσεις μνήμης που αποτελεί βασικό περιορισμό στις ροές δεδομένων. Συγκεκριμένα, μελετάμε προβλήματα εξόρυξης δεδομένων όπως ομαδοποίηση και κατηγοριοποίηση σε ροές δεδομένων και αναπτύσσουμε επαυξητικούς αλγορίθμους με χαμηλές απαιτήσεις μνήμης. Εξετάζουμε ερωτήματα ομοιότητας μεταξύ διαφορετικών ροών δεδομένων και προτείνουμε ένα σχήμα δεικτοδότησης κατάλληλο για δυναμικά δεδομένα. Προτείνουμε επίσης αλγόριθμους για την αποτίμηση συνεχών ερωτημάτων προτίμησης σε ροές δεδομένων. Τέλος, μελετάμε προβλήματα διαχείρισης δεδομένων σε δίκτυα αισθητήρων. Παρουσιάζουμε ερωτήματα για την εξαγωγή γνώσης και προτείνουμε αλγορίθμους για την αποτίμησή τους

Efficient Incremental Subspace Clustering in Data Streams

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 α-clusters in each time instance separately. A subspace αcluster consists of a set of streams, whose value difference is less than α in a consecutive number of time instances (dimensions). The clusters can be continuously and incrementally updated as the streaming time series evolve. The proposed technique is based on a careful examination of pair-wise stream similarities for a subset of dimensions and then, it is generalized for more streams per cluster. Performance evaluation results show that the proposed pruning criteria are important for search space reduction, and that the cost of incremental cluster monitoring is computationally more efficient than reclustering.

with sliding windows

Adaptive similarity search in streaming time serie

Similarity Search in Time Series Databases

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 efficien

Content-based Information Retrieval in Streaming Music *

In this paper we examine searching by content in broadcasted streams of musical data, where the querier defines a set of preferred musical pieces and receives a list of broadcasting feeds that contain music similar to the preferred set. Streaming environments impose challenging requirements for content-based music information retrieval as memory limitations do not allow for buffering, data accumulation “on-the-fly ” makes pre & post processing not a possibility while high response time is a necessity. To address these requirements we devise an incremental version of an award winning feature extraction and similarity process and propose a song boundary detection method in order to increase similarity accuracy and reduce costly feature extraction and similarity calculations. Extensive experimental results verify our claims and illustrate the superiority of the proposed method, over a baseline approach, as well as the suitability of the method for the streaming environment

Continuous trendbased classification of streaming time series

Abstract. 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 values are continuously arrive for each stream, the classification is performed by means of a sliding window which focuses on the last values of each stream. Each streaming time series is transformed to a vector by means of a Piecewise Linear Approximation (PLA) technique. The PLA vector is a sequence of symbols denoting the trend of the series, and it is constructed incrementally. The PLA is composed of a series of segments representing the trend of the raw data (either UP or DOWN). Efficient in-memory methods are used in order to: 1) determine the class of each streaming time series and 2) determine the streaming time series that comprise a specific trend class. Performance evaluation based on real-life datasets is performed, which shows the efficiency of the proposed approach both with respect to classification time and storage requirements. The proposed method can be used in order to continuously classify a set of streaming time series according to their trends, to monitor the behavior of a set of streams and to monitor the contents of a set of trend classes