scholarly journals Valid-Time Indeterminacy in Temporal Relational Databases: Semantics and Representations

2013 ◽  
Vol 25 (12) ◽  
pp. 2880-2894 ◽  
Author(s):  
Luca Anselma ◽  
Paolo Terenziani ◽  
Richard T. Snodgrass
Keyword(s):  
Relational Databases ◽  
Valid Time

scholarly journals Analisis Basis Data Relasional dan Basis Data Temporal

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Juanda Hakim Lubis
Keyword(s):  
Relational Database ◽  
Real World ◽  
Relational Databases ◽  
Historical Data ◽  
Temporal Databases ◽  
Temporal Database ◽  
Time Dimension ◽  
The Past ◽  
Valid Time ◽  
Require Data

Abstract. At present, many applications require data from the past and the future. These data are usually used to trace the events that happened to look at trends and find the mistakes in the past so as to prevent the occurrence of the same mistakes. Temporal database is one of the solutions to handling data in the past and future. Temporal database is a database with data representing the valid time dimension. The use of this valid time shows aspects of the historical data because the data will be recorded in accordance with real-world from the beginning until the end of the validity of the data. This research implements temporal databases and relational databases that take the historical aspects of the data into consideration in order to measure the effectiveness of each database. Keywords: Temporal Database, relational database, valid time, historical data, response time. Abstrak. Pada saat ini banyak aplikasi yang membutuhkan data dari masa lampau dan data pada masa yang akan datang. Data-data ini biasanya digunakan untuk menelusuri event-event yang terjadi untuk melihat trend dan menemukan kesalahan-kesalahan di masa lampau sehingga mencegah terjadinya kesalahan yang sama. Temporal Database merupakan salah satu solusi dalam penanganan data-data di masa lampau maupun di masa yang akan datang. Temporal database adalah database yang merepresentasikan data dengan dimensi waktu berupa valid time. Penggunaan valid time ini dapat memperlihatkan aspek historical data karena suatu data akan dicatat sesuai dengan waktu real world baik dari dimulai sampai akhir keberlakuan data. Penelitian ini, melakukan analisis temporal database serta relational database yang memperhitungkan aspek historical data untuk mengukur keefektifan penggunaan masing-masing basis data. Kata kunci: Temporal Database, relational database, valid time, historical data, response time.

XML Modeling of Fuzzy Data with Relational Databases

2011 ◽  
Vol 34 (2) ◽  
pp. 291-303 ◽  
Author(s):  
Li YAN ◽  
Zong-Min MA ◽  
Jian LIU ◽  
Fu ZHANG
Keyword(s):  
Relational Databases ◽  
Fuzzy Data

scholarly journals Bloom Filter-Based Parallel Architecture for Accelerating Equi-Join Operation on FPGA

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1778
Author(s):  
Binhao He ◽  
Meiting Xue ◽  
Shubiao Liu ◽  
Wei Luo
Keyword(s):  
Relational Databases ◽  
Parallel Architecture ◽  
Operation Time ◽  
Bloom Filter ◽  
Search Tree ◽  
Binary Search Tree ◽  
Maximum Acceleration ◽  
Data Intensive ◽  
Match Rate ◽  
Field Programmable

As one of the most important operations in relational databases, the join is data-intensive and time-consuming. Thus, offloading this operation using field-programmable gate arrays (FPGAs) has attracted much interest and has been broadly researched in recent years. However, the available SRAM-based join architectures are often resource-intensive, power-consuming, or low-throughput. Besides, a lower match rate does not lead to a shorter operation time. To address these issues, a Bloom filter (BF)-based parallel join architecture is presented in this paper. This architecture first leverages the BF to discard the tuples that are not in the join result and classifies the remaining tuples into different channels. Second, a binary search tree is used to reduce the number of comparisons. The proposed method was implemented on a Xilinx FPGA, and the experimental results show that under a match rate of 50%, our architecture achieved a high join throughput of 145.8 million tuples per second and a maximum acceleration factor of 2.3 compared to the existing SRAM-based join architectures.

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