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This repository provides everything about skyline computation. If you have references, recent research results, or other references related to skyline computation, please submit them to this repository. Your contribution can be used to complement learning resources in studying skyline computation

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Awesome Skyline Computation

This repository is created as a learning resource for anyone who wants to learn and explore the topic of skyline computing. Learning resources in this repository we get randomly from anywhere. Happy learning and enjoy your learning journey.

Methods

  • Sort-Based Skyline Queries
    • Block-Nested Loop (BNL)
    • SFS
    • Linear Elimination Sort for Skyline (LESS)
    • SaLSa
    • to be continue
  • Index-Based Skyline Queries
    • BBS
    • Z-SKY
    • to be continue
  • Dominance Relation-Based Skyline Queries
    • Object-based Space Partitioning Skyline
    • BSkyTree
    • BJR-Tree
    • to be continue

Ebook

  • Ramakrishnan, Raghu, Johannes Gehrke, and Johannes Gehrke. Database management systems. Vol. 3. New York: McGraw-Hill, 2003.
  • Beynon-Davies, Paul. Database systems. Bloomsbury Publishing, 2017.
  • Silberschatz, Abraham, Henry F. Korth, and Shashank Sudarshan. Database system concepts. Vol. 5. New York: McGraw-Hill, 2002.
  • Russell, Stuart J. Artificial intelligence a modern approach. Pearson Education, Inc., 2010.
  • Samet, Hanan. Foundations of multidimensional and metric data structures. Morgan Kaufmann, 2006.
  • Ma, Zongmin, and Li Yan, eds. Advances in probabilistic databases for uncertain information management. Vol. 304. Springer, 2013.
  • Zaniolo, Carlo, et al. Advanced database systems. Morgan Kaufmann, 1997.
  • Hua, Ming, and Jian Pei. "Ranking Queries on Probabilistic Linkages." Ranking Queries on Uncertain Data. Springer, New York, NY, 2011. 151-184.
  • Aggarwal, Charu C. "Managing and Mining Uncertain Data, vol. 35." (2010).
  • Pinsky, Mark, and Samuel Karlin. An introduction to stochastic modeling. Academic press, 2010.
  • Ross, Sheldon M., et al. Stochastic processes. Vol. 2. New York: Wiley, 1996.
  • Erickson, Jeff. "Algorithms." (1999).
  • Cormen, Thomas H., et al. Introduction to algorithms. MIT press, 2022.
  • Goodrich, Michael T., Roberto Tamassia, and Michael H. Goldwasser. Data structures and algorithms in Python. John Wiley & Sons Ltd, 2013.
  • Donald, E. Knuth. "Sorting and searching." The Art of Computer Programming 3 (3rd ed.). Addison-Wesley 5 (1997).
  • Elmasri, R., et al. Fundamentals of Database Systems. Addison-Wesley, 2000.
  • Garcia-Molina, Hector, Jeffrey D. Ullman, and Jennifer Widom. Database system implementation. Vol. 672. Upper Saddle River: Prentice Hall, 2000.
  • Gao, Yunjun, and Qing Liu. Preference query analysis and optimization. Springer, 2017.
  • Thu Bui, Lam, and Sameer Alam, eds. Multi-Objective Optimization in Computational Intelligence: Theory and Practice: Theory and Practice. IGI global, 2008.
  • Manolopoulos, Yannis, Apostolos N. Papadopoulos, and Michael Gr Vassilakopoulos, eds. Spatial databases: technologies, techniques and trends. Igi Global, 2005.
  • Mark, de Berg, et al. Computational geometry algorithms and applications. Spinger, 2008.
  • Manolopoulos, Yannis, Alexandros Nanopoulos, and Eleni Tousidou. Advanced signature indexing for multimedia and Web applications. Vol. 27. Springer Science & Business Media, 2003.
  • Mueck, Thomas A., and Martin L. Polaschek. Index data structures in object-oriented databases. Vol. 7. Springer Science & Business Media, 2012.
  • Manolopoulos, Yannis, Yannis Theodoridis, and Vassilis Tsotras. Advanced database indexing. Vol. 17. Springer Science & Business Media, 2012.
  • Nayak, Sukanta. Fundamentals of Optimization Techniques with Algorithms. Academic Press, 2020.
  • Sharma, Vikrant, Vinod Kumar Jain, and Atul Kumar. An Introduction to Optimization Techniques. Chapman and Hall/CRC, 2021.
  • to be continue

