Large language models have demonstrated powerful reasoning capabilities, and their integration with robotics has revolutionized human-computer interaction and automated task planning. However, LLMs are unaware of environmental knowledge and possible state changes in the environment during planning, which makes the generated tasks unexecutable, particularly when dealing with complex long-horizon tasks involving crowded objects and dynamic relations. In this paper, we propose a LLM-based robot task planning framework with support for environmental knowledge injection, which is called DRP(Decomposition-Reflection-Prediction). The DRP framework combines LLMs with rule-based task decomposition, multi-perspective reflection and environmental prediction to generate admissible actions for complex long-horizon tasks. We only leverage few-shot prompting to implement our framework, which avoids the need for additional model training work. Experiments on VirtualHome household task dataset show that the task plans generated by our method have improved the executability by 25.23%, the subgoal success rate by 64.29%, and the success rate by 58.06%, in comparison to state-of-the-art baseline methods. The complete code of our framework has been made public at https://github.com/lab-bj/taskplanning

Traffic flow prediction with trajectories is one core technology of Intelligent Transportation System, but also a challenging task, and the fundamental challenge is to effectively model the underlying causality of flows among roads, which are not only affected by vehicle driving preference, but also by road network structure features. To this end, this paper proposes a novel model, namely RNTrGNN(Road Network Enhanced Trajectory-based Graph Neural Network), which utilizes both vehicle trajectory and road network spatial features for traffic flow prediction. Specifically, RNTrGNN utilizes graph propagation of trajectory transition patterns to model the spatial traffic demand on the road network. Also, a Graph Attention Network(GAT) is used to incorporate road segment embedding into the traffic demand. Moreover, we design a traffic constraint module to take into account the effect of neighboring roads based on road distance. Extensive experiments conducted on three real-world taxi trajectory datasets show that our method can significantly outperform existing baseline models.

It aims to improve the efficiency of information collection and extraction in the current intelligent transportation system, and accurately mine the vehicle trajectory data By using Artificial Intelligence (AI) and Deep Learning methods, the trajectory data generated during vehicle driving are deeply mined and analyzed, and the characteristics of driving behavior of vehicle drivers are modeled and analyzed in detail. Then, a method of mining driving behavior characteristics based on Convolutional Neural Network (CNN) and vehicle trajectory is proposed. Based on the mathematical principle of wavelet packet and Least Square Support Vector Machine (LSSVM), a combined model of trajectory mining is constructed and applied to the short-term prediction of traffic flow. The traffic flow of Binjiang Road and Renmin Road in Guangzhou, Guangdong Province from August 19 to August 21, 2021 is predicted to verify the accuracy of the trajectory mining combined model. The results show that the combination model of data mining has good fitting effect, and the average accuracy is above 0.8. Besides, the effectiveness of the Deep Learning model in driver behavior classification is verified. The accuracy of the classification model is 75.2% for trajectory, and that is 76.8% for driver behavior characteristics. It is of great significance to effectively utilize the knowledge data in Intelligent Transportation System (ITS) and extract valuable information from it, which has certain reference value for the subsequent refined prediction of vehicle behavior.

The collaboration of Internet of Things (IoT) devices is promising nowadays to achieve complex requests in edge networks. In this setting, the functionalities of IoT devices are usually encapsulated as IoT services. A request can be fulfilled by the composition of data- or computation-intensive IoT services, which require to either consume a relatively large amount of sensory data or mandate a heavy computation capacity. Discovering functionally complementary IoT services, while satisfying their pre-specified spatial constraints, is a challenge, since certain IoT services may non-exist with respect to current IoT services deployment situation. To remedy this issue, we propose an energy-aware Data- and Computation-intensive service Migration and Scheduling mechanism (DCMS) to re-schedule certain services from their hosting devices to the ones within the geographical region prescribed by the request. Extensive experiments are conducted and evaluation results show that our DCMS is promising in reducing the energy consumption and average delay, in comparison with the state of the art's techniques.

The collaboration of contiguous Internet of Things (IoT) devices is envisioned to satisfy complex applications which are beyond the capacity of single devices. The functionalities of IoT devices are encapsulated as IoT services, and their collaboration is implemented in terms of IoT service composition. Considering the capacity occupancy, release, and consumption caused by the implementation of IoT services, their composition is challenging in capacity-dynamically fluctuating IoT networks. This paper proposes a dynamic IoT service composition mechanism with inter-service dependencies adopted to capture the dynamic changes of IoT devices, and this change is specified by various Quality-of-Service factors. IoT service composition is formalized under Computation Tree Logic specification with certain composite structures and dynamic dependencies, and this composition is formally achieved by an optimized model checking method. Extensive experiments are conducted on publicly available datasets, and evaluation results show that our technique outperforms the state-of-the-art's approaches in relevant performance metrics.

