Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks

Spatiotemporal big data have multisource, heterogeneous, high-dimensional and spatiotemporal associations. Due to the limited computing and network resources, while the spatiotemporal data to be rendered are large and dynamic, efficient visual analysis has always been a popular topic and has had difficulty in the research of spatiotemporal big data. As one of the important means of big data visualization, thermal maps play an important role in expressing data flow, information flow, and trajectory flow. At the same time, the development of a distributed computing framework also provides technical support for the online calculation and visualization of spatiotemporal data streams. In response to the above problems, this paper designs and implements GeoMapViz, a distributed management based on massive spatiotemporal data streams and a multiscale geographic spatial visualization framework, which is oriented by the expression of thermal maps of massive point datasets. First, based on the concept of the tile pyramid model and spatiotemporal cube, we propose a thermal map sequential tile pyramid (TS_Tile) model, which realizes scalable storage and efficient retrieval of data flow. GeoMapViz adopts a high-performance Flink stream computing cluster to implement the large-scale parallel construction of hierarchical tile pyramids, implements distributed storage and index construction of data based on HBase and Geomesa, and uses Geoserver to manage the map service to provide a spatiotemporal range query interface. Finally, through using an open dataset as a system simulation test, the results show that the TS_Tile model can effectively organize large-scale, time-space and multidimensional thermal map data, and the query and visualization of the heatmap can reach a subsecond response. Furthermore, GeoMapViz supports the integration of the thermal map and original flow and provides a feasible solution for the visual analysis of large-scale spatiotemporal data.

Detecting anomalies in spatio-temporal flow data by constructing dynamic neighbourhoods

With an increasing number of processing elements (PEs) integrated on a single chip, fault-tolerant techniques are critical to ensure the reliability of such complex systems. In current reconfigurable architectures, redundant PEs are utilized for fault tolerance. In the presence of faulty PEs, the physical topologies of various chips may be different, so the concept of virtual topology from network embedding problem has been used to alleviate the burden for the operating systems. With limited hardware resources, how to reconfigure a system into the most effective virtual topology such that the maximum repair rate can be reached presents a significant challenge. In this article, a new approach using a maximum flow (MF) algorithm is proposed for an efficient topology reconfiguration in reconfigurable architectures. In this approach, topology reconfiguration is converted into a network flow problem by constructing a directed graph; the solution is then found by using the MF algorithm. This approach optimizes the use of spare PEs with minimal impacts on area, throughput, and delay, and thus it significantly improves the repair rate of faulty PEs. In addition, it achieves a polynomial reconfiguration time. Experimental results show that compared to previous methods, the MF approach increases the probability to repair faulty PEs by up to 50% using the same redundant resources. Compared to a fault-free system, the throughput only decreases by less than 2.5% and latency increases by less than 4%. To consider various types of PEs in a practical application, a cost factor is introduced into the MF algorithm. An enhanced approach using a minimum-cost MF algorithm is further shown to be efficient in the fault-tolerant reconfiguration of heterogeneous reconfigurable architectures.

The ULowell Data Flow Project combines dataflow hardware, dataflow software, and a data flow development environment. The hardware component, a data flow coprocessor card, utilizes seven NEC 7281 Image Pipelined Processors. Each chip models a static dataflow computation and is programmable. The coprocessor was designed and implemented by ULowell personnel and uses an AMIGA 2000 as a host. The software component currently consists of a library of data flow image processing algorithms and an evolving software development environment. Currently the image processing library includes typical imaging operations such as: edge enhancement, filtering, histogramming, and affine transformation. The software development environment (SDE) is depicted in Figure 1. The SDE contains a Data Flow Graph Editor which programmers use to design their data flow code. The data flow algorithm can then be converted directly into NEC data flow assembly code which is then assembled using the ULowell Data Flow Assembler. The assembler differs from NEC's assembler in that it can produce relocatable code. The object code output of the assembler is given to the Data Flow Resource Manager. The resource manager monitors the available resources within the chips. Thus, if a single process does not totally utilize a chip's table space or memory, then a second process may be concurrently loaded onto the same chip. The ability to have multiple data flow processes coexisting within a single chip is unique to the ULowell environment. The data flow resource manager can download and monitor code to either the hardware or to a software simulator/debugger. The simulator/debugger can monitor and trace data flow tokens as they proceed through the chip. Output of the simulator/debugger can be fed back into the data flow graph editior to visually trace token flow through the original program design. The last component of our SDE is a High Level Language translator which can compile programs written in SISAL, a high level language for parallel computation, down to NEC 7281 assembly language. We have already implemented the relocatable assembler, and the data flow resource manager. Prototype versions of the high level translator, simulator/debugger and graph/editor are currently being implemented.

