Due to the limited driving range, insufficient charging facilities, and time-consuming recharging, optimizing charging routes for electric vehicles (EVs) presents unique challenges compared to conventional vehicles. The time and location of EV charging during a trip not only affect an individual EV’s travel time but also influence others, as queues may form at charging station(s). This issue is at large seen as a significant constraint for uplifting EV sales in many countries. In this study, we introduce a novel EV Route Planning problem, which involves two parts: (i) finding the fastest route with recharging for an EV routing request. We model the problem as a new graph problem and prove its NP-hardness. We propose an innovative two-phase algorithm that efficiently traverses the graph to identify the optimal charging route for each EV. (ii) We find routes with minimized travel time for an EV while strategically avoid charging stations and time to recharge at those stations which can lead to minimized travel time for upcoming EVs. For this purpose, we introduce the concept of an “influence factor” to guide heuristic decisions. Our results demonstrate that this method reduces total travel time by 50% compared to the state-of-the-art on real-world datasets, with benefits becoming more significant as the number of EVs on the road increases.

The rapid growth of online retail and e-commerce has made effective and efficient Vehicle Routing Problem (VRP) solutions essential. To meet rising demand, companies are adding more depots, which changes the VRP problem to a complex optimization task of Multi-Depot VRP (MDVRP) where the routing decisions of vehicles from multiple depots are highly interdependent. The complexities render traditional VRP methods suboptimal and non-scalable for the MDVRP. In this paper, we propose a novel approach to solve MDVRP addressing these interdependencies, hence achieving more effective results. The key idea is, the MDVRP can be broken down into two core spatial tasks: assigning customers to depots and optimizing the sequence of customer visits. We adopt task-decoupling approach and propose a two-stage framework that is scalable: (i) an interdependent partitioning module that embeds spatial and tour context directly into the representation space to globally match customers to depots and assign them to tours; and (ii) an independent routing module that determines the optimal visit sequence within each tour. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms all baselines across varying problem sizes, including the adaptations of learning-based solutions for single-depot VRP. Its adaptability and performance make it a practical and readily deployable solution for real-world logistics challenges.

Due to limited driving ranges, inadequate charging facilities, and time-consuming recharging, finding an optimal charging route for electric vehicles (EVs) differs from that of other vehicle types. The time and location of charging not only impact an individual EV's travel time but also the travel time of other EVs, due to potential queuing at charging station(s). We present a novel Electric Vehicle Route Planning problem for finding the fastest route with recharging. We model this as a graph problem and propose a novel two-phase algorithm to traverse the graph to find the best charging route for each EV. We also introduce the notion of 'influence factor' to propose heuristics to find the route with the minimum travel time for an EV that avoids recharging at the charging stations that can be better utilized by other EVs. Our approach reduces 50% of total travel time over the state-of-the-art.

Prefetching is a crucial technique employed in traditional databases to enhance interactivity, particularly in the context of data exploration. Data exploration is a query processing paradigm in which users search for insights buried in the data, often not knowing what exactly they are looking for. Data exploratory tools deal with multiple challenges such as the need for interactivity with no a priori knowledge being present to help with the system tuning. The state-of-the-art prefetchers are specifically designed for navigational workloads only, where the number of possible actions is limited. The prefetchers that work with SQL-based workloads, on the other hand, mainly rely on data logical addresses rather than the data semantics. They fail to predict complex access patterns in cases where the database size is substantial, resulting in an extensive address space, or when there is frequent co-accessing of data. In this paper, we propose SeLeP, a semantic prefetcher that makes prefetching decisions for both types of workloads, based on the encoding of the data values contained inside the accessed blocks. Following the popular path of using machine learning approaches to automatically learn the hidden patterns, we formulate the prefetching task as a time-series forecasting problem and use an encoder-decoder LSTM architecture to learn the data access pattern. Our extensive experiments, across real-life exploratory workloads, demonstrate that SeLeP improves the hit ratio up to 40% and reduces I/O time up to 45% compared to the state-of-the-art, attaining 96% hit ratio and 84% I/O reduction on average.

