Fast and scalable similarity and correlation queries on time series data

Time series are ubiquitous in many fields ranging from financial applications such as the stock market to scientific applications and sensor data. Hence, there has been an increasing interest in time series indexing over the past years because there has been an increasing need for fast methods for analyzing and querying these datasets that are often too big for practical brute force analysis. We start with the main contributions to the field over the past decade and a half. We will then proceed by describing new solutions to correlation analysis on time series datasets using an existing index called the Compact Multi-Resolution Index (CMRI). We describe new algorithms for indexed correlation analysis using Pearson's product moment coefficient and using the multidimensional correlation coefficient and introduce a new measure called Dynamic Time Warping Correlation (DTWC) based on Dynamic Time Warping (DTW). In addition to these linear correlation algorithms, we propose an algorithm called rank order correlation on a non-linear/monotonic measure. To support these algorithms, we revised the Compact Multi-Resolution Index (CMRI) and propose a new index for time series datasets which improves over the sizes, speed and precision of CMRI. We call this index the reduced Compact Multi-Resolution Index (rCMRI). We evaluate the performance of rCMRI compared to CMRI for range queries and range query based queries

A Framework for Distributed Spatial Indexing in Shared-Nothing Architectures

International audienceThe paper presents a complete framework for spatial indexing supportin a distributed setting. We consider a shared-nothingenvironment where a set of servers provides independent storage andcomputational services. Servers only communicate through point-to-pointmessaging, and constitute a non-structured network (i.e., non-central serveror "super peer"). These features cover two popular architectures,namely a strongly connected cluster of servers, and P2P networks.Our proposal extends the recently proposed "Scalable Distributed Rtree(SD-Rtree)" structure with new algorithms and protocols. More specifically,we introduce a nearest-neighbors algorithm, a load balancing methodand a termination protocol. The result constitutes a setof functionalities for distributed spatial indexing that matchesthose commonly found in centralized architectures

Abstract A Framework for Distributed Spatial Indexing in Shared-Nothing Architectures ∗

The paper presents a complete framework for spatial indexing support in a distributed setting. We consider a shared-nothing environment where a set of servers provides independent storage and computational services. Servers only communicate through point-to-point messaging, and constitute a non-structured network (i.e., non-central server or "super peer"). These features cover two popular architectures, namely a strongly connected cluster of servers, and P2P networks. Our proposal extends the recently proposed "Scalable Distributed Rtree (SD-Rtree) " structure with new algorithms and protocols. More specifically, we introduce a nearest-neighbors algorithm, a load balancing method and a termination protocol. The result constitutes a set of functionalities for distributed spatial indexing that matches those commonly found in centralized architectures. Keywords. Distributed index, storage balancing, share-nothing architecture.