Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems最新文献_第4页

Automating computational placement in IoT environments: doctoral symposium 物联网环境中的自动化计算放置:博士研讨会

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933435

Peter Michalák, S. Heaps, M. Trenell, P. Watson

引用次数: 4

Smooth and crispy: integrating continuous event proximity calculation and discrete event detection 平滑和脆:集成连续事件接近计算和离散事件检测

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933302

Stein Kristiansen, T. Plagemann, V. Goebel

引用次数: 5

Continuous graph pattern matching over knowledge graph streams 知识图流上的连续图模式匹配

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933306

Syed Gillani, Gauthier Picard, F. Laforest

{"title":"Continuous graph pattern matching over knowledge graph streams","authors":"Syed Gillani, Gauthier Picard, F. Laforest","doi":"10.1145/2933267.2933306","DOIUrl":"https://doi.org/10.1145/2933267.2933306","url":null,"abstract":"Continuous Graph Pattern Matching (CGPM) is an extended version of the traditional GPM that is evaluated over Knowledge Graph (Kg) streams. It comes with additional constraints of scalability and near-to-real-time response, and is used in many applications such as real-time knowledge management, social networks and sensor networks. Hence, existing GPM solutions for static Kgs are not directly applicable in this setting. This paper studies continuous GPM over Kg streams for two different executional models: event-based and incremental. We first propose a query-based graph pruning technique to filter the unnecessary triples from a Kg event. The pruned events are materialized in a set of vertically partitioned tables. We then use a hybrid join-and-explore technique to further prune and finally match the triples within a Kg event. Considering the on-the-fly execution of queries over pruned Kg events, we use an automata-based model to guide the join and exploration process. This leads to an index-free solution optimised for streaming environments. Experimental results with both synthetic and real-world datasets confirm that our system outperforms the state-of-the-art solutions by (on average) one to two orders of magnitude, in terms of performance and scalability.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123792037","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}

引用次数: 8

Complex event processing over streaming multi-cloud platforms: the FERARI approach: demo 流多云平台上的复杂事件处理:FERARI方法:演示

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933289

Ioannis Flouris, Vasiliki Manikaki, Nikos Giatrakos, Antonios Deligiannakis, M. Garofalakis, M. Mock, Sebastian Bothe, Inna Skarbovsky, Fabiana Fournier, Marko Stajcer, Tomislav Krizan, Jonathan Yom-Tov, Marijo Volarevic

引用次数: 5

Placement of distributed stream processing over heterogeneous infrastructures: doctoral symposium 分布式流处理在异构基础设施上的位置:博士研讨会

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933432

Matteo Nardelli

{"title":"Placement of distributed stream processing over heterogeneous infrastructures: doctoral symposium","authors":"Matteo Nardelli","doi":"10.1145/2933267.2933432","DOIUrl":"https://doi.org/10.1145/2933267.2933432","url":null,"abstract":"Data Stream Processing (DSP) applications can extract, in a timely manner, valuable information from distributed data sources (e.g., sensing devices, social networks). These applications are subject to unpredictable and varying workloads and have to satisfy strict quality requirements, usually expressed in terms of latency, availability, and throughput. To successfully execute DSP applications, recent trends investigate the possibility of exploiting decentralized computing resources, which nonetheless pose new challenges due to the network and system heterogeneity, geographic distribution, and non-negligible network latencies. The doctorate work, presented in this paper, investigates the deployment of DSP applications with Quality of Service (QoS) requirements over a distributed infrastructure of heterogeneous computing and networking resources. Specifically, to support our study, we extend an open-source DSP system, Apache Storm, by providing mechanisms for executing distributed QoS-aware placement policies and self-adaptation. Then, we provide a general formulation of the optimal placement problem for DSP applications, modeling the heterogeneity of the execution environment. The ongoing research aims at investigating the following directions. First, we will design heuristics able to determine the best placement in a feasible amount of time. Second, we will investigate runtime adaptation strategies and online placement algorithms. Third, to prove the generality of our approach, we will customize the designed solutions for similar problems (e.g., service selection, container deployment).","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125202858","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}

