# Retrieval Verification: The_Hybrid_Multimodal_Graph_Index_HMGI_A_ComprehensiveFramework_for_Integrated_Relational_and_Vector_Search_parsed

## Pilar: Background
**Query:** _Latar belakang, masalah, dan motivasi penelitian ini. Background, research problem, motivation, context, introduction, underlying issue._

### Rank 1 (Score: 0.3333)
**Header:** **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
Joydeep Chandra* Satyam Kumar Navneet[∗] BNRIST, Tsinghua University Independent Beijing, China Bihar, India joydeepc2002@gmail.com navneetsatyamkumar@gmail.com  
Yong Zhang BNRIST, Tsinghua University Beijing, China zhangyong05@tsinghua.edu.cn
```

### Rank 2 (Score: 0.2248)
**Header:** **References** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
- [49] Lauren Morley. Building high-throughput data ingestion pipelines with kafka on openmetal. _OpenMetal Blog_ , Jul 2025.  
- [50] Satyam Kumar Navneet and Joydeep Chandra. Rethinking autonomy: Preventing failures in ai-driven software engineering, 2025.  
- [51] Pinecone Systems, Inc. Pinecone. https://www.pinecone.io/, 2021. Accessed: 2024-01-01.  
- [52] Beth Plale, Sai Navya Jyesta, and Sachith Withana. Vector embedding of multimodal texts: a tool for discovery?, 2025.  
- [53] Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan P FisherHoch, and Joseph B Mccormick. A generative framework for predictive modeling of multiple chronic conditions using graph variational autoencoder and bandit-optimized graph neural network. _IEEE Journal of Biomedical and Health Informatics_ , page 1–14, 2025.  
- [54] Bhaskarjit Sarmah, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, and Dhagash Mehta. Hybridrag: Integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction, 2024.  
- [55] Gaurav Sehgal and Semih Salihoglu. Navix: A native vector index design for graph dbmss with robust predicate-agnostic search performance, 2025.  
- [56] Xinyu Su, Feng Liu, Yanchuan Chang, Egemen Tanin, Majid Sarvi, and Jianzhong Qi. Dualcast: A model to disentangle aperiodic events from traffic series, 2024.
```

### Rank 3 (Score: 0.2057)
**Header:** **ACM Reference Format:** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
- (1) **RQ1: How can graph databases be augmented with native vector indexing to support seamless hybrid queries on multimodal data while maintaining sub-linear time complexity for both relational traversals and semantic similarity searches?**  
- (2) **RQ2: What partitioning and update strategies optimize query performance and resource utilization in hybrid indexes for high-dimensional, modality-heterogeneous embeddings at billion-scale?**  
- (3) **RQ3: To what extent do adaptive fusion mechanisms in hybrid systems improve accuracy and latency on relationship-intensive workloads compared to decoupled vector-graph architectures?**  
Our contributions are as follows:  
- (1) **Contribution 1: The HMGI Framework for Unified Hybrid Search.** We propose HMGI as a comprehensive blueprint that integrates vector similarity (ANNS) with graph traversal in a single system, enabling hybrid queries on multimodal data. Unlike decoupled approaches, HMGI leverages Neo4j’s native vector capabilities for end-to-end processing, achieving 3x QPS on relational-semantic workloads [42].  
- (2) **Contribution 2: Modality-Aware Partitioning and Dynamic Indexing Techniques.** HMGI introduces K-meansbased partitioning tailored to multimodal embeddings, reducing search spaces by up to 70% and supporting flash quantization for 50% memory savings. This innovation outperforms monolithic indexes in filtered queries, with evaluations on billion-scale datasets [60].
```

## Pilar: Methodology
**Query:** _Metode, pendekatan, arsitektur, algoritma, eksperimen, atau dataset yang digunakan. Methodology, proposed method, approach, system architecture, dataset, materials, experimental setup, procedure._

### Rank 1 (Score: 0.4235)
**Header:** **3 Methodology** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
In this section, we detail the methodology underpinning the design and implementation of the Hybrid Multimodal Graph Index (HMGI) system. Inspired by contemporary advancements in hybrid database systems [42, 55], HMGI seamlessly integrates graph traversal capabilities with vector similarity search to handle complex multimodal datasets efficiently. The methodology is structured around key components: architectural blueprint, data ingestion and processing, storage and indexing, query processing, adaptive updates, and learned optimizations. The visual representation of the HMGI system and its core components can be seen in Fig. 1.  
Each subsection provides a detailed exposition, bolstered by theoretical underpinnings, mathematical formulations, and empirical rationales to ensure scalability, robustness, and high performance [31, 67].
```