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

With increasing numbers of flights worldwide and a continuing rise in airport traffic, air-traffic management is faced with a number of challenges. These include monitoring, reporting, planning, and problem analysis of past and current air traffic, e.g., to identify hotspots, minimize delays, or to optimize sector assignments to air-traffic controllers. To cope with these challenges, cyber worlds can be used for interactive visual analysis and analytical reasoning based on aircraft trajectory data. However, with growing data size and complexity, visualization requires high computational efficiency to process that data within real-time constraints. This paper presents a technique for real-time animated visualization of massive trajectory data. It enables (1) interactive spatio-temporal filtering, (2) generic mapping of trajectory attributes to geometric representations and appearance, and (3) real-time rendering within 3D virtual environments such as virtual 3D airport or 3D city models. Different visualization metaphors can be efficiently built upon this technique such as temporal focus+context, density maps, or overview+detail methods. As a general-purpose visualization technique, it can be applied to general 3D and 3+1D trajectory data, e.g., traffic movement data, geo-referenced networks, or spatio-temporal data, and it supports related visual analytics and data mining tasks within cyber worlds.

Task scheduling is crucial for offering the users of an operating system the impression that the system’s response is direct and in real time. However, it is a complicated process that is characterized by vagueness and uncertainty. A solution to this problem is the usage of fuzzy logic. Therefore, in this paper a rule-based fuzzy scheduling method, which considers both the execution time and the waiting time for each task, is presented. Fuzzy sets are used to describe both criteria. The operation of the presented scheduling method is based on a rule-based reasoner that decides dynamically about the priority of the tasks that wait to be executed. This reasoner is triggered each time a change occurs (i.e. the execution of a task ends, a new task arrives etc.). It has been compared with Short Job First (SJF) and First Come First Served (FCFS) scheduling algorithms for a large number of different sets of tasks. The results showed that the presented fuzzy scheduling algorithm in all the cases has the same or almost the same average waiting time with SJF, which is considered as the scheduling algorithm with the best (minimum) average waiting time. However, the presented fuzzy scheduling algorithm ensures that priority is given not only to tasks with short execution time, but also to tasks that remain into the queue waiting to be executed for a long time. In this way, the starvation, which may be caused by SJF algorithm, is eliminated.

As people’s demand for application services continues to grow, traditional TCP and IP network architectures face many security and mobility challenges. In order to completely eliminate these problems, scientists have proposed a representative new Internet architecture, such as a content center network. Each node in the content center network has a data storage function. In a limited cache area, effective content is cached through collaboration between nodes so that users can retrieve the required data faster. Double-cache collaboration means that peers on the network can not only store and retrieve data from their own cache, but also store and retrieve data from the caches of other peers. Although this affects the cache hit rate, it can improve cache utilization and improve network performance. Taking multimedia works as an artistic whole, it analyzes the movement form and work characteristics of multimedia digital artworks, movement form and style, and movement form and plot relationship of works on the existing basis, and explores the entire movement design of multimedia works. Based on distributed index technology and R tree batch loading technology, a distributed index based on spatio-temporal data flow is proposed. The index uses a two-layer distributed index structure composed of an external B+ tree and an internal R tree to achieve effective storage of a large amount of spatio-temporal data, and provide low-latency, high-concurrency indexing services for the external world.

As a frontier research topic in the field of scientific visualization, trajectory data visualization extracts valuable patterns and knowledge from trajectory data for decision support via spatiotemporal trajectory visualization techniques. We propose the concept of multivariate trajectory data and interpret two categories of attributes that are based on geographical space and abstract space. Properly analyzing multivariate trajectory data depends on many factors such as visualization task and data sparsity. Therefore, we generalize rich interactions to explore the evolution of trajectory events and transform the issue into a more intelligibly perceptual task, which derives our discussion regarding advantages and limitations of the analytical methods. This review endeavors to provide a quick and thorough cognition and comprehension with regard to fundamental features and numerous outcomes in visual analytics for trajectory data, seeks to promote comparisons and criticisms about the descriptive framework for multivariate spatiotemporal trajectory data visualization, and aims to encourage the exploration of emerging methods and techniques.

Scheduling semi-conductor manufacturing process systems is a complicated and difficult job, due to such characteristics as re-entry into manufacturing processes, high uncertainty of processes, and products and technologies changing rapidly. The researchers have carried out many studies to find efficient techniques for semi-conductor manufacturing systems with a view to accomplishing the goals of systems, such as saving cycling time and increasing production quantity per unit time. The production flow in the semi-conductor industry has the most unique characteristics, which makes it difficult to plan production and to schedule semi-conductor manufacturing. Currently, the scheduling methods in semi-conductor assembly processes follow the dispatching rule on a simple first come first serve (FCFS) basis, and a backlog is operated as a buffer based on the daily production quantity. In this study, therefore, we will apply various dispatching rules on a real-time basis and verify the effect and result of exact scheduling through simulation, based on the assumption that competitive advantages in production come from efficient inventory control and exact scheduling.

