Event Processing and Real-Time Monitoring over Streaming Traffic Data

Patroumpas, Kostas; Sellis, Timos

doi:10.1007/978-3-642-29247-7_10

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…The inference engine evaluates these pre-defined rules to define a situation. We formulated the rules based on an extensive analysis of IoT traffic monitoring scenario applications [ 62 , 63 , 64 , 65 ]. We adopt the Mamdani fuzzy inference algorithm [ 66 ] for defining our situations for IoT applications.…”

Section: Situation Aware Iot Data Generation Frameworkmentioning

confidence: 99%

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms

Mondal

Jayaraman

Haghighi

et al. 2022

Sensors

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With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.

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Section: Situation Aware Iot Data Generation Frameworkmentioning

confidence: 99%

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms

Mondal

Jayaraman

Haghighi

et al. 2022

Sensors

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“…Traffic monitoring has been a field of great interest in the complex event and stream processing community [16], [78]. However, these works detect events based on statically defined rules so any updates to the traffic conditions overtime is not taken into account.…”

Section: Distributed Stream Processingmentioning

confidence: 99%

Analysis of Spatiotemporal Data

2012

Spatial Data Analysis in Ecology and Agriculture Using R

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In this thesis, we address major challenges in spatiotemporal data mining. Spatiotemporal data are data that relate to both space and time. Spatiotemporal data mining is the process of discovering patterns and extracting knowledge from spatiotemporal data. Examples of spatiotemporal data are trajectories of moving vehicles (i.e., GPS equipped buses or taxis) that move around the city reporting their GPS location along with the timestamp of each measurement, or animals' trajectories that are particularly important for zoologists in order to analyze the ecological behavior. The ubiquitous mobile devices generate massive datasets of GPS trajectories that are particularly difficult to interpret or analyse. Examples of spatiotemporal data mining include the extraction of moving patterns among a collection of moving objects. Another example of trajectory data mining, focusing in urban environments, is the traffic modelling. This refers to the analysis of the real-time traffic reports in order to extract the current traffic condition across the road network and the forecasting of the future traffic condition. Detecting and forecasting the traffic condition is particularly important in order to perform travel time estimation of a given path. Also, traditional centralized systems are unable to cope with the current massive-scale of spatiotemporal data, mainly because a single host is not able to receive and process all incoming data in real-time. Due to this reason, there is a shift in using Distributed Stream Processing Systems (DSPS).The contributions in this thesis focus on three main topic areas: (i) discovering corridors from GPS trajectories, (ii) hybrid travel time estimation and (iii) distributed complex event processing. Bellow we provide a short description of each of these topics: Discovering corridors from GPS trajectoriesWe aim at detecting paths that are frequently followed by the moving objects in the spatiotemporal database. Our main hypothesis is that even if the moving objects have different origins and destinations they are following common paths. The detections of such common paths is particularly demanding due to the complex nature of the spatiotemporal data. In this thesis we proposed a pipelined algorithm that discovers such frequent paths. The proposed technique first discovers areas that are frequently followed together by the moving objects and then discovers frequent paths following a greedy algorithm. Hybrid travel time estimationThis thesis also, focuses on the analysis of traffic condition across the road network and more specifically with the travel time estimation problem from one location of the road network to another given the path that will be followed and the time of departure. This problem is particularly challenging since the travel time depends on multiple factors that are difficult to be modeled. In this thesis we proposed a hybrid travel time estimation technique, named HTTE. Our technique performs travel time estimation for a given path (map-matched in the road network), using da...

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“…For their very nature, this kind of data is often very large and the increase in volume is often too rapid to be properly dealt with using traditional methods. There are many applications of spatial data where these problems are relevant such as, transportation studies (Patroumpas and Sellis 2012; Putatunda 2017), medical images (Shaiboun and Shaheen 2016), and raster images from satellites (Gertz et al 2006). In this area of research, the main focus so far has been on the management of huge flows of geo‐referenced information (Fox et al 2006; Gali et al 2014) to improve the performances in storing and querying databases (Zhang 2006; Thakur et al 2015) and in developing new tools for their management, especially in view of the increasing speed with which data are collected (Raman and Ali 2010; Armstrong, Wang, and Zhang 2019).…”

Section: Introductionmentioning

confidence: 99%

Recursive Estimation of the Spatial Error Model

Ghiringhelli

Piras

Arbia

et al. 2022

Geographical Analysis

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In this paper, we propose a recursive approach to estimate the spatial error model. We compare the suggested methodology with standard estimation procedures and we report a set of Monte Carlo experiments which show that the recursive approach substantially reduces the computational effort affecting the precision of the estimators within reasonable limits. The proposed technique can prove helpful when applied to real-time streams of geographical data that are becoming increasingly available in the big data era. Finally, we illustrate this methodology using a set of earthquake data.

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Event Processing and Real-Time Monitoring over Streaming Traffic Data

Cited by 11 publications

References 20 publications

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms

Analysis of Spatiotemporal Data

Recursive Estimation of the Spatial Error Model

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