Perancangan Arsitektur Business Intelligence Masa Depan Berbasis TurboQuant untuk Efisiensi Memori Big Data
Kata Kunci:
Business Intelligence, TurboQuant, Big Data, efisiensi memori, arsitektur data, Data Warehouse, pemrosesan real-time.Abstrak
Perkembangan Big Data yang semakin pesat menuntut hadirnya arsitektur Business Intelligence (BI) generasi baru yang mampu menangani volume data sangat besar dengan efisiensi memori tinggi. Sistem BI tradisional kerap menghadapi kendala serius berupa konsumsi memori berlebih serta keterbatasan kecepatan pemrosesan data berskala besar. Penelitian ini bertujuan merancang arsitektur Business Intelligence masa depan berbasis TurboQuant, yaitu kerangka arsitektural baru yang mengintegrasikan teknik kompresi data kuantitatif, pemrosesan paralel berlapis, serta manajemen memori adaptif untuk mengoptimalkan pengelolaan Big Data.
Metode penelitian mencakup perancangan arsitektur sistem TurboQuant secara konseptual, simulasi alur pemrosesan data pada berbagai skala volume Big Data, pengembangan model Data Warehouse modern berbasis skema multidimensi, serta analisis komparatif kinerja terhadap arsitektur BI konvensional. Hasil perancangan menunjukkan bahwa arsitektur TurboQuant mampu mengurangi konsumsi memori hingga 68% dibandingkan sistem BI tradisional, meningkatkan kecepatan kueri sebesar 4,3 kali lipat pada dataset bervolume tinggi, serta mendukung pemrosesan data real-time dan near real-time dengan latensi yang sangat rendah.
Arsitektur TurboQuant terbukti memberikan skalabilitas tinggi dan efisiensi penyimpanan yang signifikan, sehingga menjadikannya solusi yang relevan dan prospektif untuk kebutuhan Business Intelligence industri masa depan. Penelitian ini diharapkan dapat berkontribusi pada pengembangan sistem BI generasi berikutnya yang mampu menghadapi tantangan pengelolaan Big Data secara lebih efektif dan efisien.
Unduhan
Referensi
[1] R. Alam, “Journal of Advanced Technology and Engineering Solutions A Comparative Study of Machine Learning Models for Predictive,” no. March, pp. 545–582, 2026, doi: 10.63125/2hj6p796.
[2] C. Computing, B. Data, and E. Topics, “Acciones de colaboración en el Consorcio de Cloud Computing, Big Data y Emerging Topics durante 2024 (,” vol. 2024, pp. 595–602, 2024.
[3] Y. Elazar et al., W HAT ’ S I N M Y B IG D ATA ? 2024.
[4] G. Magarshak, “Sequential KV Cache Compression via Probabilistic Language Tries : Beyond the Per-Vector Shannon Limit”.
[5] T. Admissibility, I. Horizons, and O. O. Updates, “NZFC-KV : KV Cache Compression under Nuclear ZFC,” no. 1, 2026.
[6] A. Zandieh, M. Daliri, V. Mirrokni, M. Hadian, and G. Deepmind, “TurboQuant : Online Vector Quantization with Near-optimal”.
[7] P. D. Alberto, “Statistical Inference and Quality Measures of KV Cache Quantisations Inspired by TurboQuant,” no. 1.
[8] S. Ahmadi, “Optimizing Data Warehousing Performance through Machine Learning Algorithms in the Cloud To cite this version : HAL Id : hal-04456263 Optimizing Data Warehousing Performance through Machine Learning Algorithms in the Cloud,” 2024.
[9] R. Sunku, “AI-Powered Data Warehouse : Revolutionizing Cloud Storage Performance through Machine Learning Optimization,” vol. 1, pp. 1–6, 2023.
[10] Z. Zhou, C. Kennelly, P. Xia, and C. Delimitrou, “Characterizing a Memory Allocator at Warehouse Scale,” pp. 192–206.
[11] L. Dinesh and K. G. Devi, “An efficient hybrid optimization of ETL process in data warehouse of cloud architecture,” J. Cloud Comput., 2024, doi: 10.1186/s13677-023-00571-y.
[12] C. Anwar et al., “Optimizing the Performance of Data Warehouse by Query Cache Mechanism,” IEEE Access, vol. 10, pp. 13472–13480, 2022, doi: 10.1109/ACCESS.2022.3148131.
[13] H. Ann, J. Lee, and M. Oh, “Block-Sphere Vector Quantization”.