Most of the existing packet scheduling mechanisms of the wireless sensor network use First Come First Served (FCFS) non pre emptive priority and pre emptive priority scheduling algorithms. The above algorithms have high processing overhead and also long end-to-end data transmission delay. In FCFS concept the data packet which is entering the node first will go out first from the node, and the packet which will enter last will leave at last. But in FCFS scheduling of real time data packets coming to the node have to wait for a long time period.In non pre emptive priority scheduling algorithm there is starvation of real time data packets because once the processor enters the running state, it will not allow remove until it is completed, so there is starvation of real time data packets. In pre emptive scheduling, starvation of non real time data packets, due to continuous arrival of real time data. Therefore the data packets are to be schedule in multilevel queue. But the multilevel queue scheduling scheme is not suitable for dynamic inputs, and hence the scheme is designed for dynamically change in the inputs. The Dynamic Multilevel Priority (DMP) packet scheduling is the scheme for dynamically changes in the inputs. In this scheme each node except the last level of the virtual hierarchy in the zone based topology of wireless sensor network has three levels of priority queues. Real time data packets are placed into highest priority queue and can preempt the data packets in the other queues. Non real time data packets are placed into other two queues based on threshold of their estimated processing time. The leaf node have two queues, one for real time data packet and another for non real time data packet since they do not receive data from other nodes and thus reduces end to end delay. This scheme reduces the average waiting time and end-to -end delay of data packets.

Visualization has become a valuable means for data exploration and analysis. Interactive visualization combines expressive graphical representations and effective user interaction. Although interaction is an important component of visualization approaches, much of the visualization literature tends to pay more attention to the graphical representation than to interaction. The goal of this work is to strengthen the interaction side of visualization. Based on a brief review of general aspects of interaction, we develop an interaction-oriented view on visualization. This view comprises five key aspects: the data, the tasks, the technology, the human, as well as the implementation. Picking up these aspects individually, we elaborate several interaction methods for visualization. We introduce a multi-threading architecture for efficient interactive exploration. We present interaction techniques for different types of data e.g., multivariate data, spatio-temporal data, graphs) and different visualization tasks (e.g., exploratory navigation, visual comparison, visual editing). With respect to technology, we illustrate approaches that utilize modern interaction modalities (e.g., touch, tangibles, proxemics) as well as classic ones. While the human is important throughout this work, we also consider automatic methods to assist the interactive part. In addition to solutions for individual problems, a major contribution of this work is the overarching view of interaction in visualization as a whole. This includes a critical discussion of interaction, the identification of links between the key aspects of interaction, and the formulation of research topics for future work with a focus on interaction.

Cloud computing has become inevitable in own way with its offering multi-services, deployment, resource provisioning, data management, etc. Efficient resource consumption proves the dignity of a reliable cloud service provider in the market. However, the cloud environment often witnesses the uncertainties with respect to the resource provisioning. The most significant uncertainty encountered is the fault that paves way for a failure in the total process. Existing methods fail to incorporate such uncertainty into the picture leading to performance degradation of the cloud service requested by cloud user. The proposed scheme, robust fault detection and recuperation (RFDR), is a reactive fault-tolerant strategy that monitors the system continuously and recovers it from failures caused by VMs, host, and PEs. The proposed mechanism is run through baseline scheduling algorithm such as first come first service (FCFS), shortest job first (SJF), priority scheduling (PS), min-min, and max–min scheduling for the performance evaluation. The experiments indicate that the proposed method efficiently handles the fault detection and recover with 99% of resource availability and reliability.

The appearance of simultaneous multithreading (SMT) processors, offering instruction-level parallelism and increasing hardware resources utilization, permits the execution of various instructions streams at the same time. A particular case is the Intel SMT implementation (Hyper-Threading Technology) where a processor can execute two instructions streams independently. Due to hardware resources sharing in a Hyper-Threading processor, both instruction streams can contend for the same execution resources depending on the instruction execution units utilization.As other works in this area have demonstrated, we have observed that the system throughput can be improved when the execution resources used by each thread are contemplated in their scheduling. Starting from the evaluation results, we propose a Thread Coloring philosophy that designs a way to manage information about resources utilization (hardware and software resources) per each thread. This policy permits the scheduler to select threads that will perform better with less resource contention, besides including OS upper layers in the resource management. The proposed Thread Coloring scheme establishes the bases of processor and OS cooperation, sharing resources utilization information and providing a form of controlling logical processor thread assignment taking into account possible resource contention.