A Distributed Indexing Method for Timeline Similarity Query

Abstract

Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most of these data can be managed in the way of timelines. However, it is still a challenge to effectively and efficiently store, query, and process big timeline data, especially the instant recommendation based on timeline similarities. Most existing studies have focused on indexing spatial and interval datasets rather than the timeline dataset. In addition, many of them are designed for a centralized system. A timeline index structure adapting to parallel and distributed computation framework is in urgent need. In this research, we have defined the timeline similarity query and developed a novel timeline index in the distributed system, called the Distributed Triangle Increment Tree (DTI-Tree), to support the similarity query. The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with the Apache Spark. Furthermore, we have provided an open source timeline benchmark data generator, named TimelineGenerator, to generate various timeline test datasets for different conditions. The experiments for DTI-Tree's construction, insertion, deletion, and similarity queries have been executed on a cluster with two benchmark datasets that are generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data.

Hexadecimal Aggregate Approximation Representation and Classification of Time Series Data

“…For each data set, the range of w was [5,20]. For the SAX method and each w, the range of the corresponding parameter alpha was [3,16]. According to the existing references about SAX, the nearest neighbor classification accuracy results for SAX are always the best for the UCRArchive2018 when the range of w is [5,20] and the range of the corresponding parameter alpha is [3,16].…”

“…Time provides a basic cognitive variable for the continuity and sequential description of object movements and changes in the world [1][2][3][4][5][6]. Human society is facing many challenges, such as environmental pollution, population growth, urban expansion, the transmission of infectious diseases and various natural disaster monitoring and prevention issues, etc.…”

“…These are all closely related to the concept of time and produce massive data containing information regarding time. Especially in recent years, a large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices [2][3][4][5][6]. Time series data can help us understand history, master the present and predict the future, as well as improve our ability to gain insight, perception and prediction of the evolution of various existences and states in the real world.…”

“…These applications cover the domains of images, audio, finance, environmental monitoring and other scientific disciplines [7][8][9]. Many creative representation methods for time series data have been proposed for similarity computing, clustering [10], classification [11], indexing and query [3,8,9,. The taxonomy of time series representations includes four types [10]: data-adaptive, non-data Table 1.…”

DFTHR: A Distributed Framework for Trajectory Similarity Query Based on HBase and Redis

“…There are many different, useful query operations over the trajectory datasets, such as range query, trajectory-to-point neighbor query, and so on. Among the many query operations, trajectory similarity query is an important basic query operator [2]. Its goal is to find one or more trajectories which similar to a query trajectory.…”