[14] J. D. Pope, “RotorQuant : Clifford Algebra Vector Quantization for LLM KV Cache Compression Background : Clifford Algebra Method : RotorQuant,” vol. 2, pp. 3–6, 2026.
[15] J. Gao et al., “Revisiting RaBitQ and TurboQuant : A Sym- metric Comparison of Methods , Theory , and Experiments,” vol. 2024, no. May, 2024.
[16] T. Verkor, T. Ravi, S. Krishna, and D. Chin, “Design Conductor 2 . 0 : An agent builds a TurboQuant inference accelerator in 80 hours,” pp. 1–12, 2026.
[17] Y. Liu, “TurboESM : Ultra-Efficient 3-Bit KV Cache Quantization for Protein Language Models with Orthogonal Rotation and QJL Correction,” pp. 1–16, 2026.
[18] R. Ben-basat, Y. Ben-itzhak, G. Mendelson, M. Mitzenmacher, A. Portnoy, and S. Vargaftik, “A Note on TurboQuant and the Earlier DRIVE / EDEN Line of Work,” pp. 1–10, 2026.
[19] S. Stumpf, V. Povyshev, and S. Petersburg, “O VERVIEW AND P ROSPECTS OF U SING I NTEGER S URROGATE K EYS FOR D ATA W AREHOUSE Sviatoslav Stumpf and Vladislav Povyshev,” pp. 181–192, 2025, doi: 10.5121/csit.2025.152213.
[20] S. Universit et al., “New approach to greedy vector quantization,” vol. 28, no. 1, pp. 424–452, 2022.
[21] X. Bin, Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever, vol. 1, no. 1. Association for Computing Machinery. doi: 10.1145/3711896.3737259.
[22] K. Fujita, “An efficient and straightforward online vector quantization method for a data stream through remove-birth updating,” 2024, doi: 10.7717/peerj-cs.1789.
[23] N. Bruno, C. Galindo-legaria, E. C. Vargas, S. Ravindran, and G. Chen, Unified Query Optimization in the Fabric Data Warehouse, vol. 1, no. 1. Association for Computing Machinery. doi: 10.1145/3626246.3653369.
[24] K. Network, “Analisis Data Penjualan Menggunakan Business Intelligence untuk Meningkatkan Profitabilitas pada UMKM,” no. X.
[25] H. Tian, S. Ali, S. Iqbal, S. Akhtar, S. Farhan, and S. Ali, “Data-driven disruptive competitiveness : Exploring the role of big data analytics capability and entrepreneurial marketing in disruptive innovation,” pp. 253–284, 2025.
[26] S. E. Lopez and S. Lopez, “Data Analytics Strategies for Growth and Sustainability Walden University This is to certify that the doctoral study by,” 2024.
[27] H. Panetto, Y. Naudet, B. Léger, D. Data, and M. Plan, “D0 . 3b Data Management Plan To cite this version : HAL Id : hal-05621242,” pp. 0–13, 2026.
[28] K. J. Merceedi, A. A. Yazdeen, A. K. Ibrahim, M. B. Abdulrazzaq, and M. R. Mahmood, “Analyses the Performance of Data Warehouse Architecture Types,” vol. 1, pp. 45–57, 2022.
[29] A. Dibouliya, “Review on : Modern Data Warehouse & how is it accelerating digital transformation,” vol. 9, no. 2, pp. 285–299.
[30] T. K. Ivanovski, S. Honap, R. Matic, S. Markovic, and L. Peyrin-biroulet, “Building a healthcare data warehouse : considerations , opportunities , and challenges,” no. December, pp. 1–15, 2025, doi: 10.3389/fdgth.2025.1691142.
[31] S. Jiang, W. Long, M. Han, Z. Chen, C. Zhu, and S. Gu, “Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression,” pp. 14440–14450.
[32] L. D. Lingle, “VIA V ECTOR Q UANTIZATION,” pp. 1–30, 2024.
[33] I. Han, “PolarQuant : Vector Quantization with Polar Transformation”.
[34] N. Alonso, T. Figliolia, and B. Millidge, “Online Vector Quantized Attention,” 2026.
[35] H. Wang, L. K. Mueller, J. Zhuang, M. Salzmann, and L. Cavigelli, “OffQ : Taming Structured Outliers in LLM Quantization by Offsetting,” pp. 1–17.
[36] M. Raeini, “Compression is All You Need,” pp. 1–7.
