International Workshop on Mobile Geographic Information Systems最新文献

{"title":"Predicting the next location change and time of change for mobile phone users","authors":"M. Ozer, Ilkcan Keles, I. H. Toroslu, P. Senkul, S. Ergüt","doi":"10.1145/2675316.2675318","DOIUrl":"https://doi.org/10.1145/2675316.2675318","url":null,"abstract":"Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121209264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards matching improvement between spatio-temporal tasks and workers in mobile crowdsourcing market systems","authors":"A. Fonteles, S. Bouveret, J. Gensel","doi":"10.1145/2675316.2675319","DOIUrl":"https://doi.org/10.1145/2675316.2675319","url":null,"abstract":"Crowdsourcing market systems (CMS) are platforms that enable one to publish tasks that others are intended to accomplished. Usually, these are systems where users, called workers, perform tasks using desktop computers. Recently, mobile CMSs have appeared with tasks that exploit the mobility and the location of workers. For example, if a third party system requires a picture of a given place, it may publish a task asking for some worker to go there, take this picture and upload it. One problem of CMSs is that the more tasks they have, the harder it is for workers to find and choose one they are interested in. Besides, workers who accomplish tasks may have no particular experience and consequently provide bad results for tasks. In order to improve the matching between workers and spatio-temporal tasks in mobile CMSs, we propose a conceptual framework that consists of two mechanisms. One considers the requirements of a task for selecting suitable workers, while the other recommends tasks for a worker according to his preferences and skills. As a result, workers spend less time searching tasks, more working on it, providing results with higher quality.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124067639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A system for stoppage pattern extraction from public bus GPS traces in developing regions","authors":"Ratna Mandal, Nitin Agarwal, Projan Das, Shreyasi Pathak, Himansu Rathi, S. Nandi, Sujoy Saha","doi":"10.1145/2675316.2675323","DOIUrl":"https://doi.org/10.1145/2675316.2675323","url":null,"abstract":"Identification of Public Bus Stops and characterising their waiting time is of significance in the domain of Intelligent Transportation System and public interest. Surveys suggest that the existing systems and algorithms cannot be merely replicated in the context of developing regions, because of the kind of heterogeneity and chaotic situation prevalent in their transport system, in contrast to the orderly and much regulated system of the developed world. Moreover a significant fraction of population residing here is not so tech-savvy, hence crowd-sourced based strategies renders useless. Either novel algorithms/techniques are required or the existing ones have to undergo suitable adaptation and innovation to suit the contrasting characteristics of developing regions. Using customized hardware we present the analysis of public bus GPS traces of more than 1000 km for a 23 km route of Durgapur, a suburban city (West Bengal, India). We have revealed 53 Bus Stops with 2% false positive along with their waiting time distribution using a density based spatio-temporal clustering algorithm.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116729940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovering frequent mobility patterns on moving object data","authors":"T. C. D. Silva, J. Macêdo, M. Casanova","doi":"10.1145/2675316.2675325","DOIUrl":"https://doi.org/10.1145/2675316.2675325","url":null,"abstract":"We consider the problem of efficiently discovering and detecting frequent mobility patterns on moving object data. Our proposed approach is key for mobility applications, such as applications that need to discover and explain movement patterns of a set of moving objects (e.g. traffic management, birds migration, disease spreading). In this sense, we developed a method that performs density based clustering on trajectory data at regular time intervals, then we analyze clusters evolution, which is characterized by appear, disappear, expand, shrink, split, merge and survive. To solve our problem, a graph-based representation called Graph Evolution Cluster over Time (Δevol) is described and an algorithm to generate the graph is also presented. Finally, we map our problem to the problem of discovering frequent graph paths on Δevol. Therefore, the frequent graph paths are the frequent sequence of evolution patterns that occurs in the dataset. We discuss a preliminary solution to this problem and present some experimental results. The results suggest that evolution patterns and their frequency can be effectively obtained through the proposed Δevol obtained from moving object data.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127234325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective evacuation route planning algorithms by updating earliest arrival time of multiple paths","authors":"Manki Min, Jonguk Lee, Sunho Lim","doi":"10.1145/2675316.2675326","DOIUrl":"https://doi.org/10.1145/2675316.2675326","url":null,"abstract":"More and more natural disasters are happening around the globe and in such urgent situations, effective evacuation planning is one of the most critical tools for human safety. Evacuation planning algorithms are different from traditional network routing algorithms in the sense that the objective is to minimize the time when the last evacuee arrives at the destination. Among the existing evacuation planning algorithms, CCRP++ finds good solutions but its computation time is not scalable in terms of the number of the evacuees. In addition, in order to improve the computation time from the base algorithm CCRP, CCRP++ makes non-global up-date of earliest arrival time of each source node which results in unnecessarily large evacuation time.\u0000 In this paper we investigated the two factors (evacuation time and computation time) of CCRP++ and proposed three algorithms, DMP, SMP, and EET, that significantly improve both factors. All four algorithms take the transportation network as the input and output the complete evacuation scenarios in which individualized evacuation plan for each evacuee is determined. Our first algorithm DMP significantly reduces the number of the shortest path findings during the evacuation iteration which is the main reason of large computation time of CCRP++. In addition, by updating the earliest arrival time of each found path efficiently, the evacuation time of DMP output is on average reduced compared to that of CCRP++ output. Our second algorithm SMP further reduces the computation time by first finding the limited number of shortest paths before the evacuation iteration while maintaining the reduced evacuation time. Our third algorithm EET focuses on reducing the evacuation time of SMP output even in the worst-case scenarios by efficiently and effectively estimating the evacuation time and by using it to determine the source node to be evacuated in each round. The computation results show that EET successfully reduces the evacuation time, especially when DMP/SMP outputs were worse than CCRP++. Due to its algorithmic complication, EET has slightly increased computation time compared to SMP, but still remains comparable to DMP.