Given a collection of m sets, each a subset of a universe {1, . . ., n}, maximum coverage is the problem of choosing k sets whose union has the largest cardinality. A simple greedy algorithm achieves an approximation factor of 1 − 1/e ≈ 0.632, which is the best possible polynomial-time approximation unless P = NP. In the streaming setting, information about the input is revealed gradually, in an online fashion. In the set-streaming model, each set is listed contiguously in the stream. In the more general edge-streaming model, the stream is composed of set-element pairs, denoting membership. The overall goal in the streaming setting is to design algorithms that use sublinear space in the size of the input. An interesting line of research is to design algorithms with space complexity polylogarithmic in the size of the input (i.e., polylogarithmic in both n and m); we call such algorithms low-space. In the set-streaming model, it is known that 1/2 is the best possible low-space approximation. In the edge-streaming model, no low-space algorithm can achieve a nontrivial approximation factor. We study the problem under the assumption that the order in which the stream arrives is chosen uniformly at random. Our main results are as follows. In the random-arrival set-streaming model, we give two new algorithms to show that low space is sufficient to break the 1/2 barrier. The first achieves an approximation factor of 1/2 + c1 using Õ(k2) space, where c1 > 0 is a small constant and Õ(·) notation suppresses polylogarithmic factors; the second achieves a factor of 1 − 1/e − ε − o(1) using Õ(k2ε−3) space, where the o(1) term is a function of k. This is essentially the optimal bound, as breaking the 1 − 1/e barrier is known to require high space. In the random-arrival edge-streaming model, we show for all fixed α > 0 and δ > 0, any algorithm that α-approximates maximum coverage with probability at least 0.9 in the random-arrival edge-streaming model requires Ω(m1−δ) space (i.e., high space), even for the special case of k = 1.

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare; making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.

Given a collection of $m$ sets from a universe $\mathcal{U}$, the Maximum Set Coverage problem consists of finding $k$ sets whose union has largest cardinality. This problem is NP-Hard, but the solution can be approximated by a polynomial time algorithm up to a factor $1-1/e$. However, this algorithm does not scale well with the input size. In a streaming context, practical high-quality solutions are found, but with space complexity that scales linearly with respect to the size of the universe $|\mathcal{U}|$. However, one randomized streaming algorithm has been shown to produce a $1-1/e-\varepsilon$ approximation of the optimal solution with a space complexity that scales only poly-logarithmically with respect to $m$ and $|\mathcal{U}|$. In order to achieve such a low space complexity, the authors used a technique called subsampling, based on independent-wise hash functions, and $F_0$-sketching. This article focuses on this sublinear-space algorithm and introduces methods to reduce the time cost of subsampling. Firstly, we give some optimizations that do not alter the space complexity, number of passes and approximation quality of the original algorithm. In particular, we reanalyze the error bounds to show that the original independence factor of $Ω(\varepsilon^{-2} k \log m)$ can be fine-tuned to $Ω(k \log m)$. Secondly we show that $F_0$-sketching can be replaced by a much more simple mechanism. Finally, our experimental results show that even a pairwise-independent hash-function sampler does not produce worse solution than the original algorithm, while running significantly faster by several orders of magnitude.

Abstract The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of partial evaluation: if the volume of intermediate results is large, a large number of repeated partial matches will be generated; and the assembly computation handled by the master would be a bottleneck. In this paper, we propose an optimal partial evaluation algorithm and a filter method to reduce partial matches by exploring the computing characteristics of partial evaluation and assembly framework. (1) An index structure named inner boundary node index (IBN-Index) is constructed to prune for graph exploration to improve the searching efficiency of the partial evaluation phase. (2) The boundary characteristics of local partial matches are utilized to construct a boundary node index (BN-Index) to reduce the number of local partial matches. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.

While the electric vehicle (EV) industry is facing some challenges concerning its refueling, its rapid growth in popularity is increasing these difficulties. In this paper, we demonstrate the gravity of the problems that EVs may experience for charging,both now and in the near future, and show how establishing new charging stations can be challenging. We also present the challenges in optimizing the use of charging stations by EV users. Then, we envisage opportunities for the rise of alternative charging options, such as distributed generation, crowdsourced, wireless and mobile charging stations. Additionally, we explain directions on how route and charging stations' location planning can cater to optimizing the charging infrastructure.

In this modern era, traffic congestion has become a major source of severe negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The research field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Recently, researchers have started to focus on machine learning models because of their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its sheer prediction power which can be attributed to the complex and deep structure. Despite the popularity of deep neural network models in the field of traffic prediction, literature surveys of such methods are rare. In this work, we present an up-to-date survey of deep neural network for traffic prediction. We will provide a detailed explanation of popular deep neural network architectures commonly used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.

