The booming of mobile internet and crowdsourcing technology has offered great opportunities for first and last mile logistics (FLML) service. Unlike the traditional FLML service that separates the parcel collection in the first mile from the parcel delivery in the last mile, a new type of crowdsourced FLML service integrates parcel collection and parcel delivery services as a whole, which can significantly improve the efficiency of FLML service. Briefly, in a crowdsourced FLML service, the platform assigns the customers’ triggered pick-up parcels to the couriers who are delivering drop-off parcels in terms of the real-time status of couriers (e.g., capacity, location, and schedule). Existing works solving the crowdsourced FLML problem only consider the utility maximization for the platform but ignore the incentive to the utilities of couriers. Inspired by this, in this paper, we investigate a novel type of crowdsourced FLML problem, namelyAuction-based Crowdsourced FLML (ACF), where the platform assigns the couriers with suitable pick-up parcels based on the preferences of couriers with the goal of maximizing the social welfare of the platform and couriers. To solve the ACF problem, we present a novel auction model namedMulti-attribute Reverse Vickrey (MRV), where the couriers bid on parcels according to their preferences for parcels. Based on the MRV model, we present three efficient assignment algorithms to assign parcels to couriers. In addition, we give theoretical analysis for our proposed algorithms. Extensive experiments examine the efficiency and effectiveness of our solutions.

In the past few years ridesharing has largely reshaped the transportation marketplace. It is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. In the current ridesharing research, social ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing are two active directions with important business implications. Simultaneously optimizing social cohesion and revenue is vital to a commercial ridesharing platform's sustainable development, which, however, has not been previously studied. In this paper, we first present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-$k$Matching (PTkM) queries which retrieve the top-$k$vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment.

This paper presents a HAO-Graph system that generates and visualizes knowledge graphs from a speech in real-time. When a user speaks to the system, HAO-Graph transforms the voice into knowledge graphs with key phrases from the original speech as nodes and edges. Different from language-to-language systems, such as Chinese-to-English and English-to-English, HAO-Graph converts a speech into graphs, and is the first of its kind. The effectiveness of our HAO-Graph system is verified by a two-hour chairman's talk in front of two thousand participants at an annual meeting in the form of a satisfaction survey.

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 Knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.

Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. There has been much attention on the optimization of costs/detour in shared rides. However, other important factors, such as social comfort and trust issues, have not been fully explored in the existing work in ridesharing. In this paper, we introduce social-awareness into ridesharing services and study two social-aware ridesharing problem: the top-k Social-aware Taxi Ridesharing (TkSaTR) problem and the Assignment of Requests to Offers (ARO) problem. Furthermore, this paper also presents the directions of future work in social-aware ridesharing.

With the pervasiveness of location-based services (LBS), spatial data processing has received considerable attention in the research of database system management. Among various spatial query techniques, index structures play a key role in data access and query processing. However, existing spatial index structures (e.g., R-tree) mainly focus on partitioning data space or data objects. In this paper, we explore the potential to construct the spatial index structure by learning the distribution of the data. We design a new data-driven spatial index structure, namely learned Z-order Model (ZM) index, which combines the Z-order space filling curve and the staged learning model. Experimental results on both real and synthetic datasets show that our learned index significantly reduces the memory cost and performs more efficiently than R-tree in most scenarios.

Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort and trust issues, have not been fully considered in the existing works. In this paper, we formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP-hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets.