{"title":"Exploratory novelty identification in human activity data streams","authors":"A. Pozdnoukhov, F. Walsh","doi":"10.1145/1878500.1878512","DOIUrl":"https://doi.org/10.1145/1878500.1878512","url":null,"abstract":"Heterogeneous human-generated data streams are the measurands which provide opportunities to identify patterns, detect novelties and explore evolution of complex social systems. Communication technologies with their very high penetration into society can serve as particularly rich sources of information. However, for a variety of observable communication channels one has little or no access to the content of human-to-human communications, while the data streams on the intensities of such events are more common. The paper presents a framework of methods useful for exploratory analysis and visualization of such data streams. Particularly, we demonstrate how untypical activity levels can be identified by fitting a non-homogeneous Markov-modulated Poisson process and spatialising the component corresponding to unusual bursts/lulls of activity via heat maps. This approach is illustrated with a case study devoted to the analysis of geo-referenced data streams of instant messaging activity on the internet.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125260324","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":"Microsoft framework for geospatial stream processing","authors":"E. Katibah","doi":"10.1145/1878500.1878501","DOIUrl":"https://doi.org/10.1145/1878500.1878501","url":null,"abstract":"Microsoft StreamInsight is a platform for developing and deploying streaming applications. StreamInsight embraces a temporal stream model to unify and further enrich query language features, handle imperfections in event delivery and define consistency guarantees on the output. With its extensibility framework, StreamInsight enables developers to integrate their domain expertise within the query pipeline as user defined functions, operators and aggregates. The addition of the Microsoft SQL Server Spatial Library enables StreamInsight to deliver Geostreaming support. With StreamInsight analyzing and summarizing input data, SQL Server and SQL Azure has the ability to persist these summaries in the data center or the cloud. These summaries can be used as feedback to StreamInsight for future query processing or used as the basis for historical data analysis and reporting. This framework enables organizations to seamlessly consume, use, and extend all form of stream data including geographic data.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123818227","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":"Spatiotemporal summarization of traffic data streams","authors":"Bei Pan, Ugur Demiryurek, F. Kashani, C. Shahabi","doi":"10.1145/1878500.1878504","DOIUrl":"https://doi.org/10.1145/1878500.1878504","url":null,"abstract":"With resource-efficient summarization and accurate reconstruction of the historic traffic sensor data, one can effectively manage and optimize transportation systems (e.g., road networks) to become smarter (better mobility, less congestion, less travel time, and less travel cost) and greener (less waste of fuel and less greenhouse gas production). The existing data summarization (and archival) techniques are generic and are not designed to leverage the unique characteristics of the traffic data for effective data reduction. In this paper, we propose and explore a family of data summaries that take advantage of the high temporal and spatial redundancy/correlation among sensor readings from individual sensors and sensor groups, respectively, for effective data reduction. In particular, with these summaries we derive and maintain a \"signature\" as well as a series of \"outliers\" for the readings received from each individual sensor or group of co-located sensors. While signatures capture the typical readings that estimate the actual readings with bounded error, the outliers represent the actual readings where the error-bound is violated. With the combination of signatures and outliers, our proposed data summaries can effectively represent the actual data with much smaller storage footprint, while allowing for efficient querying of the sensor data with bounded error. Our experiments with a real traffic sensor dataset shows that our proposed data summaries use only 23% of the storage space otherwise required for storing the actual data, while allowing for highly accurate query results with guaranteed precision.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134633269","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 data stream-based evaluation framework for traffic information systems","authors":"Sandra Geisler, C. Quix, S. Schiffer","doi":"10.1145/1878500.1878505","DOIUrl":"https://doi.org/10.1145/1878500.1878505","url":null,"abstract":"Traffic information systems based on mobile, in-car sensor technology are a challenge for data management systems as a huge amount of data has to be processed in real-time. Data mining methods must be adapted to cope with these challenges in handling streaming data. Although several data stream mining methods have been proposed, an evaluation of such methods in the context of traffic applications is yet missing. In this paper, we present an evaluation framework for data stream mining for traffic applications. We apply a traffic simulation software to emulate the generation of traffic data by mobile probes. The framework is evaluated in a first case study, namely queue-end detection. We show first results of the evaluation of a data stream mining method, varying multiple parameters for the traffic simulation. The goal of our work is to identify parameter settings for which the data stream mining methods produce useful results for the traffic application at hand.