An ontological characterization of time-series and state-sequences for data mining

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.

[Conference report] Magic and mathematics: The life and work of John Dee. National Maritime Museum, Greenwich, 13-14 June 2003

Tony Mann provides a report of a two-day meeting "Magic and mathematics: The life and work of John Dee" held from 13-14 June 2003 at the National Maritime Museum, Greenwich.

[Book review] Mathematics and Culture II

Review of Mathematics and Culture II. Visual Perfection: Mathematics and Creativity, Michele Emmer (Ed.), Springer, 2005 ISBN: 978-3-540-21368-0

BSHM meetings: BMS History of Mathematics Splinter Group

Introduction to abstracts from papers given at BMS History of Mathematics Splinter Group, held 17 April 2007, in Swansea.

BSHM meetings: British Mathematics Colloquium

Report on the British Mathematics Colloquium, which took place in York, 25-28 March 2008. Also includes abstracts of the individual talks.

[BSHM meetings] Topics in the history of financial mathematics: early commerce to chaos in modern stock markets

This account provides an overview of the study day, entitled 'Topics in the History of Financial Mathematics: Early commerce to chaos in modern stock markets,' held by the British Society for the History of Mathematics jointly with Gresham College, at Gresham College, London on 25th April 2008. The series of talks explored the development of mathematics and mathematical techniques in a commercial and financial context.

Reviews: making mathematics with needlework

Review of Making Mathematics with Needlework, edited by Sarah-Marie Belcastro and Carolyn Yackel, published by AK Peters Ltd, 2007 (ISBN 978-1-56881-331-8).

[Book review] Collaborative learning in mathematics

Review of: Collaborative Learning in Mathematics: A challenge to our beliefs and practices by Malcolm Swan, National Institute of Adult Continuing Education, paperback £24.95, ISBN 981-1-86201-311-7; hardback £44.95, ISBN 978-1-86201-316-2.

Some issues on assessment methods and learning in mathematics and statistics

This paper looks at the application of some of the assessment methods in practice with the view to enhance students’ learning in mathematics and statistics. It explores the effective application of assessment methods and highlights the issues or problems, and ways of avoiding them, related to some of the common methods of assessing mathematical and statistical learning. Some observations made by the author on good assessment practice and useful approaches employed at his institution in designing and applying assessment methods are discussed. Successful strategies in implementing assessment methods at different levels are described.

A robust approach to subsequence matching

In terms of a general time theory which addresses time-elements as typed point-based intervals, a formal characterization of time-series and state-sequences is introduced. Based on this framework, the subsequence matching problem is specially tackled by means of being transferred into bipartite graph matching problem. Then a hybrid similarity model with high tolerance of inversion, crossover and noise is proposed for matching the corresponding bipartite graphs involving both temporal and non-temporal measurements. Experimental results on reconstructed time-series data from UCI KDD Archive demonstrate that such an approach is more effective comparing with the traditional similarity model based algorithms, promising robust techniques for lager time-series databases and real-life applications such as Content-based Video Retrieval (CBVR), etc.

Temporal pattern recognition in video clips detection

Temporal representation and reasoning plays an important role in Data Mining and Knowledge Discovery, particularly, in mining and recognizing patterns with rich temporal information. Based on a formal characterization of time-series and state-sequences, this paper presents the computational technique and algorithm for matching state-based temporal patterns. As a case study of real-life applications, zone-defense pattern recognition in basketball games is specially examined as an illustrating example. Experimental results demonstrate that it provides a formal and comprehensive temporal ontology for research and applications in video events detection.