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128061566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monochromatic RkNN queries in time-dependent road networks","authors":"Felix Borutta, M. Nascimento, Johannes Niedermayer, Peer Kröger","doi":"10.1145/2675316.2675317","DOIUrl":"https://doi.org/10.1145/2675316.2675317","url":null,"abstract":"The problem of finding influence sets for a specific point, e.g. determining the influence of a location for a new restaurant on competitive restaurants, can be modeled as the reverse k nearest neighbor (RkNN) query. Although a lot of research has already been published on this topic, there is no adequate solution to solve the problem in time-dependent networks. In this work, we address RkNN queries in networks considering time-dependency, e.g. in road networks where traffic conditions influence the travel speed. Due to that the reverse nearest neighbors set can change over time, even if the objects are assumed to be static. We present an algorithm that solves the monochromatic time-dependent RkNN problem efficiently for a specific point in time. This algorithm uses a pruning technique to minimize the necessary network expansion. Furthermore, we present a variant of the algorithm which uses apriori knowledge from a pre-processing step to save further network expansion. Finally, we compare the proposed methods for monochromatic queries to a simple baseline approach by using time-dependent road networks of different sizes, various densities for the points of interests and various values for k. The results show that our proposed algorithms are orders of magnitude faster than a straightforward alternative.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131051941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new approach to process top-k spatial preference queries in a directed road network","authors":"M. Attique, Rizwan Qamar, Hyung-Ju Cho, Tae-Sun Chung","doi":"10.1145/2675316.2675320","DOIUrl":"https://doi.org/10.1145/2675316.2675320","url":null,"abstract":"Top-k spatial preference query ranks objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for wide range of location based services such as hotel browsing and apartment searching; several algorithms have been proposed to process them in Euclidean space. Although, few algorithms study top-k preference queries in a road network, however, they all focus on undirected road network. To the best of our knowledge, this is the first attempt to investigate the problem of processing the top-k spatial preference queries in a directed road networks. Computation of data object score requires examining the scores of feature objects in its spatial neighborhood. This may raise the processing cost resulting in high query processing time. Therefore, in this paper we propose a new preference query search algorithm called PSA that can efficiently answer the top-k spatial preference queries in directed road network. Experimental study shows that our algorithm significantly reduces the query processing time compared to baseline solution for a wide range of problem settings.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114054248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human activity recognition from spatial data sources","authors":"Zolzaya Dashdorj, Stanislav Sobolevsky, L. Serafini, C. Ratti","doi":"10.1145/2675316.2675321","DOIUrl":"https://doi.org/10.1145/2675316.2675321","url":null,"abstract":"Recent availability of big data of digital traces of human activity boosted research on human behavior. However, in most of the datasets such as mobile phone data or GPS traces, an important layer of information is typically missing: providing an extensive information of when and where people go typically does not allow understanding of what they do there. Predicting the context of human behavior in such cases where such information is not directly available from the data is a complex task that addresses context recognition problems. To fill in the contextual information for such data, we developed an ontological and stochastic model (HRBModel) that interprets semantic (high-level) human behaviors from geographical maps like OpenStreetMap, analyzing the distribution of Points of Interest(POIs), in a given region and time period. The semantic human behaviors are human activities that are accompanied by their likelihood, depending on their location and time. In this paper, we perform an extended evaluation of this model based on other qualitative data source, namely a country-wide anonymized bank card transaction data in Spain, which contains contextual information about the locations and the types of business categories where transactions occurred. This allows us to validate the model, by matching our predicted activity patterns with the actually observed ones, so that it can be later applied to the cases where such information is unavailable. This extended evaluation aimed to define the applicability of the predictive model, HRBModel, taking various type of spatial and temporal factors into account.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128596667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding high-probability mobile photography routes between origin and destination endpoints","authors":"T. Phan","doi":"10.1145/2675316.2675322","DOIUrl":"https://doi.org/10.1145/2675316.2675322","url":null,"abstract":"A challenge for mobile smartphone photographers is finding the best route to take interesting photos given origin and destination constraints. To solve this problem, we leveraged crowd-sourced geotagged photographs in order to build a model that can generate the highest-probability route between two geolocation endpoints. Our implemented system comprises discretizing the geocoordinate space, determining popular geolocations, computing Markov transition probabilities, and finally generating highest-probability end-to-end routes by solving a shortest path problem where edge probabilities are converted into edge distances. The experimental results show the impact of path length and spatial resolution on the prediction accuracy of our system.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131098437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAME4HPC: a promising approach in building a scalable and mobile environment for high-performance computing","authors":"R. Karthik","doi":"10.1145/2675316.2675324","DOIUrl":"https://doi.org/10.1145/2675316.2675324","url":null,"abstract":"In this paper, an architecture for building Scalable And Mobile Environment For High-Performance Computing with spatial capabilities called SAME4HPC is described using cutting-edge technologies and standards such as Node.js, HTML5, ECMAScript 6, and PostgreSQL 9.4. Mobile devices are increasingly becoming powerful enough to run high-performance apps. At the same time, there exist a significant number of low-end and older devices that rely heavily on the server or the cloud infrastructure to do the heavy lifting. Our architecture aims to support both of these types of devices to provide high-performance and rich user experience. A cloud infrastructure consisting of OpenStack with Ubuntu, GeoServer, and high-performance JavaScript frameworks are some of the key open-source and industry standard practices that has been adopted in this architecture.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128680490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}