Many distance or similarity measures have been proposed for time series similarity search. However, none of these measures is guaranteed to be optimal when used for 1-Nearest Neighbor (NN) classification. In this paper we study the problem of selecting the most appropriate distance measure, given a pool of time series distance measures and a query, so as to perform NN classification of the query. We propose a framework for solving this problem, by identifying, given the query, the distance measure most likely to produce the correct classification result for that query. From this proposed framework, we derive three specific methods, that differ from each other in the way they estimate the probability that a distance measure correctly classifies a query object. In our experiments, our pool of measures consists of Dynamic Time Warping (DTW), Move-Split-Merge (MSM), and Edit distance with Real Penalty (ERP). Based on experimental evaluation with 45 datasets, the best-performing of the three proposed methods provides the best results in terms of classification error rate, compared to the competitors, which include using the Cross Validation method for selecting the distance measure in each dataset, as well as using a single specific distance measure (DTW, MSM, or ERP) across all datasets.

Sequences of event intervals appear in several application domains including sign language, sensor networks, medicine, human motion databases, and linguistics. Such sequences comprise events that occur at time intervals and are time stamped at their start and end time. In this paper, we propose a new method, called IBSM, for comparing such sequences. IBSM performs full sequence matching using a vector-based representation of the original sequence. At each time point an event vector is computed; hence, the original sequence is mapped to an ordered set of vectors, which we call event table. Given two sequences, their event tables are resized using bilinear interpolation, which ensures they are of the same size. The resulting event tables are then compared using the Euclidean distance. In addition, we propose two techniques for reducing the computational cost of IBSM when performing nearest neighbor search in a large database. Extensive experiments on eight real datasets show that IBSM outperforms existing state-of-the-art methods by up to a factor of two in terms of nearest neighbor classification accuracy, and by up to two orders of magnitude in terms of runtime.

We present "Hum-a-song", a system built for music retrieval, and particularly for the Query-By-Humming (QBH) application. According to QBH, the user is able to hum a part of a song that she recalls and would like to learn what this song is, or find other songs similar to it in a large music repository. We present a simple yet efficient approach that maps the problem to time series subsequence matching. The query and the database songs are represented as 2-dimensional time series conveying information about the pitch and the duration of the notes. Then, since the query is a short sequence and we want to find its best match that may start and end anywhere in the database, subsequence matching methods are suitable for this task. In this demo, we present a system that employs and exposes to the user a variety of state-of-the-art dynamic programming methods, including a newly proposed efficient method named SMBGT that is robust to noise and considers all intrinsic problems in QBH; it allows variable tolerance levels when matching elements, where tolerances are defined as functions of the compared sequences, gaps in both the query and target sequences, and bounds the matching length and (optionally) the minimum number of matched elements. Our system is intended to become open source, which is to the best of our knowledge the first non-commercial effort trying to solve QBH with a variety of methods, and that also approaches the problem from the time series perspective.

Sensor Networks have received considerable attention recently, as they provide manifold benefits. Not only are they a means for data acquisition and monitoring of unexplored or inaccessible areas, they are also a low-cost alternative for sensing the environment, which greatly aids to better understand our surroundings. A major motivation in either occasion is to acknowledge endangering situations and take action(s) accordingly. To this end, we would like to enable data mining or analysis techniques on top or, even better, within such networks, due to the prohibitive cost of communication in this setting. In this work, we demonstrate running data mining algorithms on a set of sensors, which are of low-processing power. In addition to showcasing the execution of data analysis algorithms on resource-constrained hardware, our demo is intended to show how to take advantage of the properties of each algorithm to make better use of the sensors and their capabilities. We support the execution and monitoring of these algorithms with a graphical user interface (GUI).