Browsing by Author "Kazar, Okba"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Comparing NoSQL Databases with YCSB Standard Benchmark
    (University of Eloued جامعة الوادي, 2022-01-24) Ben Seghier, Nadia; Kazar, Okba
    NoSQL data-stores are commonly used to provide flexibility and availability for Big Data handling. The important companies in the IT sector find these NoSQL systems, new solutions to respond to scalability needs. These databases can be broadly classified as key value stores, column based, document stores and graph database depending on their mechanism of data storage and other features. NoSQL databases assert that their performance is better than legacy Relational Database systems for higher workloads, particularly common in Big Data and Cloud Computing applications. Multiple open-source and proprietary models of NoSQL are available on the market. Because of the large number and diversity of existing solutions, it is difficult to select an appropriate solution for a specific problem. In this paper, we develop a comparative study about the performance of three solutions widely employed: Redis 3.0.504, MongoDB 4.4.0 and Cassandra 3.11.1, and tests the runtime for different proportions of read, update, scan, readmodify- write and insert operations using six workloads by YCSB 0.17.0 tool on Windows OS. The purpose of our comparative study is to provide assistance and support to actors interested of Big Data and Cloud Computing for eventual decisions for the choice of solutions to be adopted.
  • No Thumbnail Available
    Item
    Deep learning for seismic data semantic segmentation
    (University of Eloued جامعة الوادي, 2022-01-24) Naoui, Mohammed Anouar; Lejdel, Brahim; Kazar, Okba; Berrehouma, Ridha
    Drilling for oil and gas is an expensive and time-consuming process. Companies in the oil and gas industry invest millions of dollars in an effort to improve their understanding of subsurface components, and using traditional workflows for interpreting large volumes of seismic data is an important part of this effort. Manually defining links between geological characteristics and seismic patterns is required by geoscientists. As a result, geologists and oil and gas industry businesses resorted to a seismic survey, in which seismic waves provide a wealth of information about what is inside the earth without the need to dig. The main of this paper concerns the identification of salt layers of a seismic image by a computer which often coexist with gas and oil under the ground by proposing a deep Learning for seismic analysis.We propose U-net architecture to discover seismic data. Moreover, we study the data augmentation with U-net architecture. The result of data augmentation can perform 10 % the U-net architecture model.