Memory-Efficient Storing of Timestamps for Spatio-Temporal Data Management in Columnar In-Memory Databases

With the development of RFID technology, we can identify, locate, track and monitor items in supply chain, retail store, and asset management applications. RFID has become a key technology in the Internet of Things. But RFID data can’t be effectively managed by only using traditional data model because they have their own unique characteristics, such as aggregation, location, temporal and history-oriented, which have to be fully considered and integrated into the data model. Therefore, the architecture of RFID spatio-temporal data management is proposed in this paper. We provide a brief overview of RFID technology and highlight a few of the spatio-temporal data management challenges, such as RFID middleware, RFID event processing, In-memory cache.

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Spatio-Temporal Data Management for Moving Objects

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.123.1155 ◽

2003 ◽

Vol 123 (6) ◽

pp. 1155-1165

Author(s):

Yiqun Wang ◽

Hiroshi Nozawa ◽

Yoshinori Hijikata ◽

Mie Nakatani ◽

Shogo Nishida

Keyword(s):

Data Management ◽

Moving Objects ◽

Temporal Data ◽

Spatio Temporal

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A Survey on Big Data Processing Frameworks for Mobility Analytics

ACM SIGMOD Record ◽

10.1145/3484622.3484626 ◽

2021 ◽

Vol 50 (2) ◽

pp. 18-29

Author(s):

Christos Doulkeridis ◽

Akrivi Vlachou ◽

Nikos Pelekis ◽

Yannis Theodoridis

Keyword(s):

Big Data ◽

Data Processing ◽

Data Management ◽

Spatial Data ◽

State Of The Art ◽

Temporal Data ◽

Big Data Processing ◽

Mobility Data ◽

New Challenges ◽

Spatio Temporal

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

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