### Rank 2 (Score: 0.4126)
**Header:** **2.3 Graph Neural Network based Dataspaces** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
full index recreation [48]. For hybrid search, dynamic edge navigation from DEG was suggested to allow runtime weighting of semantic versus relational components, enabling flexible query adaptation and improving accuracy in polysemous cases by 10– 15% [67]. Frameworks should prioritize native designs like NaviX for predicate-agnostic operations, augmented with learned index structures to auto-tune HNSW parameters based on query patterns, reducing manual overhead in large deployments [55]. Hardwareaccelerated traversals, offloading ANNS to GPUs while retaining CPU for graph logic, were proposed to achieve millisecond latencies in real-time RAG applications, bridging gaps in current CPU-bound systems [60]. These recommendations formed a cohesive blueprint for next-generation hybrid systems, emphasizing unification of vector and graph paradigms to address the evolving demands of multimodal data management [14].
```

### Rank 3 (Score: 0.3951)
**Header:** **References** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
- [28] Alon Halevy, Michael Franklin, and David Maier. Principles of dataspace systems. In _Proceedings of the Twenty-Fifth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems_ , PODS ’06, page 1–9, New York, NY, USA, 2006. Association for Computing Machinery.  
- [29] Roman Heinrich, Xiao Li, Manisha Luthra, and Zoi Kaoudi. Learned cost models for query optimization: From batch to streaming systems. _Proc. VLDB Endow._ , 18(12):5482–5487, September 2025.  
- [30] Joseph M. Hellerstein. Looking back at postgres, 2019.  
- [31] Guimin Hu, Yi Xin, Lijie Hu, Zhihong Zhu, and Hasti Seifi. Partitioner guided modal learning framework, 2025.  
- [32] Guoyu Hu, Shaofeng Cai, Tien Tuan Anh Dinh, Zhongle Xie, Cong Yue, Gang Chen, and Beng Chin Ooi. Hakes: Scalable vector database for embedding search service, 2025.  
- [33] iCORER. The vector database landscape: An in-depth analysis of architectures, technologies, and strategic applications. Blog Post, 2025.  
- [34] S. Jin, M. Yuan, and Y. Song. Mysql data analysis and its application in e-commerce user behavior analysis. In _5th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM 2023)_ , volume 2023, pages 370–375, 2023.  
- [35] Zhi Jing, Yongye Su, Yikun Han, Bo Yuan, Haiyun Xu, Chunjiang Liu, Kehai Chen, and Min Zhang. When large language models meet vector databases: A survey, 2024.
```

## Pilar: Gap
**Query:** _Gap penelitian, keterbatasan, kelemahan, atau masalah yang belum terselesaikan. Research gap, limitations, weakness, unresolved problem, remains a challenge, yet to be explored, however._