With the rapid growth of internet usage for enterprise-wide and cross-enterprise business applications (such as those in Electronic Commerce), workflow systems are gaining importance as an infrastructure for automating inter-organizational interactions. However, the traditional centralized workflow management technology can no longer meet the needs of current application services. For example, in e-commerce, cross-enterprise business applications may cause many security problems and cross-domain problems in the implementation of workflow. At the same time, due to the uncertainty and variability of environment and user requirements in practical applications, many business logics are difficult to be completely defined in advance. Therefore workflow models need to be immediately built or adjusted dynamically. Nowadays, distributed scheduling and decentralized control of workflow have become the emerging trend and are facing many challenges at the forefront of Internet development technology. In this paper, a distributed workflow control execution method based on “meta-process” is proposed. Specifically, we designed and implemented a decentralized distributed scheduling management system for workflow tasks. To manage and control the distributed scheduling of workflow, we constructed a “meta-process”, which can ensure the integrity of the control chain in the distributed scheduling process. Our system can efficiently handle the data state migration between task nodes and supports the dynamic adjustment of the workflow model. For validation, we simulated a large number of service scheme samples and applied them to the system, which proved that all service cases can be executed correctly. Therefore, the feasibility of this method is verified.

Business process model and notation BPMN2.0 is an industry standard in the field of business process management. In order not to destroy the association rules between the various tasks of the business process, and to schedule them in the cloud edge environment, improve the service quality of the business process, this paper proposes a method of segmentation and reconstruction of the BPMN2.0 model, based on the BMPN2.0 model of the original business process and its constraints of the cloud-edge environment, the original business process is divided and reconstructed into multiple sub-business processes, so that the reconstructed sub-business process conforms to the BPMN2.0 modeling standard and is actually usable, providing basis for the scheduling of business processes in the cloud-edge environment. Through example verification and experimental analysis, the effectiveness of the segmentation reconstruction method in this paper is verified.

In this paper, we describe PACOL (Panoramic-Collaboration), a decentralized and multi-layered architecture of runtime environment for service collaboration. We illustrate by a case study why it is designed as a decentralized control architecture, and why it is designed to support multi-tenants and service solution evolution and continuous optimization. Preliminary case analysis indicates that PACOL could be a feasible proposition to realise cross-domain, reliable and optimized service collaboration for service collaboration towards Internet of Services.

For the national grid power quality monitoring system, the effective integration of monitoring terminals, the master stations of each network and the province and the state grid data center work together, the reasonable placement of the monitoring data in the system, and the relief of the calculation pressure of the state grid data center are the project research focus. From a global perspective, this paper models and describes the data placement problem of the harmonic monitoring system, and proposes a data placement strategy based on an improved particle swarm optimization algorithm. This paper proposes an initial population generation algorithm based on Markov random walk, which enables individuals in the initial population to have a certain degree of clustering accuracy and strong diversity. The initial population generation algorithm cooperates with the particle swarm optimization algorithm, which effectively enhances the algorithm's optimization ability. Through comparative experiments with traditional data placement strategies, the experimental results show that the data placement strategy based on improved particle swarm optimization algorithm has higher efficiency.

With the widespread application of AIS (Automatic Ship Identification System), ship trajectory data is being collected and becoming increasingly available. Consequently, a lot of ship trajectory data applications have become feasible that mine the value from the data. In this paper, based on massive ship trajectory data, we aim to classify two kinds of ship stay behavior for recognizing different areas in the port, namely berth and anchorage. The traditional trajectory data classification model mainly distinguishes the moving and staying state of moving objects, but there is little research on the classification of different kinds of stay behavior, especially for ship stay behavior classification. In this work, we propose an extraction algorithm based on the cloud storage and distributed computing frameworks to extract classification features by analyzing the behavioral characteristics of ships at berths and anchors. Second, with the consideration of the low precision, drift and sparsity characteristics of ship trajectory data, we design a series of experiments based on ten-fold cross-validation method for evaluating five classical classification models, such as XGBoost, Random Forest and so on. Third, experimental verifications of various classification models are conducted based on a real ship trajectory dataset, and the effectiveness of different models for recognizing ship stay area are compared.

