Sequence Data Indexing Method Based on Minimum DTW Distance

2012 ◽  
Author(s):  
Kijeong Khil ◽  
Seokil Song
Keyword(s):  
Sequence Data ◽  
Data Indexing ◽  
Indexing Method

scholarly journals Sequence Data Indexing Method based on Minimum DTW Distance

2011 ◽  
Vol 11 (12) ◽  
pp. 52-59
Author(s):  
Ki-Jeong Khil ◽  
Seok-Il Song ◽  
Chai-Jong Song ◽  
Seok-Pil Lee ◽  
Sei-Jin Jang ◽  
...  
Keyword(s):  
Sequence Data ◽  
Data Indexing ◽  
Indexing Method

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

Sensors ◽  
2014 ◽  
Vol 14 (7) ◽  
pp. 12990-13005 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Temporal Data ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

2021 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
Hybrid Index ◽  
Temporal Data ◽  
Trajectory Data ◽  
Generation Efficiency ◽  
Tree Structures ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

scholarly journals The efficient implementation of distributed indexing with Hadoop for digital investigations on Big Data

2014 ◽  
Vol 11 (3) ◽  
pp. 1037-1054 ◽  
Author(s):  
Taerim Lee ◽  
Hyejoo Lee ◽  
Kyung-Hyune Rhee ◽  
Uk Shin
Keyword(s):  
Big Data ◽  
Text Processing ◽  
Processing System ◽  
Cloud Service ◽  
Data Indexing ◽  
Hadoop Mapreduce ◽  
Relevant Evidence ◽  
Indexing Method ◽  
Distributed Indexing ◽  
Digital Investigation

Big Data brings new challenges to the field of e-Discovery or digital forensics and these challenges are mostly connected to the various methods for data processing. Considering that the most important factors are time and cost in determining success or failure of digital investigation, the development of a valid indexing method for efficient search should come first to more quickly and accurately find relevant evidence from Big Data. This paper, therefore, introduces a Distributed Text Processing System based on Hadoop called DTPS and explains about the distinctions between DTPS and other related researches to emphasize the necessity of it. In addition, this paper describes various experimental results in order to find the best implementation strategy in using Hadoop MapReduce for the distributed indexing and to analyze the worth for practical use of DTPS by comparative evaluation of its performance with similar tools. To be short, the ultimate purpose of this research is the development of useful search engine specially aimed at Big Data indexing as a major part for the future e-Discovery cloud service.

Key Technology in a Lightweight WPF-Based Electronic Map Engine

2014 ◽  
Vol 678 ◽  
pp. 70-74 ◽  
Author(s):  
Lei Peng
Keyword(s):  
Spatial Data ◽  
Data Organization ◽  
Design And Development ◽  
Electronic Map ◽  
Key Technology ◽  
Data Indexing ◽  
Organization And Management ◽  
Indexing Method ◽  
Engine Components

This paper introduces key technology in design and development of a lightweight WPF-Based electronic map engine, systematically analyzes characteristics of WPF technology, designs the structure of electronic map engine components, elaborates on data organization and management, vector and raster spatial data indexing method, presents a electronic map engine specific for nautical map display style.

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

2021 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
Hybrid Index ◽  
Temporal Data ◽  
Trajectory Data ◽  
Generation Efficiency ◽  
Tree Structures ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

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