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  1. 1

    Optimizing skyline query processing in incomplete data by Gulzar, Yonis, Alwan, Ali Amer, Turaev, Sherzod

    Published 2019
    “…Hence, missing data pose new challenges if the processing skyline queries cannot easily apply those methods that are designed for complete data. …”
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    Article
  2. 2

    Processing skyline queries in incomplete database: issues, challenges and future trends by Gulzar, Yonis, Aljuboori, Ali A.Alwan, Salleh, Norsaremah, Alshaikhli, Imad Fakhri Taha

    Published 2017
    “…In many contemporary database applications such as multi-criteria decision-making and real-time decision-support applications, data mining, ecommerce and recommendation systems, users need query operators to process the data aiming at finding the results that best fit with their preferences. …”
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  3. 3

    A systematic literature review of skyline query processing over data stream by Mohamud, Mudathir Ahmed, Ibrahim, Hamidah, Sidi, Fatimah, Rum, Siti Nurulain Mohd, Dzolkhifli, Zarina Binti, Xiaowei, Zhang, Lawal, Ma’aruf Mohammed

    Published 2023
    “…Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. …”
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  4. 4

    D-SKY: a framework for processing skyline queries in a dynamic and incomplete database by Gulzar, Yonis, Alwan, Ali Amer, Ibrahim, Hamidah, Xin, Qin

    Published 2018
    “…Processing skyline queries in incomplete data is challenging, particularly, for a database with dynamic contents in which the database is frequently updated. …”
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    Proceeding Paper
  5. 5

    A systematic literature review of skyline query processing over data stream by Mudathir Ahmed, Mohamud, Hamidah D., Ibrahim, Fatimah, Sidi, Siti Nurulain, Mohd Rum, Zarina, Dzolkhifli, Xiaowei, Zhang, Lawal, Ma'aruf Mohammed

    Published 2023
    “…Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. …”
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  6. 6

    GNN-based skyline query processing for large-scale and incomplete graphs by Adzman, Hasan Khair, Hassan, Raini, Dwi Handayani, Dini Oktarina

    Published 2026
    “…This research demonstrates the potential to improve query processing efficiency and to support applications in e-commerce, finance, and smart data systems…”
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  7. 7

    Processing skyline queries in centralised and distributed incomplete databases by Alwan, Ali Amer

    Published 2013
    “…Several experiments on synthetic and real datasets have been conducted. The results showed that our proposed approach for processing skyline queries in incomplete database has reduced the number of pairwise comparisons in the range of 75%-93% and the processing time in the range of 50%-89% compared to the previous approach. …”
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    Thesis
  8. 8

    Efficient skyline query processing in incomplete graph databases using machine learning techniques by Noor, Ubair, Hassan, Raini, Dwi Handayani, Dini Oktarina

    Published 2025
    “…Processing skyline queries in such massive, incomplete graphs is computationally intensive due to missing values and high-dimensional data. …”
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  9. 9

    Analyzing Power Consumption in Query Processing for Centralized Database System by Mohd Bohari, Muhammad Nasrulhaq

    Published 2012
    “…In supporting to green computing, this project is being carried out with the main purpose to analyze the efficient performance of query processing activity in consuming power from a centralized database by implementing different query strategies in data grouping. …”
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    Final Year Project
  10. 10

    Efficient management of Top-k queries over Uncertain Data Streams with dynamic Sliding Window Model by Raja Wahab, Raja Azhan Syah

    Published 2024
    “…The Uncertain Data Stream (UDS) utilized for query processing can provide challenges in many technical contexts owing to its inherent inconsistency, ambiguity, and time delay in interpreting information. …”
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    Thesis
  11. 11

    Skyline query processing for incomplete data in cloud environment by Gulzar, Yonis, Aljuboori, Ali A.Alwan, Salleh, Norsaremah, Alshaikhli, Imad Fakhri Taha

    Published 2017
    “…An approach is needed taking into considera-tion the unique features of cloud environment when processing skyline que-ries on a database with incomplete data. …”
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    Proceeding Paper
  12. 12

    Skyline queries computation on crowdsourced- enabled incomplete database by Swidan, Marwa B., Alwan, Ali A., Turaev, Sherzod, Ibrahim, Hamidah, Abualkishik, Abedallah Zaid, Gulzar, Yonis

    Published 2020
    “…Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. …”
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  13. 13

    Enhancing skyline query processing on large and incomplete graphs with graph neural networks: a hybrid machine learning approach by Adzman, Hasan Khair, Hassan, Raini, Handayani, Dini Oktarina Dwi

    Published 2025
    “…This work demonstrates the potential of creating a more efficient query processing, supporting applications in e-commerce, finance, and smart data systems, while aligning with the 9th Sustainable Development Goal on industry, innovation, and infrastructure.…”
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  14. 14

    Optimizing B-tree search performance of big data sets / Mohsen Marjani by Mohsen , Marjani

    Published 2017
    “…This research contributes in optimizing data retrieval for big data sets and gives direction to researchers towards a novel approach of indexing and searching big data in order to improve query processing and search performance.…”
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    Thesis
  15. 15

    Skyline queries computation on crowdsourced- enabled incomplete database by Swidan, Marwa, Aljuboori, Ali A.Alwan, Turaev, Sherzod, Ibrahim, Hamidah, Zaid Abualkishik, Abedallah, Gulzar, Yonis

    Published 2020
    “…Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. …”
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    Article
  16. 16

    IDSA: an efficient algorithm for skyline queries computation on dynamic and incomplete data with changing states by Gulzar, Yonis, Alwan, Ali Amer, Ibrahim, Hamidah, Turaev, Sherzod, Wani, Sharyar, Soomro, Arjumand Bano, Hamid, Yasir

    Published 2021
    “…Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. …”
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  17. 17

    IDSA: an efficient algorithm for skyline queries computation on dynamic and incomplete data with changing states by Gulzar, Yonis, Alwan, Ali Amer, Ibrahim, Hamidah, Turaev, Sherzod, Wani, Sharyar, Soomo, Arjumand Bano, Hamid, Yasir

    Published 2021
    “…Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. …”
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  18. 18

    CSQUiD: an index and non-probability framework for constrained skyline query processing over uncertain data by Lawal, Ma'aruf Mohammed, Ibrahim, Hamidah, Mohd Sani, Nor Fazlida, Yaakob, Razali, Alwan, Ali A.

    Published 2024
    “…Therefore, this work introduces an index, non-probability framework named Constrained Skyline Query processing on Uncertain Data (CSQUiD) aiming at reducing the computational time in processing constrained skyline queries over uncertain high-dimensional data. …”
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  19. 19

    Identifying skylines in cloud databases with incomplete data by Gulzar, Yonis, Aljuboori, Ali Amer Alwan, Salleh, Norsaremah, Al Shaikhli, Imad Fakhri

    Published 2019
    “…Thus, this paper introduces a new approach called Incomplete-data Cloud Skylines (ICS) aiming at processing skyline queries in cloud databases with incomplete data. …”
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  20. 20

    Building textual OLAP cubes using real-time intelligent heterogeneous approach by Alzeini, Haytham I. M., Hameed, Shihab A., Habaebi, Mohamed Hadi

    Published 2018
    “…In this context, the approach (a) exploits the GPU along with the CPU in order to process textual data. (b) Stores the queries aggregations’ hash table in the global memory such that the higher aggregations levels are being answered in a shorter time (c) Introduces an intelligent self-evaluating mechanism (ISEM), that evaluates the resource efficiency on query-basis by deciding which resource (CPU or GPU+CPU) is more reliable to process each query. …”
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