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vdms

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VDMS: Your Favorite Visual Data Management System

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# VDMS: Your Favorite Visual Data Management System

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VDMS is a storage solution for efficient access of big-”visual”-data that aims
to achieve cloud scale by searching for relevant visual data via visual
metadata stored as a graph and enabling machine friendly enhancements to
visual data for faster access.  We use an in-persistent-memory graph database
developed in our team called Persistent Memory Graph Database (PMGD) as the
metadata tier and we are exploring the use of an array data manager, TileDB
and other formats for images, visual descriptors, and videos as part of our
Visual Compute Library (VCL). VDMS is run as a server listening for client
requests and we provide client side bindings to enable communication between (
python, C++) applications and the server. Hence, it also has a Request Server
component defined to implement the VDMS API, handle concurrent client
requests, and coordinate the request execution across its metadata and data
components to return unified responses. This project aims to research the use
of a scalable multi-node graph based metadata store as part of a hierarchical
storage framework specifically aimed at processing visual data, and also it
includes an investigation into the right hardware and software optimizations
to store and efficiently access large scale (pre-processed) visual data.

## Motivation

Data access is swiftly becoming a bottleneck in visual data processing,
providing an opportunity to influence the way visual data is treated in the
storage system. To foster this discussion, we identify two key areas where
storage research can strongly influence visual processing run-times:
efficient metadata storage and new storage formats for visual data. We
propose a storage architecture designed for efficient visual data access
that exploits next generation hardware and give preliminary results showing
how it enables efficient vision analytics.

## Get Started

To get started, take a look at the [Install.md](/docs/Install.md) file, where
you will find instructions on how to install and run the server.

Also, visit our [documentation](https://intellabs.github.io/vdms/)
to learn more about the VDMS API, and take a look at some of
the examples/tutorials.

## Academic Papers

| Conference | Links, Cite | Description |
| ---------- | ----------- | ----------- |
| Industrial and Applications @ VLDB 2021 | [Paper](http://vldb.org/pvldb/vol14/p3240-remis.pdf) | Demonstrate VDMS capabilities in image search application |
| 2nd USENIX Workshop @ HotEdge 2019 | [Paper](https://www.usenix.org/system/files/hotedge19-paper-altarawneh.pdf), [Presentation](https://www.usenix.org/sites/default/files/conference/protected-files/hotedge19_slides_altarawneh.pdf) | VDMS in Edge-to-cloud architecture for video streaming application |
| Learning Systems @ NIPS 2018 | [Paper](https://export.arxiv.org/abs/1810.11832), [Cite](https://dblp.uni-trier.de/rec/bibtex/journals/corr/abs-1810-11832) | Systems for Machine Learning [Workshop](http://learningsys.org/nips18/cfp.html) @ NIPS |
| HotStorage @ ATC 2017 | [Paper](https://www.usenix.org/conference/hotstorage17/program/presentation/gupta-cledat), [Presentation](https://www.usenix.org/conference/hotstorage17/program/presentation/gupta-cledat), [Cite](https://www.usenix.org/biblio/export/bibtex/203374)| Positioning Paper at USENIX ATC 2017 Workshop |

## Integrations
- **Generative AI Component of Open Platform for Enterprise AI (OPEA):** VDMS is a component (retriever, dataprep, vectorstore) of OPEA and leveraged in the [VideoQnA Application](https://github.com/opea-project/GenAIExamples/tree/main/VideoQnA).

- **Langchain:** VDMS is available as a [vector store](https://python.langchain.com/docs/integrations/vectorstores/vdms/). We also provide a few helpful guides:
    * [**VDMS notebook:**](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/vectorstores/vdms.ipynb) Details the steps to use VDMS with Langchain.
    * [**Visual RAG cookbook:**](https://github.com/langchain-ai/langchain/blob/master/cookbook/visual_RAG_vdms.ipynb) which performs Visual Retrieval-Augmented-Generation (RAG) using videos and scene descriptions generated by open source models.
    * [**Multi-modal RAG cookbook:**](https://github.com/langchain-ai/langchain/blob/master/cookbook/multi_modal_RAG_vdms.ipynb) Performs RAG on documents including text and images, using unstructured for parsing, and VDMS as the vectorstore.

- **Open Visual Cloud:** VDMS is used as a meta data store in a [Video Curation sample application](https://github.com/OpenVisualCloud/Video-Curation-Sample)
 and a [Streaming Video Curation sample application](https://github.com/IntelLabs/Video-Curation-Sample-DEV).

## Contributing

Thank you for your interest in contributing to VDMS.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.

To learn how to contribute to VDMS, please follow the [Developer Guide](/docs/contribute/Developer-Guide.md).

vdms

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