Time Travel and Temporal Aggregation Query Processing Methods

A processing method and time technology, applied in the computer field, can solve problems such as temporal big data time travel and temporal aggregation operations without developing distributed solutions, and temporal data query without native support, so as to achieve easy understanding and implementation, The effect of improving query speed and query efficiency

Active Publication Date: 2021-08-31
SHANGHAI JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As far as we know, none of the existing big data systems (such as Apache Hadoop, Apache Spark) natively support temporal data query, and none of the previous work has developed memory-based distributed solutions to process temporal big data. Time Travel and Temporal Aggregation Operations

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  • Time Travel and Temporal Aggregation Query Processing Methods
  • Time Travel and Temporal Aggregation Query Processing Methods
  • Time Travel and Temporal Aggregation Query Processing Methods

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Embodiment Construction

[0080] The present invention is described in further detail now in conjunction with accompanying drawing. These drawings are all simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, so they only show the configurations related to the present invention.

[0081] 1. Problem definition

[0082] Specifically, the present invention attempts to implement two representative operations on temporal data (ie, time travel and temporal aggregation) in a distributed environment. However, the framework and algorithms we describe later can be easily extended to support other temporal operations (e.g., temporal joins) and other data (e.g., bitemporal data [R. Bliujute, C.S. Jensen, S. Saltenis, G. Slivinskas: R-tree based indexing of now-relative bittemporal data. In VLDB, 1998], dual-time data, that is, data records that contain both valid time (Valid time) and transaction time (Transaction time)). Next, we formally define...

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Abstract

The invention discloses a time travel and temporal aggregation query processing method, the method adopts a distributed processing frame system based on time travel and temporal aggregation query, and the method includes the following two stages: (i) global pruning stage and ( ii) local search stage; the global pruning stage utilizes the global index and query input to prune irrelevant partitions; the local search stage mainly retrieves qualified records in each candidate partition according to the local index and partial query input; Different indexes are used in the local lookup stage to support time travel and temporal aggregation queries; the time travel queries include time travel exact match queries and time travel range queries. The present invention adopts a distributed memory analysis framework, which is easy to understand and implement without losing efficiency. The method realizes time travel query and temporal aggregation query at the same time, can meet the requirements of high throughput and low delay, and can improve the efficiency of query efficiency and query speed.

Description

technical field [0001] The invention belongs to the field of computers, and in particular relates to a temporal data query method, in particular to a time travel and temporal aggregation query processing method. Background technique [0002] The management of temporal data has been studied for decades and has recently received increasing attention due to its wide range of applications [cf: M. Gupta, J. Gao, C. C. Aggarwal, J. Han: Outlier Detection for Temporal Data: A Survey.In TKDE,2014; F.Li,K.Yi,W.Le:Top-k queries on temporal data.InVLDBJ,2010]. For example, a user may wish to investigate demographic information for an administrative region (eg, California) at a certain time (eg, 5 years ago). Querying historical versions of a database (as described above) is often referred to as time travel [R.Elmasri, G.T.Wuu, and Y.J.Kim. The Time Index: An Access Structure for Temporal Data. InVLDB, 1990; B.Becker, S.Gschwind, T. Ohler, B. Seeger, B. Widmayer: An asymptotically opt...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06F16/2458
Inventor过敏意姚斌张伟沈耀李超郑文立
OwnerSHANGHAI JIAOTONG UNIV