The swift advancement of urbanization has resulted in the growth of numerous large cities, which have enhanced the lives of many individuals but have also created significant challenges, such as air pollution, higher energy consumption, and traffic congestion. Addressing these issues was nearly unfeasible in the past due to the intricate and ever-changing nature of urban environments. Today, however, advancements in sensing technologies and extensive computing infrastructures have generated vast amounts of big data related to urban areas, including information on human mobility, air quality, traffic patterns, and geographic data. Inspired by the potential for creating smarter cities, we developed a vision for urban computing that seeks to harness insights from diverse and extensive data collected in urban settings, using this valuable information to tackle the critical problems our cities currently encounter.

A natural language interface for databases (NLIDB) plays an important role by allowing non-expert users to query spatial data using natural language without understanding complex query syntax or schema. Natural language query (NLQ) corpora play a vital role in supporting syntactic parsing and intent recognition during the training of NLIDB systems, enhancing the system’s ability to understand and transform natural language queries. The lack of high-quality spatial NLQ corpora constrains the diversity and robustness of spatial NLIDB systems, preventing the accurate handling of complex geospatial queries. To address this, we propose SpaCor, a tool designed to construct high-quality spatial NLQ corpora by integrating two key modules: (i) automatic detection and repair, and (ii) template-based corpus generation. Firstly, the detection and repair module corrects query errors using specialized spatial knowledge bases. Secondly, the corpus generation module creates diverse and syntactically accurate queries. Experimental results show that SpaCor improves spatial NLIDB translation performance, achieving a 21.4% improvement in conversion rate and a 18.6% increase in accuracy, and improving the quality of corpus generation. The demo video is available at https://youtu.be/eeeTs_6ndRo.

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption, and traffic congestion. Tackling these challenges was nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today.

We consider surveillance of a geographic region by a collaborative system of drones. The drones assist each other in identifying and managing activities of interest on the ground. We also consider an adversary who can create both genuine and fake activities on the ground. The objective of the adversary is to use fake activities to maximize the response time to genuine activities. We present two collaboration algorithms and analyze their response times, as well as the adversary’s efforts in terms of the number of fake activities required to achieve a certain response time.

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges were nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today.

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges were nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today. This is the eleventh time that we organize this workshop. The previous 10 workshops were hosted with SIGKDD and SIGSPATIAL, each of which attracted over 70 participants and 30 submissions on average.

Transportation is an indispensable link for human progress, and essential to the development of civilizations [...]

Consciousness is key to Artificial General Intelligence. It is currently unclear what is consciousness and we expect that by the time it is understood, there will probably be millions of man-years invested in the development of AI agents. This paper addresses the following question. How should AI agents be engineered from now on, such that when it becomes clear how consciousness and subjective experiences are generated, the effort of endowing AI agents and robots existing at that time with consciousness is minimized. As far as we know this question has not been studied heretofore. The paper proposes a methodology and architecture that are based on evolving Probabilistic Relational Databases, and on stepwise progressive data fusion. The methodology and architecture are independent of what consciousness turns out to be, provided that it can be incorporated into AI agents.

SPECIALTY GRAND CHALLENGE article Front. Sustain. Cities, 27 May 2020 | https://doi.org/10.3389/frsc.2020.00022

This paper describes an information service that personalizes air pollution monitoring by considering the fine grained user location, her microenvironment, and her activity. Personalization is obtained by integrating a large number of information sources including the Environmental Protection Agency (EPA) monitoring stations, traffic, weather, portable air pollution data from sensors carried by a small fraction of the population, smartphone sensors, vehicle sensors data captured via on-board diagnostics.

The spatial information (SI) community has an opportunity to address major societal and scientific problems including public health, climate change, air pollution, transportation, and others. Beyond the significant contributions made by the SI community, more can be done by focusing the efforts of the community, and generalizing them. Focus can be achieved by an IMAGENET-like spatial information database and competition. Generalization can be achieved by solving spatio-temporal information problems in disciplines such as neuroscience, chemistry, biology, astronomy, and engineering.

Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer's disease, leading to "hyperexcitability" in neuronal circuits. In this study, we evaluated a novel hyperexcitation indicator (HI) for each brain region using a hybrid resting-state structural connectome to probe connectome-level excitation-inhibition balance in cognitively intact middle-aged apolipoprotein E (APOE) ε4 carriers with noncarriers (16 male/22 female in each group). Regression with three-way interactions (sex, age, and APOE-ε4 carrier status) to assess the effect of APOE-ε4 on excitation-inhibition balance within each sex and across an age range of 40-60 years yielded a significant shift toward higher HI in female carriers compared with noncarriers (beginning at 50 years). Hyperexcitation was insignificant in the male group. Further, in female carriers the degree of hyperexcitation exhibited significant positive correlation with working memory performance (evaluated via a virtual Morris Water task) in three regions: the left pars triangularis, left hippocampus, and left isthmus of cingulate gyrus. Increased excitation of memory-related circuits may be evidence of compensatory recruitment of neuronal resources for memory-focused activities. In sum, our results are consistent with known Alzheimer's disease sex differences; in that female APOE-ε4 carriers have globally disrupted excitation-inhibition balance that may confer greater vulnerability to disease neuropathology.