Understanding urban areas of interest (AOIs) is essential in many real-life scenarios, and such AOIs can be computed based on the geographic points that satisfy user queries. In this article, we study the problem of efficient and effective visualization of user-defined urban AOIs in an interactive manner. In particular, we first define the problem of user-defined AOI visualization based on a real estate data visualization scenario, and we illustrate why a novel footprint method is needed to support the visualization. After extensively reviewing existing “footprint” methods, we propose a parameter-free footprint method, named AOI-shapes, to capture the boundary information of a user-defined urban AOI. Next, to allow interactive query refinements by the user, we propose two efficient and scalable algorithms to incrementally generate urban AOIs by reusing existing visualization results. Finally, we conduct extensive experiments with both synthetic and real-world datasets to demonstrate the quality and efficiency of the proposed methods.

Similar trajectory search is a crucial task that facilitates many downstream spatial data analytic applications. Despite its importance, many of the current literature focus solely on the trajectory’s spatial similarity while neglecting the temporal information. Additionally, the few papers that use both the spatial and temporal features based their approach on a traditional point-to-point comparison. These methods model the importance of the spatial and temporal aspect of the data with only a single, pre-defined balancing factor for all trajectories, even though the relative spatial and temporal balance can change from trajectory to trajectory. In this article, we propose the first spatio-temporal, deep-representation-learning-based approach to similar trajectory search. Experiments show that utilizing both features offers significant improvements over existing point-to-point comparison and deep-representation-learning approach. We also show that our deep neural network approach is faster and performs more consistently compared to the point-to-point comparison approaches.

In this paper, we propose and study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and a set of rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing ridesharing solutions in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing ridesharing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost (either distance or time). (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per newly coming rider requests should be incorporated, while it is unfortunately excluded by most existing solutions. In order to achieve our goal, we propose an underlying index structure on top of a partitioned road network, and compute the lower bounds of the shortest path distance between any two vertices. Using the proposed index together with a set of new pruning rules, we develop an efficient algorithm to dynamically include new riders directly into an existing trip schedule of a driver. In order to respond to new rider requests more effectively, we propose two algorithms that bilaterally match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the effectiveness and efficiency of the proposed methods.

SimRank is a significant metric to measure the similarity of nodes in graph data analysis. The problem of SimRank computation has been studied extensively, however there is no existing work that can provide one unified algorithm to support the SimRank computation both on static and temporal graphs. In this work, we first propose CrashSim, an index-free algorithm for single-source SimRank computation in static graphs. CrashSim can provide provable approximation guarantees for the computational results in an efficient way. In addition, as the reallife graphs are often represented as temporal graphs, CrashSim enables efficient computation of SimRank in temporal graphs. We formally define two typical SimRank queries in temporal graphs, and then solve them by developing an efficient algorithm based on CrashSim, called CrashSim-T. From the extensive experimental evaluation using five real-life and synthetic datasets, it can be seen that the CrashSim algorithm and CrashSim-T algorithm substantially improve the efficiency of the state-of-the-art SimRank algorithms by about 30%, while achieving the precision of the result set with about 97%.

Searching for a community based on query nodes in a graph is a fundamental problem and has been extensively investigated. Most of the existing approaches focus on finding a community in a social network, and very few studies consider location-based social networks where users can check in locations. In this paper we propose the GeoSocial Community Search problem (GCS) which aims to find a social community and a cluster of spatial locations that are densely connected in a location-based social network simultaneously. The GCS can be useful for marketing and user/location recommendation. To the best of our knowledge, this is the first work to find a social community and a cluster of spatial locations that are densely connected from location-based social networks. We prove that the problem is NP-hard, and is not in APX, unless P = NP. To solve this problem, we propose three algorithms: core-based basic algorithm, top-down greedy removing algorithm, and an expansion algorithm. Finally, we report extensive experimental studies that offer insights into the efficiency and effectiveness of the proposed solutions.

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. We observe that existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the co-moving pattern. In this study, we propose the problem of finding recurrent co-moving patterns from streaming trajectories, enabling us to discover recent co-moving patterns that are repeated within a given time period. Experimental results on real-life trajectory data verify the efficiency and effectiveness of our method.

