ApertureData Platform’s Documentation¶
ApertureData Platform 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). ApertureData Platform is run as a server listening for client requests and we provide client side bindings (Python, C++) to enable communication between applications and the server. Hence, it also has a Request Server component defined to implement the ApertureData Platform 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.
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.
|Learning Systems @ NIPS 2018||learningsys.org||Systems for Machine Learning Workshop @ NIPS||uni-trier.de|
|HotStorage @ ATC 2017||usenix.org||Positioning Paper at USENIX ATC 2017 Workshop||usenix.org/bibtex|