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124996655","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":"AIMS: an SQL-based system for airspace monitoring","authors":"Gereon Schüller, Andreas Behrend","doi":"10.1145/1878500.1878508","DOIUrl":"https://doi.org/10.1145/1878500.1878508","url":null,"abstract":"In this paper, we present the \"Airspace Monitoring System\" (AIMS) for monitoring and analyzing flight data streams with respect to the occurrence of arbitrary complex events. In contrast to already existing tools which often focus on a single task like flight delay detection, we want to provide a more general system that allows for a comprehensive analysis of aircraft movements. This includes, e.g., the detection of critical deviations from the current flight plan, abnormal approach parameters of landing flights as well as areas with an increased risk of collisions. To this end, tracks are extracted from cluttered radar data and SQL views are employed for a timely processing of these tracks.\u0000 Our general aim is to show that conventional relational database technology is capable of dealing with data streams of remarkably high complexity and frequency.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127877569","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":"Querying streaming point clusters as regions","authors":"Chengyang Zhang, Y. Huang","doi":"10.1145/1878500.1878510","DOIUrl":"https://doi.org/10.1145/1878500.1878510","url":null,"abstract":"This paper focuses on one important type of geo-streaming data - point geo-streams. Many interesting applications require selected discrete points with similar observations to be clustered according to spatial proximity and further elevated into higher-level spatial regions. Querying streaming point clusters as regions directly in a geo-stream database has many benefits, but is also very challenging. We propose two query optimization strategies, namely semantics-based optimization and incremental optimization for answering queries involving both point geo-streams and static data set. The experimental results on a streaming meteorological data set demonstrate the effectiveness and the efficiency of the query processing techniques. Compared with the baseline methods, our optimization methods can reduce the total execution time by more than an order of magnitude.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134483832","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}
J. Whiteneck, K. Tufte, Amit Bhat, D. Maier, Rafael Fernández-Moctezuma
{"title":"Framing the question: detecting and filling spatial-temporal windows","authors":"J. Whiteneck, K. Tufte, Amit Bhat, D. Maier, Rafael Fernández-Moctezuma","doi":"10.1145/1878500.1878506","DOIUrl":"https://doi.org/10.1145/1878500.1878506","url":null,"abstract":"We propose a new mechanism, which we term frames, for data-dependent windows. In contrast to traditional timestamp-based windows, frames represent just the boundary of a window and can be filled with data from secondary streams or historical data. Examples show how frames can be useful in network and sensor monitoring applications. We present frame definition and implementation in one dimension, discuss extension to multidimensional frames, and identify issues for further investigation.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888233","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":"Geostreaming at IBM: infosphere streams and intelligent traffic systems","authors":"Robert Uleman","doi":"10.1145/1878500.1878502","DOIUrl":"https://doi.org/10.1145/1878500.1878502","url":null,"abstract":"Based on a system developed at IBM Research over eight years, and in production in one of the most demanding environments imaginable for the last three, IBM has introduced InfoSphere Streams, an advanced, commercial stream processing platform. With an innovative, distributed-processing runtime and a graph-based, extensible programming paradigm, it is well suited to extreme performance requirements and highly sophisticated processing and analytics on all kinds of data, from structured business records to text, audio, imagery, time-series and geospatial data. It is already being used in numerous scientific applications and academic projects. In this talk, I will give an overview of the Streams product and then highlight one such project, a collaboration with the KTH University in Stockholm. The ultimate aim of the project is to build an intelligent route planning and travel time prediction system based on instantaneous information about speeds and travel times from GPS-instrumented vehicles as well as historical data collected by the same vehicles. I will discuss the design and implementation of the application as well as performance results from initial simulations.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"391 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115204467","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}
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