In recent years, Transportation Network Companies (TNC) such as Uber and Lyft have embraced ridesharing: a passenger who requests a ride may decide to save money in exchange for the inconvenience of sharing the ride with someone else and incurring a delay. When matching passengers, these services attempt to optimize cost savings. But a possible scenario is that while passenger A is matched to passenger B, if matched to passenger C then both A and C would have saved more money. This leads to the concept of “fairness” in ridesharing, which consists of finding the Nash equilibrium in a ridesharing plan. In this paper we compare the optimum plan (i.e., benefit maximized at a global level) and the fair plan in both static and dynamic contexts. We show that in contrast to the theoretical indications, the fair plan is almost optimum. Furthermore, the fairness concept may help attract more passengers to rideshare and thus further reduce vehicle miles traveled. If social preferences are included in the total benefit, we demonstrate that the optimum ridesharing plan may be unboundedly and predominantly unfair in a sense that will be formalized in this paper.

In this article, we survey the main achievements of moving objects with transportation modes that span the past decade. As an important kind of human behavior, transportation modes reflect characteristic movement features and enrich the mobility with informative knowledge. We make explicit comparisons with closely related work that investigates moving objects by incorporating into location-dependent semantics and descriptive attributes. An exhaustive survey is offered by considering the following aspects: 1) modeling and representing mobility data with motion modes; 2) answering spatio-temporal queries with transportation modes; 3) query optimization techniques; 4) predicting transportation modes from sensor data, e.g., GPS-enabled devices. Several new and emergent issues concerning transportation modes are proposed for future research.

The human brain is probably the most complex object in the universe, and also one of the least understood. For example, how the brain produces the mind and consciousness is a complete mystery. Nevertheless, the brain is amenable to measurements of various kinds that produce lots of data. It is a spatial object residing in the skull; it is also temporal in the sense that neurons communicate by signals that take traverse the brain network over time. In this paper we ask whether spatio-temporal data analysis can contribute to its understanding. Toward this goal we propose several research directions that are inspired by GIS work. However, these are just examples, and other work on moving objects in space or on networks is applicable.

The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks. EEG-based temporal dynamic functional connectomes are created based on 20 psychiatrically healthy participants’ EEG recordings during resting state and an emotion regulation task. Graph dissimilarity space embedding was applied to all the dynamic EEG connectomes. In order to visualize the learned manifold in a lower dimensional space, local neighborhood information is reconstructed via k-nearest neighbor-based nonlinear dimensionality reduction (NDR) and epsilon distance-based NDR. We showed that two neighborhood constructing approaches of NDR embed the manifold in a two-dimensional space, which we named Thought Chart. In Thought Chart, different task conditions represent distinct trajectories. Properties such as the distribution or average length in the 2-D space may serve as useful parameters to explore the underlying cognitive load and emotion processing during the complex task. In sum, this framework is a novel data-driven approach to the learning and visualization of underlying neurophysiological dynamics of complex functional brain data.

A mobile ad hoc network (MANET) database is a database that is stored in the peers of a MANET. The network is composed by a finite set of mobile peers that communicate with each other via short range wireless protocols, such as IEEE 802.11, Bluetooth, Zigbee, or Ultra Wide Band (UWB). These protocols provide broadband (typically tens of Mbps) but short-range (typically 10–100 m) wireless communication. On each mobile peer there is a local database that stores and manages a collection of data items, or reports. A report is a set of values sensed or entered by the user at a particular time, or otherwise obtained by a mobile peer. Often a report describes a physical resource such as an available parking slot. All the local databases maintained by the mobile peers form the MANET database. The peers communicate reports and queries to neighbors directly, and the reports and queries propagate by transitive multi-hop transmissions. Figure 1 below illustrates the definition. 10.1007/978-0-387-39940-9_220 http://www.springerlink.com.proxy.cc.uic.edu/content/n068351ju072v133...

In this paper, we deal with the resource search problem in a probabilistic setting. In a resource search problem, there are spatially located static resources and a mobile agent. The agent looks to obtain one of the resources while minimizing the cost. This cost may consist of different types of costs the agent has to pay, from travel time to the cost of obtaining a certain resource. We assume that the agent has no knowledge of exact availability of the resources in real-time, but some prior or partial data gives estimations of this information. This model applies to many situations that arise in urban transportation systems, such as drivers looking for street parking, taxis looking for new customers, and electric vehicles looking for charging stations. Our approach to the resource search problem only employs uncertain information about resource availability, minimizes the expected cost, and utilizes concepts from decision theory. A simulation that uses real-world data is used to compare our approach to alternatives.

Due to device limitations, a mobile database is often much smaller than its counterpart residing on servers and mainframes. A mobile database is managed by a Database Management System (DBMS). Again, due to resource constraints, such a system often has limited functionality compared to a full blown database management system. For example, mobile databases are single user systems, and therefore a concurrency control mechanism is not required. Other DBMS components such as query processing and recovery may also be limited.