Abundant sensors in various types are widely used in modern cities to comprehend the current situations in real time. The raw data in open conditions is always in low quality and is hard to employ directly due to its imperfect or missing records. Traditional data preprocessing methods focus on the offline historical data and remain a dilemma between the efficiency and the overhead. In this paper, a data harmonization service DS-Harmonizer is proposed on spatiotemporal data stream in the edge computing environment. Through the online cleaning and complementing steps of the hierarchical service instances, the records’ validity and continuity can be guaranteed in an efficient way. On the simulated data in a practical project, the service shows high performance, low latency, and acceptable precision in extensive conditions.

In Big Data era, continuous data with low latency and high throughput makes high-availability essential for stream computing. Traditional availability guarantee is tightly-coupled and inefficient for customization and reuse. In this paper, a framework is proposed to improve the availability of stream computing, in which basic functions are provided as general services like reliable point-to-point communication and distributed status management. With its help, high-level patterns can be achieved effectively. Comprehensive experiments have been designed and evaluated to show the availability improvement with acceptable extra overheads.

With the continuous expansion of the scope of traffic sensor networks, traffic sensory data becomes widely available and is continuously being produced. Traffic sensory data gathered by large amounts of sensors show the massive, continuous, streaming and spatio-temporal characteristics compared to traditional traffic data. In order to satisfy the requirements of different applications with these data, we need to have the capability of processing both real-time traffic sensory data in streaming way and historical traffic sensory data in large amount. In this paper, we present an approach and corresponding system for traffic sensory data processing, which is designed to combine spatio-temporal data partition, parallel pipeline processing and stream computing to support traffic sensory data processing in a scalable architecture with real-time guarantee. Three types of applications in real project are also described in detail to show the significant effect gains of the proposed approach and system. Numerical evaluations according to experiment results also show that the system can gain high performance in terms of the processing time of traffic sensory data stream.

With the continuous expansion of the scope of traffic sensor networks, traffic sensor data becomes widely available and large in amount. Traffic sensor data gathered by large amounts of sensors shows the massive, continuous, streaming and spatio-temporal characteristics compared to traditional traffic data. In order to satisfy the requirements of different applications in Intelligent Transportation Systems (ITS), we need to have the capability of real-time processing over both streaming and historical traffic sensor data. In this paper, we present DeCloud4SD, an integrated processing platform for traffic sensor data, which is designed to provide services for receiving, storing, acquiring and computing traffic sensor data in a scalable architecture with real-time guarantee. Three types of applications using DeCloud4SD in a real ITS project are also described in detail. Through the analysis of these applications, we can see that DeCloud4SD can ensure: 1) scalable and customizable traffic sensor data gathering and computing, 2) rapid application development and deployment using a MapReduce-like model, 3) seamless integration with existing relational data sources and applications.

SUMMARY Under distributed Cloud environment, the real‐time and continuous data stream makes the availability during processing essential but expensive. For aggregation tasks of data stream processing systems, traditional replica‐based high‐availability mechanisms require large overheads at run‐time and long recovery latency at fail‐time, because of specific nature of aggregations. In this paper, we focus on the typical quantile tasks and propose a feature‐based high‐availability mechanism to reduce related overhead and the latency. With the help of monitor module, quantile feature is maintained incrementally through histogram synopsis over time‐based sliding window, and the failed quantile tasks can be recovered precisely with high probability in an efficient way. The effectiveness has been analyzed theoretically, and meanwhile, the acceptable tradeoff between overheads and performance has been demonstrated by comprehensive experiments on both synthetic and real data. Copyright © 2013 John Wiley & Sons, Ltd.

Data process in Cloud or IoT (Internet of Things) sometimes implies continuous real-time queries as data streams. In order to acquire extreme value of data stream over time-based sliding window, traditional approaches computed the exact solution through vast space especially under ultra circumstances like high-rate or high-concurrency. In this paper, we design space-bounded synopsis data structure and extreme aggregation algorithm to get approximate solution by finite extreme candidates over time sliding window, whose validity can be theoretically guaranteed. Comprehensive experiments over synthetic and real data set are designed to analyze the tradeoff between accuracy and overhead, which also illustrate the efficiency.