### Rank 1 (Score: 0.4402)
**Header:** **3.2 Data Ingestion and Processing Pipeline** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
K
Cluster Assignment = arg min
Ile -μcll2,
(1)
C=1  
where _𝐾_ (default 2) centroids _𝝁𝑐_ ∈ R _[𝑑]_ are fitted on sampled embeddings, reducing search spaces by up to 70% for modalityspecific queries [31]. As shown in Equation (1), e ∈ R _[𝑑]_ represents the embedding vector of a data point in a _𝑑_ -dimensional space, and ∥e − _𝝁𝑐_ ∥[2] denotes the squared Euclidean norm, measuring the distance between the embedding and each centroid. Partition  
The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector SearchConference Name ’25, October 11–13, 2025, City, Country  
**Table 2: Gap Analysis: Comparison with Prior Works and Surveys**  
|Work|Multimodal Support|Vector-Graph Integration|Modality-Aware Partitioning|Adaptive Updates|Learned Optimizations|
|---|---|---|---|---|---|
|IDS Framework<br>TigerVector<br>NaviX<br>HybridRAG<br>Neo4j Vector Support<br>Milvus[59]<br>HMGI(Ours)|✓<br>Partial<br>Partial<br>Partial<br>Partial<br>✓<br>✓|×<br>✓<br>✓<br>✓<br>✓<br>×<br>✓|×<br>Partial<br>✓<br>×<br>Partial<br>✓<br>✓|Partial<br>✓<br>Partial<br>×<br>Partial<br>✓<br>✓|Partial<br>Partial<br>×<br>×<br>×<br>Partial<br>✓|
```

### Rank 2 (Score: 0.3198)
**Header:** **References** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
- [69] Song Yu, Shengyuan Lin, Shufeng Gong, Yongqing Xie, Ruicheng Liu, Yijie Zhou, Ji Sun, Yanfeng Zhang, Guoliang Li, and Ge Yu. A topology-aware localized update strategy for graph-based ann index, 2025.  
- [70] Osama Zafar, Rosemarie Santa González, Mina Namazi, Alfonso Morales, and Erman Ayday. Empowering digital agriculture: A privacy-preserving framework for data sharing and collaborative research, 2025.  
- [71] Ximu Zeng, Liwei Deng, Penghao Chen, Xu Chen, Han Su, and Kai Zheng. Lira: A learning-based query-aware partition framework for large-scale ann search. In _Proceedings of the ACM on Web Conference 2025_ , WWW ’25, page 2729–2741, New York, NY, USA, 2025. Association for Computing Machinery.  
- [72] Fuheng Zhao, Divyakant Agrawal, and Amr El Abbadi. Hybrid querying over relational databases and large language models, 2024.  
- [73] Kangfei Zhao and Jeffrey Xu Yu. All-in-one: Graph processing in rdbmss revisited. In _Proceedings of the 2017 ACM International Conference on Management of Data_ , SIGMOD ’17, page 1165–1180, New York, NY, USA, 2017. Association for Computing Machinery.  
- [74] Shengda Zhuo, Jiwang Fang, Hongguang Lin, Yin Tang, Min Chen, Changdong Wang, and Shuqiang Huang. Edgegfl: Rethinking edge information in graph feature preference learning, 2025.  
- [49] Lauren Morley. Building high-throughput data ingestion pipelines with kafka on openmetal. _OpenMetal Blog_ , Jul 2025.
```

### Rank 3 (Score: 0.3017)
**Header:** **References** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
- [8] Santosh Bhupathi. Role of databases in genai applications, 2025.  
- [9] Angela Bonifati, Stefania Dumbrava, Haridimos Kondylakis, Georgia Troullinou, and Giannis Vassiliou. Progressive querying on knowledge graphs. In _EDBT 2025 : International Conference on Extending Database Technology_ , Barcelona, France, March 2025.  
- [10] Linyue Cai, Chaojia Yu, Yongqi Kang, Yu Fu, Heng Zhang, and Yong Zhao. Practices, opportunities and challenges in the fusion of knowledge graphs and large language models. _Frontiers in Computer Science_ , Volume 7 - 2025, 2025.  
- [11] Joydeep Chandra, Aleksandr Algazinov, Satyam Kumar Navneet, Rim El Filali, Matt Laing, and Andrew Hanna. Truegl: A truthful, reliable, and unified engine for grounded learning in full-stack search, 2025.  
- [12] Joydeep Chandra, Ramanjot Kaur, and Rashi Sahay. Integrated framework for equitable healthcare ai: Bias mitigation, community participation, and regulatory governance. In _2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT)_ , pages 819–825, 2025.  
- [13] Joydeep Chandra and Prabal Manhas. Adversarial robustness in optimized llms: Defending against attacks. Available at SSRN: https://ssrn.com/abstract=5116078, dec 2024.  
- [14] Joydeep Chandra and Prabal Manhas. Efficient and scalable self-healing databases using meta-learning and dependency-driven recovery, 2025.
```

## Pilar: Objective
**Query:** _Tujuan utama, sasaran, atau pertanyaan penelitian yang ingin dijawab. Main objective, research goal, purpose of this study, aims to investigate, to evaluate, this paper proposes._

### Rank 1 (Score: 0.3651)
**Header:** **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
Joydeep Chandra* Satyam Kumar Navneet[∗] BNRIST, Tsinghua University Independent Beijing, China Bihar, India joydeepc2002@gmail.com navneetsatyamkumar@gmail.com  
Yong Zhang BNRIST, Tsinghua University Beijing, China zhangyong05@tsinghua.edu.cn
```

