Learning-Based Data Storage [Vision] (Technical Report)

Deep neural network (DNN) and its variants have been extensively used for a wide spectrum of real applications such as image classification, face/speech recognition, fraud detection, and so on. In addition to many important machine learning tasks, as artificial networks emulating the way brain cells function, DNNs also show the capability of storing non-linear relationships between input and output data, which exhibits the potential of storing data via DNNs. We envision a new paradigm of data storage, "DNN-as-a-Database", where data are encoded in well-trained machine learning models. Compared with conventional data storage that directly records data in raw formats, learning-based structures (e.g., DNN) can implicitly encode data pairs of inputs and outputs and compute/materialize actual output data of different resolutions only if input data are provided. This new paradigm can greatly enhance the data security by allowing flexible data privacy settings on different levels, achieve low space consumption and fast computation with the acceleration of new hardware (e.g., Diffractive Neural Network and AI chips), and can be generalized to distributed DNN-based storage/computing. In this paper, we propose this novel concept of learning-based data storage, which utilizes a learning structure called learning-based memory unit (LMU), to store, organize, and retrieve data. As a case study, we use DNNs as the engine in the LMU, and study the data capacity and accuracy of the DNN-based data storage. Our preliminary experimental results show the feasibility of the learning-based data storage by achieving high (100%) accuracy of the DNN storage. We explore and design effective solutions to utilize the DNN-based data storage to manage and query relational tables. We discuss how to generalize our solutions to other data types (e.g., graphs) and environments such as distributed DNN storage/computing.Comment: 14 pages, 16 figure

Evaluation of the Impacts of Data Model and Query Language on Query Performance

It is important to understand how users can utilize database systems more effectively to enhance performance. A major research interest is to evaluate and compare user performance across different data models and query languages. So far, experiments have tested combinations of model plus language. An interesting theoretical and practical question is: how much of the performance difference is caused by the data model itself, and how much by the additional query language syntax? A cognitive model of query processing suggests measurement at two stages. The data model has impact at the first stage, and the model with the query language syntax together has the impact at the second stage. An experiment that compares the objected-oriented and relational models and query languages at the two stages provides fresh results