Cloud computing provides virtualized, dynamically-scalable computing power. At the same time, reduction of cost is also considered as an important advantage of cloud computing. Data integration can notably benefit from cloud computing because integrating data is usually an expensive task. However, existing optimization techniques pay less attention on the fact that different execution plans of the same data integration application generate different usage costs while cloud computing provides good enough performance, so this paper introduces the cost optimization of cloud-based data integration system. The data integration system's data service layer facilitates accessing and composing information from a range of enterprise data sources through data service composition. In addition, two task scheduling algorithms for parallel part and non-parallel part are proposed to minimize the usage cost required to complete the execution of composite data service when computational capability provided by cloud computing is charged. Both of the two can obtain optimal plans in polynomial time. Experiments with the system indicate that our algorithms can lead to significant cost saving over more straightforward techniques.

Today, when the Internet is concerned, one seldom thinks about the network itself and the TCP/IP suite only. With the boom of Web applications, Internet services, new business models and innovative computing paradigms (e.g. software-as-a-service model and cloud computing), the Internet has evolved into an indispensable social infrastructure and the largest computing platform of the world as well. It remains a challenging issue herein how end users can "program" and share their own dependable internet-based applications with cyber services and within virtual communities in cyberspace. In the first part of this tutorial, we discuss the above-stated trends, identify the bottlenecks of the contemporary service computing approaches and Web service technologies, in particular when data and discrete events are concerned, and raise some considerations for further developments.

This paper proposes a novel approach to service management and monitoring on the basis of service collaboration relations that can be extracted from service composition history. An undirected and weighted service collaboration network is constructed therefore. On the network, two abstractions are defined: service centrality, which measures a service's collaboration capability comparing to other services for a long period, and service activeness, which measures the frequency of a service's collaboration with others for a short period. The capability of service management among Open APIs is investigated and demonstrated. Experimental data are collected from Programmable Web with the period from 2005-9-14 to 2009-9-21. Experiments show that the defined metrics can properly reflect the service properties.

The recent trend of "everything as a service", is fostering the Internet of services (IoS) and will promote the emergence of service operation. Service operation provides the business and technical base for advanced business models. Service monitoring is a crucial issue for the guaranteed service delivery in service operation. However, most service monitoring approaches are specific and focus on IT level. The challenge of how to monitor diverse business aspect of service simply and flexibly needs to be overcome. In this paper, we present a model-driven approach for service monitoring from business perspective. In the approach, a business-oriented service monitoring metamodel is put forward to define various monitoring models on demand. The model can flexibly specify the monitored information in both business level and IT level and the monitoring process. Also, a service monitor is implemented through model-driven way which brings the scalability to its implementation.

Web services are rapidly emerging as a popular standard technology for sharing data and functionality among heterogeneous systems. Service providers and consumers are loosely coupled and distributed across the network, either within an organization or across organizational boundaries, and therefore, performance becomes a major concern in such a distributed environment. Furthermore, XML is widely used as message format for service providers and consumers in Web services environment. XML message packaging and parsing brings extra overhead to both ends. Web services response latency, as well as throughput, is becoming a bottleneck problem. In this paper, We propose a consistency-preserving mechanism for Web services response caching, which reduces the volume of data transmitted without semantic interpretation of service requests or responses, and accelerates the services response finally. It achieves this reduction through the use of cryptographic hashing to detect similarities with previous results. Experiments with an initial prototype called SigsitAcclerator indicate that our mechanism can lead to significant performance improvement over more straightforward techniques.

The growing need for an integrated view of scientific information from different sources has led to the need for scientific information integration, and on the other hand, SOA is one most prevailing technology for its advantages on solving integration problems. In this paper, we argue that the deployment of SOA in an organization should be business domain-specific, and propose an approach called CAFISE-S, which introduces SOA into scientific information integration from a business view-aspect. Business service is put forward as basic elements in CAFISE-S to model business context and IT services coherently in a semantic way. Based on business service, CAFISE-S provides a business domain-specific modeling method for specification of information services, and then supports business-oriented publication, management and usage of information services. The implementation of CAFISE-S platform and an application of CAFISE-S in a real-world project of scientific information integration are also presented in this paper.

The growing need for an integrated view of information from different sources has led to the concept of information resource integration. In this paper, a Service Oriented approach for Information Resource Integration called SOIRI is introduced. Information services are put forward as basic elements in the SOIRI approach to realize information resource integration. A design method for information services is put forward, the metadata based management of information services is discussed, and a mediation way for information services access is given, also the SOIRI system for the approach is implemented, all of which contribute to the SOIRI approach. A real project utilizing the SOIRI approach is also discussed in the paper.