There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Location monitoring is an important issue for real time management of mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial continuous queries on moving objects in a centralized location monitoring system. Surprisingly, very few have promoted a distributed approach to real-time location monitoring. In this paper we present a distributed and scalable solution to processing continuously moving queries on moving objects and describe the design of MobiEyes, a distributed real-time location monitoring system in a mobile environment. Mobieyes utilizes the computational power at mobile objects, leading to significant savings in terms of server load and messaging cost when compared to solutions relying on central processing of location information at the server. We introduce a set of optimization techniques, such as Lazy Query Propagation, Query Grouping, and Safe Periods, to constrict the amount of computations handled by the moving objects and to enhance the performance and system utilization of Mobieyes. We also provide a simulation model in a mobile setup to study the scalability of the MobiEyes distributed location monitoring approach with regard to server load, messaging cost, and amount of computation required on the mobile objects.

MobiEyes: Distributed processing of continuously moving queries on moving objects in a mobile system

An integrated GIS platform architecture for spatiotemporal big data

With the widespread deployment of mobile devices with positioning capabilities, increasingly massive volumes of trajectory data are being collected that capture the movements of people and vehicles. This data enables co-movement pattern detection, which is important in applications such as trajectory compression and future-movement prediction. Existing co-movement pattern detection studies generally consider historical data and thus propose offline algorithms. However, applications such as future movement prediction need real-time processing over streaming trajectories. Thus, we investigate real-time distributed co-movement pattern detection over streaming trajectories.Existing off-line methods assume that all data is available when the processing starts. Nevertheless, in a streaming setting, unbounded data arrives in real time, making pattern detection challenging. To this end, we propose a framework based on Apache Flink, which is designed for efficient distributed streaming data processing. The framework encompasses two phases: clustering and pattern enumeration. To accelerate the clustering, we use a range join based on two-layer indexing, and provide techniques that eliminate unnecessary verifications. To perform pattern enumeration efficiently, we present two methods FBA and VBA that utilize id-based partitioning. When coupled with bit compression and candidate-based enumeration techniques, we reduce the enumeration cost from exponential to linear. Extensive experiments offer insight into the efficiency of the proposed framework and its constituent techniques compared with existing methods.

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Real-time distributed co-movement pattern detection on streaming trajectories

A transit ridership study is an essential part of sustainability, and can provide a deep understanding of people’s travel patterns for efficient transportation development and urbanization. However, there is a lack of empirical studies comparing subway and taxi services, and their interactions within a city, that is to say, the interdependent transportation networks. Incorporating new data, this study aims to examine the spatial variation of urban taxi ridership due to the impacts of a new subway line operation opened in 2014 in Wuxi, China. We examine the spatial patterns and interactions of ridership in Wuxi by integrating taxi trajectory from GPS data and subway data from continuously collected fare transactions. The results indicated that the demand for taxi and subway usage is quite elastic with respect to both location and time, and the new subway’s opening had more influence on areas adjacent to subway stations and urban center-suburban travel. Furthermore, increases in travel time and distance would increase the demand for subway, while taxi trips largely represented movements for those locations that the subway could not reach. This paper betters the understanding of travel patterns through large volumes of transportation data for sustainable urbanization policy design.

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Examining the interaction of taxi and subway ridership for sustainable urbanization

With the rapid development of mobile data acquisition technology, the volume of available spatial data is growing at an increasingly fast pace. The real-time processing of big spatial data has become a research frontier in the field of Geographic Information Systems (GIS). To cope with these highly dynamic data, we aim to reduce the time complexity of data updating by modifying the traditional spatial index. However, existing algorithms and data structures are based on single work nodes, which are incapable of handling the required high numbers and update rates of moving objects. In this paper, we present a distributed spatial index based on Apache Storm, an open-source distributed real-time computation system. Using this approach, we compare the range and K-nearest neighbor (KNN) query efficiency of four spatial indexes on a single dataset and introduce a method of performing spatial joins between two moving datasets. In particular, we build a secondary distributed index for spatial join queries based on the grid-partition index. Finally, a series of experiments are presented to explore the factors that affect the performance of the distributed index and to demonstrate the feasibility of the proposed distributed index based on Storm. As a real-world application, this approach has been integrated into an information system that provides real-time traffic decision support.