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on a real-life trajectory database show the efficiency of our method.

Understanding urban areas of interest (AOIs) is essential to decision making in various urban planning and exploration tasks. Such AOIs can be computed based on the geographic points that satisfy the user query. In this demo, we present an interactive visualization system of urban AOIs, supported by a parameter-free and efficient footprint method called AOI-shapes. Compared to state-of-the-art footprint methods, the proposed AOI-shapes (i) is parameter-free, (ii) is able to recognize multiple regions/outliers, (iii) can detect inner holes, and (iv) supports the incremental method. We demonstrate the effectiveness and efficiency of the proposed AOI-shapes based on a real-world real estate dataset in Australia. A preliminary version of the online demo can be accessed at http://aoishapes.com/.

Urban data (e.g., real estate data, crime data) often have multiple attributes which are highly geography-related. With the scale of data increases, directly visualizing millions of individual data points on top of a map would overwhelm users' perceptual and cognitive capacity and lead to high latency when users interact with the data. In this demo, we present ConvexCubes, a system that supports interactive visualization of large-scale multidimensional urban data in a multi-granularity way. Comparing to state-of-the-art visualization-driven data structures, it exploits real-world geographic semantics (e.g., country, state, city) rather than using grid-based aggregation. Instead of calculating everything on demand, ConvexCubes utilizes existing visualization results to efficiently support different kinds of user interactions, such as zooming & panning, filtering and granularity control. Our system can be accessed at http://115.146.89.158/ConvexCubes/.

Abstract In this paper we study the problem of supporting effective and scalable visualization for the rapidly increasing volumes of urban data. From an extensive literature study, we find that the existing solutions suffer from at least one of the drawbacks below: (i) loss of interesting structures/outliers due to sampling; (ii) supporting heatmaps only, which provides limited information; and (iii) no notion of real‐world geography semantics (e.g., country, state, city) is captured in the visualization result as well as the underlying index. Therefore, we propose ConcaveCubes, a cluster‐based data cube to support interactive visualization of large‐scale multidimensional urban data. Specifically, we devise an appropriate visualization abstraction and visualization design based on clusters. We propose a novel concave hull construction method to support boundary based cluster map visualization, where real‐world geographical semantics are preserved without any information loss. Instead of calculating the clusters on demand, ConcaveCubes (re)utilizes existing calculation and visualization results to efficiently support different kinds of user interactions. We conduct extensive experiments using real‐world datasets and show the efficiency and effectiveness of ConcaveCubes by comparing with the state‐of‐the‐art cube‐based solutions.

With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data has become available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of smart transportation services and recent break-throughs in autonomous vehicles increase our reliance on trajectory data in a wide variety of applications. Supporting these services in emerging platforms requires more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Best Facility Trajectory Search ( k BFT); and (ii) k Best Coverage Facility Trajectory Search ( k BCovFT). We propose a novel index structure, the Trajectory Quadtree (TQ-tree) that utilizes a quadtree to hierarchically organize trajectories into different nodes, and then applies a z-ordering to further organize the trajectories by spatial locality inside each node. This structure is highly effective in pruning the trajectory search space, which is of independent interest. By exploiting the TQ-tree, we develop a divide-and-conquer approach to efficiently process a k BFT query. To solve the k BCovFT, which is a non-submodular NP-hard problem, we propose a greedy approximation. We evaluate our algorithms through an extensive experimental study on several real datasets, and demonstrate that our algorithms outperform baselines by two to three orders of magnitude.

A socio spatial group query finds a group of users who possess strong social connections with each other and have the minimum aggregate spatial distance to a meeting point. Existing studies limit to either finding the best group of a fixed size for a single meeting location, or a single group of a fixed size w.r.t. multiple locations. However, it is highly desirable to consider multiple locations in a real-life scenario in order to organize impromptu activities of groups of various sizes. In this paper, we propose Top k Flexible Socio Spatial Group Query (Top k-FSSGQ) to find the top k groups w.r.t. multiple POIs where each group follows the minimum social connectivity constraints. We devise a ranking function to measure the group score by combining social closeness, spatial distance, and group size, which provides the flexibility of choosing groups of different sizes under different constraints. To effectively process the Top k-FSSGQ, we first develop an Exact approach that ensures early termination of the search based on the derived upper bounds. We prove that the problem is NP-hard, hence we first present a heuristic based approximation algorithm to effectively select members in intermediate solution groups based on the social connectivity of the users. Later we design a Fast Approximate approach based on the relaxed social and spatial bounds, and connectivity constraint heuristic. Experimental studies have verified the effectiveness and efficiency of our proposed approaches on real datasets.