Source Code and Algorithm

Lecture Notes

Video

Research Paper

  • Groz, B., Mallmann-Trenn, F., Mathieu, C., Verdugo, V. (2020). Skyline Computation with Noisy Comparisons. In: Gąsieniec, L., Klasing, R., Radzik, T. (eds) Combinatorial Algorithms. IWOCA 2020. Lecture Notes in Computer Science(), vol 12126. Springer, Cham. https://doi.org/10.1007/978-3-030-48966-3_22
  • Wang G, Xin J, Chen L, Liu Y. 2012. Energy-efficient reverse skyline query processing over wireless sensor networks. IEEE Trans Knowl Data Eng. 24(7):1259–1275. doi:10.1109/TKDE.2011.64
  • Zhu H, Li X, Liu Q, Xu Z. 2020. Top-k Dominating Queries on Skyline Groups. IEEE Trans Knowl Data Eng. 32(7):1431–1444. https://doi.org/10.1109/TKDE.2019.2904065.
  • Lee KCK, Lee WC, Zheng B, Li H, Tian Y. 2010. Z-SKY: An efficient skyline query processing framework based on Z-order. VLDB J. 19(3):333–362. https://doi.org/10.1007/s00778-009-0166-x.
  • Jiang T, Zhang B, Lin D, Gao Y, Li Q. 2015. Incremental evaluation of top-κ combinatorial metric skyline query. Knowledge-Based Syst. 74:89–105. doi:10.1016/j.knosys.2014.11.009. http://doi.org/10.1016/j.knosys.2014.11.009.
  • Miao X, Gao Y, Chen G, Zhang T. 2016. K-Dominant Skyline Queries on Incomplete Data. Inf Sci (Ny). 367–368:990–1011. https://doi.org/10.1016/j.ins.2016.07.034.
  • Kulkarni RD, Momin BF. 2016. Skyline computation for frequent queries in update intensive environment. J King Saud Univ - Comput Inf Sci. 28(4):447–456. https://doi.org/10.1016/j.jksuci.2015.04.003.
  • Kalyvas C, Maragoudakis M. 2019. Skyline and reverse skyline query processing in SpatialHadoop. Data Knowl Eng. 122(April):55–80. https://doi.org/10.1016/j.datak.2019.04.004.
  • Ihm SY, Lee KE, Nasridinov A, Heo JS, Park YH. 2014. Approximate convex skyline: A partitioned layer-based index for efficient processing top-k queries. Knowledge-Based Syst. 61:13–28. https://doi.org/10.1016/j.knosys.2014.01.022.
  • Gao Y, Liu Q, Zheng B, Mou L, Chen G, Li Q. 2015. On processing reverse k-skyband and ranked reverse skyline queries. Inf Sci (Ny). 293:11–34. https://doi.org/10.1016/j.ins.2014.08.052.
  • Dehaki GB, Ibrahim H, Udzir NI, Sidi F, Alwan AA. 2018. A framework for processing skyline queries for a group of mobile users. ACM Int Conf Proceeding Ser.:333–339. https://doi.org/10.1145/3282373.3282392.
  • Ding L, Zhang X, Zhang H, Liu L, Song B. 2021. CrowdSJ: Skyline-Join Query Processing of Incomplete Datasets with Crowdsourcing. IEEE Access. 9:73216–73229. https://doi.org/10.1109/ACCESS.2021.3079324.
  • Kuo A Te, Chen H, Tang L, Ku WS, Qin X. 2022. ProbSky: Efficient Computation of Probabilistic Skyline Queries over Distributed Data. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2022.3151740.
  • Lai CC, Akbar ZF, Liu CM. 2016. A cooperative method for processing range-skyline queries in mobile wireless sensor networks. ACM Int Conf Proceeding Ser.:1–8. https://doi.org/10.1145/3007818.3007820.
  • Yin B, Gu K, Wei X, Zhou S, Liu Y. 2018. A cost-efficient framework for finding prospective customers based on reverse skyline queries. Knowledge-Based Syst. 152:117–135. https://doi.org/10.1016/j.knosys.2018.04.011.
  • Bartolini I, Ciaccia P, Patella M. 2008. Efficient sort-based skyline evaluation. ACM Trans Database Syst. 33(4). https://doi.org/10.1145/1412331.1412343.
  • Wong RCW, Pei J, Fu AWC, Wang K. 2009. Online skyline analysis with dynamic preferences on nominal attributes. IEEE Trans Knowl Data Eng. 21(1):35–49. https://doi.org/10.1109/TKDE.2008.115.
  • Ding X, Jin H. 2012. Efficient and progressive algorithms for distributed skyline queries over uncertain data. IEEE Trans Knowl Data Eng. 24(8):1448–1462. https://doi.org/10.1109/TKDE.2011.77.
  • Wang Z, Xin J, Ding L, Ba J, Gao X. 2018. ρ-Dominant skyline computation on data stream. IEEE Access. 6:53201–53213. https://doi.org/10.1109/ACCESS.2018.2871254.
  • Papadias D, Tao Y, Fu G, Seeger B. 2005. Progressive skyline computation in database systems.
  • BALKE W, GUNTZER U. 2004. Multi-objective Query Processing for Database Systems. Proc 2004 VLDB Conf.:936–947. https://doi.org/10.1016/b978-012088469-8/50082-6.
