Clustering Algorithms for Storage of Tick Data
Gábor Nagy, Krisztián Búza · 2012
Abstract. Tick data is one of the most prominent types of temporal data, as it can be used to represent data in various domains such as geophysics or finance. Storage of tick data is a challenging problem because two criteria have to be fulfilled simultaneously: the storage structure should allow fast execution of queries and the data should not occupy too much space on the hard disk or in the main memory. We present two clustering-based solutions, in particular, our recently-developed cluster-ing algorithms, SOHAC and SOPAC. These algorithms are designed to support the storage of tick data and are under publication (see References). We evaluate our algorithms both on publicly available real-world datasets, as well as real-world tick data from the financial domain provided by one of the world-wide most renowned investment bank. In our experiments, we compare our approaches, SOHAC and SOPAC, against a large collection of conventional clustering algorithms from the literature. The experiments show that our algorithm substantially outperforms – both in terms of statistical significance and practical relevance – the examined clus-tering algorithms for the tick data storage problem. Additionally, we present our