In this demonstration, we present VizQ, an efficient, scalable, and interactive system to process and visualize a comprehensive collection of novel visibility queries in the presence of obstacles in 3D space. Specifically, we demonstrate four types of query processing: (i) k Maximum Visibility Query (kMVQ), that finds k locations with the maximum visibility of a target object (ii) Visibility Color Map (VCM), where each point in the space is assigned a color value denoting the visibility measure of the target (iii) Continuous Maximum Visibility (CMV) that continuously finds the location that provides the best view of a moving target, and (iv) Text Visibility Color Map (TVCM), where VCM is generated considering readability of text data displayed on a target. We are the first to propose efficient algorithms to run all of the above four types of visibility queries in the context of a large number of 3D obstacle database. We exploit human visibility metrics to design our data structures and algorithms to efficiently process queries, and our approaches outperform baseline approaches in several order of magnitude both in terms of I/Os and processing time. The link of our demonstration video is https://youtu.be/rcizJtFvQfU.

In this paper, we propose and study the problem of top-m rank aggregation of spatial objects in streaming queries, where, given a set of objects O, a stream of spatial queries (kNN or range), the goal is to report the m objects with the highest aggregate rank. The rank of an object with respect to an individual query is computed based on its distance from the query location, and the aggregate rank is computed from all of the individual rank orderings. In order to solve this problem, we show how to upper and lower bound the rank of an object for any unseen query. Then we propose an approximation solution to continuously monitor the top-m objects efficiently, for which we design an Inverted Rank File (IRF) index to guarantee the error bound of the solution. In particular, we propose the notion of safe ranking to determine whether the current result is still valid or not when new queries arrive, and propose the notion of validation objects to limit the number of objects to update in the top-m results. We also propose an exact solution for applications where an approximate solution is not sufficient. Last, we conduct extensive experiments to verify the efficiency and effectiveness of our solutions. This is a fundamental problem that draws inspiration from three different domains: rank aggregation, continuous queries and spatial databases, and the solution can be used to monitor the importance / popularity of spatial objects, which in turn can provide new analytical tools for spatial data.

The problem of maximizing bichromatic reverse k nearest neighbor queries (BR k NN) has been extensively studied in spatial databases. In this work, we present a related query for spatial-textual databases that finds an optimal location, and a set of keywords that maximizes the size of bichromatic reverse spatial textual k nearest neighbors (MaxBRST k NN). Such a query has many practical applications including social media advertisements where a limited number of relevant advertisements are displayed to each user. The problem is to find the location and the text contents to include in an advertisement so that it will be displayed to the maximum number of users. The increasing availability of spatial-textual collections allows us to answer these queries for both spatial proximity and textual similarity. This paper is the first to consider the MaxBRST k NN query. We show that the problem is NP-hard and present both approximate and exact solutions.

Many real-world problems, such as placement of surveillance cameras and pricing of hotel rooms with a view, require the ability to determine the visibility of a given target object from different locations. Advances in large-scale 3D modeling (e.g., 3D virtual cities) provide us with data that can be used to solve these problems with high accuracy. In this paper, we investigate the problem of finding the location which provides the best view of a target object with visual obstacles in 2D or 3D space, for example, finding the location that provides the best view of fireworks in a city with tall buildings. To solve this problem, we first define the quality measure of a view (i.e., visibility measure) as the visible angular size of the target object. Then, we propose a new query type called the k-Maximum Visibility (kMV) query, which finds k locations from a set of locations that maximize the visibility of the target object. Our objective in this paper is to design a query solution which is capable of handling large-scale city models. This objective precludes the use of approaches that rely on constructing a visibility graph of the entire data space. As a result, we propose three approaches that incrementally consider relevant obstacles in order to determine the visibility of a target object from a given set of locations. These approaches differ in the order of obstacle retrieval, namely: query centric distance based, query centric visible region based, and target centric distance based approaches. We have conducted an extensive experimental study on real 2D and 3D datasets to demonstrate the efficiency and effectiveness of our solutions.