### Rank 2 (Score: 0.3582)
**Header:** **4 Results and Evaluation** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
The HMGI framework stands at the intersection of ongoing research in databases and AI. Its design is informed by and contributes to several emerging trends, and its value is best understood through comparison with alternative systems. To validate HMGI’s efficacy, we conducted comprehensive experiments comparing it against leading competitors: Vespa, Vald, Qdrant, Milvus, and Kuzu. Evaluations used a unified harness measuring key metrics across standardized datasets and parameters, ensuring fair comparisons. Scripts automated index builds, queries, updates, and metric collection. Hardware: 8-node cluster with NVIDIA A100 GPUs (80GB), Intel Xeon CPUs (64 cores), 64GB RAM, NVMe SSDs. Competitors at latest stable versions (e.g., Vespa 8.2, Qdrant 1.5). Statistical significance via Wilcoxon signed-rank tests (p<0.01). _The initial phase results have been shown here up to 50K queries, and in the future version, the full-scale evaluation results will be shared._
```

### Rank 3 (Score: 0.2800)
**Header:** **References** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
- [1] Sarat Ahmad, Zeinab Nezami, Maryam Hafeez, and Syed Ali Raza Zaidi. Benchmarking vector, graph and hybrid retrieval augmented generation (rag) pipelines for open radio access networks (oran), 2025.  
- [2] Mohammad Alaei and Fahimeh Yazdanpanah. A survey on heterogeneous cpu–gpu architectures and simulators. _Concurrency and Computation: Practice and Experience_ , 37, 10 2024.  
- [3] Panagiotis Antonopoulos, Mansi Chauhan, Shailender Dabas, Rajat Jain, Darshan Kattera, Wonseok Kim, Hanuma Kodavalla, Nikolas Ogg, Prashanth Purnananda, Rahul Ranjan, Alex Swanson, and Divyesh Tikmani. Md-mvcc: Multi-version concurrency control for schema changes in azure sql database. _Proc. VLDB Endow._ , 18(12):4791–4803, September 2025.  
- [4] Anyscale. Ray open source. Website, 2025.  
- [5] Benedikt T. Arnold, Christoph Lange, Christina Gillmann, and Stefan Decker. A service architecture for dataspaces, 2025.  
- [6] Siddhant Arora and Srikanta Bedathur. On embeddings in relational databases, 2020.  
- [7] Saurabh Bajaj, Hojae Son, Juelin Liu, Hui Guan, and Marco Serafini. Graph neural network training systems: A performance comparison of full-graph and mini-batch, 2024.  
- [8] Santosh Bhupathi. Role of databases in genai applications, 2025.
```

## Pilar: Futurework
**Query:** _Pekerjaan di masa depan, saran untuk peneliti lain, atau langkah selanjutnya. Future work, recommendations, future research directions, next steps, further investigation._