引用次数: 0

Taming velocity and variety simultaneously in big data with stream reasoning: tutorial 用流推理同时驯服大数据中的速度和多样性:教程

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933539

Emanuele Della Valle, Daniele Dell'Aglio, Alessandro Margara

{"title":"Taming velocity and variety simultaneously in big data with stream reasoning: tutorial","authors":"Emanuele Della Valle, Daniele Dell'Aglio, Alessandro Margara","doi":"10.1145/2933267.2933539","DOIUrl":"https://doi.org/10.1145/2933267.2933539","url":null,"abstract":"Many \"big data\" applications must tame velocity (processing data in-motion) and variety (processing many different types of data) simultaneously. The research on knowledge representation and reasoning has focused on the variety of data, devising data representation and processing techniques that promote integration and reasoning on available data to extract implicit information. On the other hand, the event and stream processing community has focused on the velocity of data, producing systems that efficiently operate on streams of data on-the-fly according to pre-deployed processing rules or queries. Several recent works explore the synergy between stream processing and reasoning to fully capture the requirements of modern data intensive applications, thus giving birth to the research domain of stream reasoning. This tutorial paper offers an overview of the theoretical and technological achievements in stream reasoning, highlighting the key benefits and limitations of existing approaches, and discussing the open challenges and the opportunities for future research. The paper mainly targets researchers and practitioners in the area of event and stream processing. The paper aims to stimulate the discussion on stream reasoning and to further promote the integration of reasoning techniques within event and stream processing systems in three ways: (i) by presenting an active research domain, where researchers on event and stream processing can apply their expertise; (ii) by discussing techniques and technologies that can help advancing the state of the art in event and stream processing; (iii) by identifying the open problems in the field of stream reasoning, and drawing attention to promising research directions.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125864160","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}

引用次数: 32

Theory and implementation of a distributed event based platform 分布式事件平台的理论与实现

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2940321

K. Chandy

{"title":"Theory and implementation of a distributed event based platform","authors":"K. Chandy","doi":"10.1145/2933267.2940321","DOIUrl":"https://doi.org/10.1145/2933267.2940321","url":null,"abstract":"This paper presents theory and an implementation of a Distributed Event Based System (DEBS) platform. The theory is based on a simple model that forms the basis of the implementation. Though this paper is about a DEBS platform, a description of the theory and model provides the motivation for the design. Many software libraries operate on \"data at rest', i.e. fixed data structures such as arrays and graphs. By contrast, DEBS systems operate on \"data in motion,\" i.e., data structures that change, in increments, over time. Many software libraries are designed for sequential execution or synchronous parallel execution. By contrast, DEBS systems have multiple agents executing asynchronously. The paper presents sufficient conditions that enable programs operating on data at rest to be reconfigured as networks of asynchronous agents operating on data structures that change incrementally as time progresses. The paper provides a brief description of a DEBS platform, called StreamPy, implemented in Python. StreamPy enables the use of libraries designed to operate on data at rest --- particularly for data analytics, artificial intelligence, and scientific computation --- for data in motion. An event is either defined by a pre-specified pattern or an event is learned from data. Learning what is, and what is not, an event requires the use of machine learning algorithms. A goal of StreamPy is to incorporate machine learning into data streaming to obtain a DEBS platform that learns what is an event and then to continually improve this learning.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126760483","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}

引用次数: 2

Proactive disruption management system: how not to be surprised by upcoming situations 主动中断管理系统:如何对即将到来的情况不感到惊讶

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933271

Suad Sejdovic, Yvonne Hegenbarth, Gerald H. Ristow, Roland Schmidt

引用次数: 10

A model driven approach for event processing applications 用于事件处理应用程序的模型驱动方法

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933268

O. Etzion, Fabiana Fournier, Inna Skarbovsky, B. Halle

引用次数: 7

The SABER system for window-based hybrid stream processing with GPGPUs: demo 基于gpgpu的基于窗口的混合流处理SABER系统:演示

Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems Pub Date : 2016-06-13 DOI: 10.1145/2933267.2933291

A. Koliousis, M. Weidlich, R. Fernandez, A. Wolf, Paolo Costa, P. Pietzuch

引用次数: 0