https://www.mdpi.com/2220-9964/5/10/178/pdf

Real-Time Spatial Queries for Moving Objects Using Storm Topology

Conformal Geometric Algebra has been introduced into geographic information science as a mathematical theory because of its advantages in terms of uniform multidimensional representation and computation. The traditional intersection computation between two geometric objects of different types is not unified. In this study, we propose algorithms based on Conformal Geometric Algebra to determine the spatial relationships between geographic objects in a unified manner. The unified representation and intersection computation can be realized for geometric objects of different dimensions. Different basic judgment rules are provided for different simple geometries. The algorithms are designed and implemented using MapReduce to improve the efficiency of the algorithms. From the results of several experiments we provide, the correctness and effectiveness of the algorithms can be verified.

https://downloads.hindawi.com/journals/mpe/2016/7412373.pdf

Unified Spatial Intersection Algorithms Based on Conformal Geometric Algebra

The article is devoted to the analysis of the vocal cycle of the modern Ukrainian composer "Quiet Songs", which is considered in the context of Ukrainian music of the 1970s. The main features of its poetics as a transitional phenomenon between avant-garde and postmodernism are revealed: meditativeness, uncertainty, blurring of the composition, variability of the rhythmic structure, intertextuality, polystylistic, reflexivity, self-absorption, etc. It is concluded that Silvestrov went through a complex evolution from avant-garde experiments to quiet simplicity, to small vocal form and poetics of introspection. He created a sound model of the world, immersed in contemplation and reflection, understood as the path of self-awareness, tragic and beautiful at the same time.

Postmodernism in Ukrainian music: “Quiet Songs” by Valentyn Silvestrov

Speech Emotion Recognition (SER) typically relies on utterance-level solutions. However, emotions conveyed through speech should be considered as discrete speech events with definite temporal boundaries, rather than attributes of the entire utterance. To reflect the fine-grained nature of speech emotions, we propose a new task: Speech Emotion Diarization (SED). Just as Speaker Diarization answers the question of"Who speaks when?", Speech Emotion Diarization answers the question of"Which emotion appears when?". To facilitate the evaluation of the performance and establish a common benchmark for researchers, we introduce the Zaion Emotion Dataset (ZED), an openly accessible speech emotion dataset that includes non-acted emotions recorded in real-life conditions, along with manually-annotated boundaries of emotion segments within the utterance. We provide competitive baselines and open-source the code and the pre-trained models.

Speech Emotion Diarization: Which Emotion Appears When?

Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then improve the accuracy of traffic violation prediction. Therefore, we propose a GATR (graph attention network based on road network) model to predict the spatiotemporal distribution of traffic violations, which adopts a graph attention network model combined with historical traffic violation features, external environmental features, and urban functional features. Experiments show that the GATR model can express the spatiotemporal distribution pattern of traffic violations more clearly and has higher prediction accuracy (RMSE = 1.7078) than Conv-LSTM (RMSE = 1.9180). The verification of the GATR model based on GNN Explainer shows the subgraph of the road network and the influence degree of features, which proves GATR is reasonable. GATR can provide an important reference for prevention and control of traffic violations and improve traffic safety.

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Yingzhi Wang

Papers

Real-Time Spatial Queries for Moving Objects Using Storm Topology

Unified Spatial Intersection Algorithms Based on Conformal Geometric Algebra

Postmodernism in Ukrainian music: “Quiet Songs” by Valentyn Silvestrov

Speech Emotion Diarization: Which Emotion Appears When?

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network