### Rank 1 (Score: 0.3163)
**Header:** **4.4 Resource Utilization and Build Efficiency** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
HMGI’s build time was 180s for 1B vectors, 1.5x faster than Qdrant (270s) via progressive indexing [64]. Critically, its memory footprint is exceptionally low. As detailed in Table 6, HMGI demonstrated the lowest memory usage on most datasets, with its consumption often being 20-25% lower than the other systems evaluated. This efficiency extends to disk usage as well [20]. CPU/GPU util averaged upto 80% during builds, with HMGI’s heterogeneous offload  
**Figure 4: Memory Usage of HMGI vs Leading Open Source Competitors**  
yielding 2x efficiency over CPU-only Kuzu [2]. Update latency at 50ms supported dynamic ingestion, 3x faster than Vespa’s rebuilds (150ms) [69]. It can be seen in Fig. 4 how HMGI outperforms the other leading open source competitors in terms of less memory usage(in mb).  
**Table 6: Memory Usage (MB) Across Systems and Datasets (Lower is Better)**  
|System|Deep1B|DEC-10K|DEC-5K|DEC-50K|SIFT1B|mm-codex-s|
|---|---|---|---|---|---|---|
|**HMGI**<br>Kuzu<br>Milvus<br>Qdrant<br>Vald<br>Vespa|**18.50**<br>25.45<br>24.80<br>24.90<br>24.70<br>25.30|**19.20**<br>26.10<br>25.50<br>25.60<br>25.30<br>25.90|21.80<br>24.90<br>24.20<br>24.30<br>23.90<br>**21.50**|**19.80**<br>25.80<br>25.10<br>25.20<br>24.90<br>25.50|22.50<br>25.10<br>24.60<br>24.70<br>**22.10**<br>24.90|**17.90**<br>26.50<br>25.90<br>26.00<br>25.70<br>26.20|
```

### Rank 2 (Score: 0.3024)
**Header:** **4.3 Accuracy Metrics** / **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
DEC-5K
DEC-50K
SIFT1B
mm-codex-s
60
Score
40
20
HMGI
Vald
Vespa
Milvus
Qdrant
Kuzu  
**Figure 2: QPS of HMGI vs Leading Open Source Competitors**  
> **[Teks Gambar Hasil OCR - The_Hybrid_Multimodal_Graph_Index_-HMGI-:_A_ComprehensiveFramework_for_Integrated_Relational_and_Vector_Search.pdf-0009-04.png]**:
Recall
100
Metrics
Deep1B
DEC-10K
(outof100)
80
DEC-5K
DEC-50K
SIFT1B
mm-codex-s
60
Score
40
20
HMGI
Vespa
Vald
Qdrant
Milvus
Kuzu  
**Figure 3: Recall@10 of HMGI vs Leading Open Source Competitors**  
positives by 15% in multimodal queries, enhancing reliability [42]. The Recall@10 of HMGI vs other open source competition can be seen in Fig. 3.  
**Table 5: Recall@10 Across Systems and Datasets (Higher is Better)**  
|System|Deep1B|DEC-10K|DEC-5K|DEC-50K|SIFT1B|mm-codex-s|
|---|---|---|---|---|---|---|
|**HMGI**<br>Kuzu<br>Milvus<br>Qdrant<br>Vald<br>Vespa|**0.995**<br>0.952<br>0.961<br>0.973<br>0.981<br>0.988|0.989<br>0.948<br>0.955<br>0.968<br>0.975<br>**0.992**|0.991<br>0.955<br>0.963<br>0.975<br>0.983<br>**0.993**|**0.996**<br>0.951<br>0.958<br>0.971<br>0.979<br>0.985|0.988<br>0.945<br>0.952<br>0.966<br>**0.991**<br>0.982|**0.997**<br>0.958<br>0.965<br>0.978<br>0.985<br>0.990|
```

### Rank 3 (Score: 0.3023)
**Header:** **The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search**
**Content:**
```text
Joydeep Chandra* Satyam Kumar Navneet[∗] BNRIST, Tsinghua University Independent Beijing, China Bihar, India joydeepc2002@gmail.com navneetsatyamkumar@gmail.com  
Yong Zhang BNRIST, Tsinghua University Beijing, China zhangyong05@tsinghua.edu.cn
```
