Big Sequence Management

EasyChair Preprint 3836

Abstract

Data series are a prevalent data type that has attracted lots of interest in recent years. Specifically, there has been an explosive interest towards the analysis of large volumes of data series in many different domains. This is both in businesses (e.g., in mobile applications) and in sciences (e.g., in biology). In this tutorial, we focus on applications that produce massive collections of data series, and we provide the necessary background on data series storage, retrieval and analytics. We look at systems historically used to handle and mine data in the form of data series, as well as at the state of the art data series management systems that were recently proposed. Moreover, we discuss the need for fast similarity search for supporting data mining applications, and describe efficient similarity search techniques, indexes and query processing algorithms. Finally, we look at the gap of modern data series management systems in regards to support for efficient complex analytics, and we argue in favor of the integration of summarizations and indexes in modern data series management systems. We conclude with the challenges and open research problems in this domain.

Keyphrases: Big Sequence Management, data series, sequences

@booklet{EasyChair:3836,
  author    = {Karima Echihabi and Kostas Zoumpatianos and Themis Palpanas},
  title     = {Big Sequence Management},
  howpublished = {EasyChair Preprint 3836},
  year      = {EasyChair, 2020}}