Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Prediction queries, which combine relational operations with DL models to analyze multi-modal data, provide a powerful facility for smart in-database analysis. However, loose integration systems, which support such queries via User-Defined Functions (UDFs) and external runtimes, often impose high economic costs, particularly in cloud-based environments; while tight integration systems, which implement model inference through a sequence of explic-itly written SQL queries, incur heavy user burdens and huge optimization space. In this paper, we introduce PEPS, an end-to-end analytical database for automatic and efficient prediction query processing. PEPS automates the process of prediction query synthesis and ensures usability through declarative schemes. Additionally, it improves query performance with offline optimization using the DB-oriented Computation Graph Optimization (DBCGO) algorithm and online optimization via heuristic query rewriting. Empirical evaluations show that PEPS offers better usability than baseline methods, provides lower economic costs, and achieves performance speedup compared to advanced Python UDFs.

Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Collaborative queries, which employ relational operations to process structured data and DL models to process unstructured data, provide a powerful facility for DL-based in-database analysis. The classical approach to support collaborative queries in relational databases is to integrate DL models with user-defined functions (UDFs) in a general-purpose language (e.g., C++) to process unstructured data. This approach suffers from suboptimal performance as the opaque UDFs preclude the generation of an optimal query plan. A recent work, DL2SQL, addresses the problem of collaborative query optimization by first converting DL computations into SQL subqueries and then using a classical relational query optimizer to optimize the entire collaborative query. However, the DL2SQL approach compromises usability by requiring data analysts to manually manage DL-related data and tune query performance. To this end, this paper introduces AQUA, an analytical database designed for efficient collaborative query processing. Built on DL2SQL, AQUA automates translations from collaborative queries into SQL queries. To enhance usability, AQUA introduces two techniques: 1) a declarative scheme for DL-related data management, and 2) DL-specific optimizations for collaborative query processing, eliminating the burden of manual data management and performance tuning from the data analysts. We demonstrate the key contributions of AQUA via a web APP that allows the audience to perform collaborative queries on the CIFAR-10 dataset.

Welfare maximization (WM) aims to select a group of seed nodes to allocate different items for marketing, so that the whole welfare after diffusion over a social network is maximized. It has attracted much attention due to the practical applications such as viral marketing and online advertisements, where the economic incentives are incorporated into users' adoption behaviors. However, existing studies ignore the time impact on the diffusion and consider a single item type. In this paper, we propose an effective time-aware utility-driven independent cascade (TUIC) model, that incorporates the time-aware multi-item propagation, utility-driven item adoption, and mixed item relationships together. We identify and formulate the time-aware welfare maximization problem. We develop a general framework to address the problem for mixed competitive, complementary, and independent items. It derives item allocation with the$(1 -1/e-\epsilon)$approximate social welfare in special cases. Extensive experiments on several real-life social networks demonstrate the effectiveness of TUIC model and the efficiency of the proposed framework, compared to the state of the arts.