[37] B. Park, Y. Park, and C. Park, “PrismQuant : Rate – Distortion-Optimal Vector Quantization for Gaussian-Mixture Sources arXiv : 2605 . 15507v1 [ cs . IT ] 15 May 2026,” no. NeurIPS, pp. 1–19, 2026.
[38] K. Swain, S. Han, D. K. I. Weidele, M. Martino, D. Cox, and A. Torralba, “Hurwitz Quaternion Multiplicative Quantization for KV Cache Compression,” vol. 3, 2026.
[39] H. Zamil, M. Mohiuddin, and N. H. Mamun, “BUSINESS INTELLIGENCE SYSTEMS IN FINANCE AND ACCOUNTING : A REVIEW OF REAL-TIME DASHBOARDING USING POWER BI & TABLEAU,” vol. 03, no. 02, pp. 52–79, 2024, doi: 10.63125/fy4w7w04.
[40] P. Ravichandran, J. R. Machireddy, S. K. Rachakatla, L. Developer, and I. E. Holdings, “AI-Enhanced Data Analytics for Real-Time Business Intelligence : Applications and Challenges,” vol. 2, no. 2, pp. 168–195.
[41] I. Technology, S. S. Palli, I. Researcher, P. Issue, P. Number, and O. B. Intelligence, “Real-time Data Integration Architectures for Operational Business Intelligence in Global Enterprises,” pp. 361–371, 2023.
[42] S. H. Swarnali, “1 st Global Research and Innovation Conference 2025 , AI-Enhanced ERP Financial Intelligence Systems for Real-Time Business Analytics and Risk Decision Support,” no. April, pp. 2703–2741, 2025, doi: 10.63125/t1zw3w09.
[43] S. Ibtisum, S. M. A. Rahman, and S. M. S. Hossain, “Comparative analysis of MapReduce and Apache Tez Performance in Multinode clusters with data compression,” 2023.
[44] A. Hassan, S. Javed, S. Hussain, R. Ahmad, and S. Qazi, “Discover Computing Arithmetic N ‑ gram : an efficient data compression technique,” Discov. Comput., no. February, 2024, doi: 10.1007/s10791-024-09431-y.
[45] A. Abayomi, A. C. Uzoka, J. C. Ogeawuchi, A. Agboola, T. P. Gbenle, and O. E. Akpe, “Revolutionizing Business Intelligence Reporting : Advances in Cloud-Native Data Visualization Tools for Real-Time Insights,” vol. 3, no. 6, pp. 1582–1588, 2023.
[46] R. H. Chowdhury, “Blockchain and AI : Driving the future of data security and business intelligence,” 2025.
[47] R. F. Famutimi, M. O. Oyelami, A. O. Ibitoye, and O. M. Awoniran, “An empirical comparison of the performances of single structure columnar in ‑ memory and disk ‑ resident data storage techniques using healthcare big data,” J. Big Data, 2023, doi: 10.1186/s40537-023-00691-x.
[48] D. Martinez-mosquera, R. Navarrete, S. Luján-mora, L. Recalde, and A. Andrade-cabrera, “Integrating OLAP with NoSQL Databases in Big Data Environments : Systematic Mapping,” 2024.
[49] U. For, “AWQ : A CTIVATION - AWARE W EIGHT Q UANTIZATION FOR O N -D EVICE LLM C OMPRESSION AND A CCELERATION,” 2022.
[50] S. Executive, The Architecture of Leverage : Structural Concentration and Competitive Moats in the AI Compute Supply Chain, vol. 0. 2026.
[51] H. Herman, “BEYOND SCALE : CLQPI AND THE CASE FOR OPTIMIZATION IN LLMS COMPRESSION-DEPTH OPTIMIZATION IN LLMS,” no. March, 2026.
[52] I. Of, D. Warehouse, B. Intelligence, F. O. R. Monitoring, S. Academic, and P. In, “IMPLEMENTASI DATA WAREHOUSE DAN BUSINESS INTELLIGENCE UNTUK MONITORING KINERJA AKADEMIK IMPLEMENTATION OF DATA WAREHOUSE AND BUSINESS INTELLIGENCE FOR MONITORING STUDENT ACADEMIC PERFORMANCE IN HIGHER b ) Basis Data Nilai Fakultas : Berisi data nilai akhir mata kuliah dan status kelulusan . c ) File Excel Laporan Kehadiran Dosen : Mengandung data persentase kehadiran mahasiswa per mata kuliah .,” vol. 1, no. 2, pp. 557–565, 2025.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Imam Syafi'i (Penulis); Zainal Arif (Penerjemah)
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
