{"title":"Chronograph: A Distributed Platform for Event-Sourced Graph Computing","authors":"Benjamin Erb, F. Kargl","doi":"10.1145/3007592.3007601","DOIUrl":null,"url":null,"abstract":"Many data-driven applications require mechanisms for processing interconnected or graph-based data sets. Several platforms exist for offline processing of such data and fewer solutions address online computations on dynamic graphs. We combined a modified actor model, an event-sourced persistence layer, and a vertex-based, asynchronous programming model in order to unify event-driven and graph-based computations. Our distributed chronograph platform supports both near-realtime and batch computations on dynamic, event-driven graph topologies, and enables full history tracking of the evolving graphs over time.","PeriodicalId":125362,"journal":{"name":"Proceedings of the Posters and Demos Session of the 17th International Middleware Conference","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Posters and Demos Session of the 17th International Middleware Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3007592.3007601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Many data-driven applications require mechanisms for processing interconnected or graph-based data sets. Several platforms exist for offline processing of such data and fewer solutions address online computations on dynamic graphs. We combined a modified actor model, an event-sourced persistence layer, and a vertex-based, asynchronous programming model in order to unify event-driven and graph-based computations. Our distributed chronograph platform supports both near-realtime and batch computations on dynamic, event-driven graph topologies, and enables full history tracking of the evolving graphs over time.