  • Liu X, Yang DN, Ye M, Lee WC. 2013. U-skyline: A new skyline query for uncertain databases. IEEE Trans Knowl Data Eng. 25(4):945–960. https://doi.org/10.1109/TKDE.2012.33.
  • Wijayanto H, Wang W, Ku W-S, Chen A. 2020. LShape Partitioning: Parallel Skyline Query Processing using MapReduce. IEEE Trans Knowl Data Eng. XX(XX):1–1. https://doi.org/10.1109/tkde.2020.3021470.
  • Yu W, Liu J, Pei J, Xiong L, Chen X, Qin Z. 2020. Efficient Contour Computation of Group-Based Skyline. IEEE Trans Knowl Data Eng. 32(7):1317–1332. https://doi.org/10.1109/TKDE.2019.2905239.
  • Cai Z, Cui X, Su X, Guo L, Ding Z. 2020. Speed and Direction Aware Skyline Query for Moving Objects. IEEE Trans Intell Transp Syst. 23(4):3000–3011. https://doi.org/10.1109/tits.2020.3028152.
  • Hidayat A, Cheema MA, Lin X, Zhang W, Zhang Y. 2021. Continuous monitoring of moving skyline and top-k queries. VLDB J. https://doi.org/10.1007/s00778-021-00702-4.
  • Gulzar Y, Alwan AA, Ibrahim H, Turaev S, Wani S, Soomo AB, Hamid Y. 2021. IDSA: An Efficient Algorithm for Skyline Queries Computation on Dynamic and Incomplete Data with Changing States. IEEE Access. 9:57291–57310. https://doi.org/10.1109/ACCESS.2021.3072775.
  • Zeng Y, Chen G, Li Kenli, Zhou Y, Zhou X, Li Keqin. 2019. M-Skyline: Taking sunk cost and alternative recommendation in consideration for skyline query on uncertain data. Knowledge-Based Syst. 163:204–213. https://doi.org/10.1016/j.knosys.2018.08.024.
  • Park Y, Min JK, Shim K. 2015. Processing of probabilistic skyline queries using mapreduce. Proc VLDB Endow. 8(12):1406–1417. https://doi.org/10.14778/2824032.2824040.
  • Saad NHM, Ibrahim H, Sidi F, Yaakob R, Alwan AA. 2021. Efficient Skyline Computation on Uncertain Dimensions. IEEE Access. 9:96975–96994. https://doi.org/10.1109/ACCESS.2021.3094547.
  • Wang W, Zhang J, Sun M Te, Ku WS. 2019. A scalable spatial skyline evaluation system utilizing parallel independent region groups. VLDB J. 28(1):73–98. https://doi.org/10.1007/s00778-018-0519-4.
  • Hsueh YL, Lin CC, Chang CC. 2017. An Efficient Indexing Method for Skyline Computations with Partially Ordered Domains. IEEE Trans Knowl Data Eng. 29(5):963–976. https://doi.org/10.1109/TKDE.2017.2656906.
  • Zhang K, Gao H, Han X, Cai Z, Li J. 2020. Modeling and Computing Probabilistic Skyline on Incomplete Data. IEEE Trans Knowl Data Eng. 32(7):1405–1418. https://doi.org/10.1109/TKDE.2019.2904967.
  • Zhou X, Li Kenli, Zhou Y, Li Keqin. 2016. Adaptive Processing for Distributed Skyline Queries over Uncertain Data. IEEE Trans Knowl Data Eng. 28(2):371–384. https://doi.org/10.1109/TKDE.2015.2475764.
  • Saad NHM, Ibrahim H, Sidi F, Yaakob R, Alwan AA. 2021. Efficient Skyline Computation on Uncertain Dimensions. IEEE Access. 9:96975–96994. https://doi.org/10.1109/ACCESS.2021.3094547.
  • Liu X, Yang D-NDN, Ye M, Lee W-CWC. 2013. U-skyline: A new skyline query for uncertain databases. IEEE Trans Knowl Data Eng. 25(4):945–960. https://doi.org/10.1109/TKDE.2012.33.
  • Miao X, Gao Y, Zhou L, Wang W, Li Q. 2018. Optimizing Quality for Probabilistic Skyline Computation and Probabilistic Similarity Search. IEEE Trans Knowl Data Eng. 30(9):1741–1755. https://doi.org/10.1109/TKDE.2018.2805824.
  • Tang M, Yu Y, Aref WG, Malluhi QM, Ouzzani M. 2018. Efficient Parallel Skyline Query Processing for High-Dimensional Data. IEEE Trans Knowl Data Eng. 30(10):1838–1851. https://doi.org/10.1109/TKDE.2018.2809598.
  • to be continue

Dataset

FYI

If you have articles, source code or other reference sources, please submit them to this repository via the pull request menu.

Acknowledgement

Thanks for contribution:

  • triawan
  • to be continue

Disclaimer

Feel free to use all resource in this repository.

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This repository provides everything about skyline computation. If you have references, recent research results, or other references related to skyline computation, please submit them to this repository. Your contribution can be used to complement learning resources in studying skyline computation

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