Parametric Query Optimization (PQO) is crucial for efficiently handling parametrized queries (PQ) in many database applications. This paper addresses two key challenges in existing PQO techniques, focusing on plan set generation and best plan selection. Regarding plan set generation, existing methods rely on modifying sub-plan cardinalities, often resulting in inefficiency and sub-optimal performance due to unclear extents of modifications needed. To overcome this issue, we propose a hybrid plan enumeration algorithm that adeptly adjusts both cardinality and join order. Regarding best plan selection, recent methods rely on machine learning models to choose plans with minimum predicted latency, but they struggle with accurate predictions when parameter bindings vary. Even minor variations in parameters can significantly impact cardinality, affecting plan optimality. To overcome this issue, we propose to utilize a learning-to-rank model, which uses relative rankings as a more reliable performance indicator. Our approach, integrated into PostgreSQL, undergoes extensive experiments on real datasets, showcasing significant improvements in both efficiency and accuracy, as compared to baselines. Specifically, it accelerates the PostgreSQL optimizer by up to 2.57× and surpasses the best existing baseline by up to 1.36×.

With the expansion of modern database services, multi-user access has become a crucial feature in various practical application scenarios, including enterprise applications and e-commerce platforms. However, if multiple users submit queries within a short time frame, it can result in potential issues such as redundant computation and query concurrency. Unfortunately, most existing multi-query optimization methods, which aim to enhance query processing efficiency, have not adequately addressed these two problems, especially in the setting where multiple queries are being executed concurrently. To this end, we propose a novel method named Lemo for the multi-query optimization problem. Specifically, we propose a novel value network to predict latencies of concurrent queries as the foundation model for query plan generation. Furthermore, we introduce a shared buffer manager component to cache the intermediate results of sub-queries. The shared buffer manager applies a novel replacement policy to maintain the cached buffer with the objective of maximizing the opportunity for the reuse of the cached sub-queries. Based on the shared buffer, our proposed value network can incorporate the cached results into cost estimation to further guide Lemo in generating query plans, thus avoiding redundant computation. Lemo has been integrated into PostgreSQL and experiments conducted on real datasets with PostgreSQL show that it outperforms all the baselines in efficiency.

Reorganizing bus frequencies to cater for the actual travel demands can significantly save the cost of the public transport system. Many, if not all, previous studies formulate this as a bus frequency optimization problem that tries to minimize passengers’ average waiting time. On the other hand, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an index-based$(1-1/e)$-approximation algorithm. By exploiting the locality property of routes in a bus network, we further propose a partition-based greedy method that achieves a$(1-\rho)(1-1/e)$approximation ratio. Then we propose a progressive partition-based greedy method to further boost the efficiency while achieving a$(1-\rho)(1-1/e-\varepsilon)$approximation ratio. For the FASTCO problem, two greedy-based heuristic methods are proposed. Experiments on a real city-wide bus dataset in Singapore have been conducted to verify the efficiency, effectiveness, and scalability of our methods in addressing FAST and FASTCO respectively.

Abstract Motivated by the application of viral marketing , the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G , such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT). This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.

In this paper, we demonstrate a satisfaction-boosting bus scheduling assistant called FASTS, which assists users to find an optimal bus schedule. FASTS performs bus scheduling based on the constraints specified by the user in either a coarse-grained or a fine-grained manner, supports different explorations with a varying number of constraints, and provides analysis to quantify the performance of bus schedules and presents the results in a visually pleasing way. We demonstrate FASTS using real-world bus routes (396 routes) and one-week bus touch-on/touch-off records (28 million trip records) in Singapore.

In this paper, we propose and study a new problem called the weighted random walk domination. Given a weighted graph G ( V, E ) and a budget B of the weighted random walk, it aims to find a k -size set S , which can minimize the total costs of the remaining nodes to access S through the weighted random walk, which is bounded by B. This problem is critical to a range of real-world applications, such as advertising in social networks and telecommunication base station selection in wireless sensor networks. We first present a dynamic programming based greedy method (DpSel) as a baseline. DpSel is time-consuming when | V | is huge. Thus, to overcome this drawback, we propose a matrix-based greedy method (MatrixSel), which can reduce the computation cost greatly. To further accelerate MatrixSel, we propose a BoundSel approach to reduce the number of the gain computations in each candidate selection by proactively estimating the upper bound of the marginal gain of the candidate node. Notably, all methods can achieve an approximation ratio of (1 - 1/ e ). Experiments on real datasets have been conducted to verify the efficiency, effectiveness, memory consumption and scalability of our methods.

In this paper we propose and study the problem of optimizing the influence of outdoor advertising (ad) when impression counts are taken into consideration. Given a database U of billboards, each of which has a location and a non-uniform cost, a trajectory database T and a budget B, it aims to find a set of billboards that has the maximum influence under the budget. In line with the advertising consumer behavior studies, we adopt the logistic function to take into account the impression counts of an ad (placed at different billboards) to a user trajectory when defining the influence measurement. However, this poses two challenges: (1) our problem is NP-hard to approximate within a factor of O(|T|1-ε) for any ε>0 in polynomial time; (2) the influence measurement is non-submodular, which means a straightforward greedy approach is not applicable. Therefore, we propose a tangent line based algorithm to compute a submodular function to estimate the upper bound of influence. Henceforth, we introduce a branch-and-bound framework with a θ-termination condition, achieving θ2/(1 - 1/e) approximation ratio. However, this framework is time-consuming when |U| is huge. Thus, we further optimize it with a progressive pruning upper bound estimation approach which achieves θ2/(1 - 1/e - ε) approximation ratio and significantly decreases the running-time. We conduct the experiments on real-world billboard and trajectory datasets, and show that the proposed approaches outperform the baselines by 95% in effectiveness. Moreover, the optimized approach is around two orders of magnitude faster than the original framework.

In this paper, we demonstrate an Intelligent Trajectory-driven outdoor Advertising deployment Assistant (ITAA), which assists users to find an optimal strategy for outdoor advertising (ad) deployment. The challenge is how to measure the influence to the moving trajectories of ads, and how to optimize the placement of ads among billboards that maximize the influence has been proven NP-hard. Therefore, we develop a framework based on two trajectory-driven influence models. ITAA is built upon this framework with a user-friendly UI. It serves both ad companies and their customers. We enhance the interpretability to improve the user's understanding of the influence of ads. The interactive function of ITAA is made interpretable and easy to engage.