Querying Temporal Databases via OWL 2 QL

2014 ◽  
pp. 92-107 ◽  
Author(s):  
Szymon Klarman ◽  
Thomas Meyer
Keyword(s):  
Temporal Databases

scholarly journals Cache-efficient sweeping-based interval joins for extended Allen relation predicates

2021 ◽  
Author(s):  
Danila Piatov ◽  
Sven Helmer ◽  
Anton Dignös ◽  
Fabio Persia
Keyword(s):  
Data Structure ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Temporal Databases ◽  
Access Method ◽  
Wide Range ◽  
Interval Relation ◽  
Cache Efficient ◽  
Join Algorithms ◽  
Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

Main Memory-Based Algorithms for Efficient Parallel Aggregation for Temporal Databases

2004 ◽  
Vol 16 (2) ◽  
pp. 123-163 ◽  
Author(s):  
Dengfeng Gao ◽  
Jose Alvin G. Gendrano ◽  
Bongki Moon ◽  
Richard T. Snodgrass ◽  
Minseok Park ◽  
...  
Keyword(s):  
Main Memory ◽  
Temporal Databases

Resolution of time concepts in temporal databases

1994 ◽  
Vol 80 (1-2) ◽  
pp. 91-125 ◽  
Author(s):  
Sharma Chakravarthy ◽  
Seung-Kyum Kim
Keyword(s):  
Temporal Databases ◽  
Time Concepts

scholarly journals Partitioned storage for temporal databases

1988 ◽  
Vol 13 (4) ◽  
pp. 369-391 ◽  
Author(s):  
Ilsoo Ahn ◽  
Richard Snodgrass
Keyword(s):  
Temporal Databases

scholarly journals Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1478
Author(s):  
Penugonda Ravikumar ◽  
Palla Likhitha ◽  
Bathala Venus Vikranth Raj ◽  
Rage Uday Kiran ◽  
Yutaka Watanobe ◽  
...  
Keyword(s):  
Real World ◽  
Pattern Mining ◽  
Transportation Network ◽  
Frequent Pattern Mining ◽  
Temporal Databases ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Temporal Database ◽  
First Case

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

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