Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems最新文献_第3页

Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing 预测CEP规则的自动学习:弥合数据挖掘和复杂事件处理之间的差距

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3093917

Raef Mousheimish, Y. Taher, K. Zeitouni

{"title":"Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing","authors":"Raef Mousheimish, Y. Taher, K. Zeitouni","doi":"10.1145/3093742.3093917","DOIUrl":"https://doi.org/10.1145/3093742.3093917","url":null,"abstract":"Due to the undeniable advantage of prediction and proactivity, many research areas and industrial applications are accelerating the pace to keep up with data science and predictive analytics. However and due to three well-known facts, the reactive Complex Event Processing (CEP) technology might lag behind when prediction becomes a requirement. 1st fact: The one and only inference mechanism in this domain is totally guided by CEP rules. 2nd fact: The only way to define a CEP rule is by writing it manually with the help of a human expert. 3rd fact: Experts tend to write reactive CEP rules, because and regardless of the level of expertise, it is nearly impossible to manually write predictive CEP rules. Combining these facts together, the CEP is---and will stay--- a reactive computing technique. Therefore in this article, we present a novel data mining-based approach that automatically learns predictive CEP rules. The approach proposes a new learning algorithm where complex patterns from multivariate time series are learned. Then at run-time, a seamless transformation into the CEP world takes place. The result is a ready-to-use CEP engine with enrolled predictive CEP rules. Many experiments on publicly-available data sets demonstrate the effectiveness of our approach.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115823822","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: 30

An Autonomous and Dynamic Coordination and Discovery Service for Wide-Area Peer-to-peer Publish/Subscribe: Experience Paper 面向广域点对点发布/订阅的自主动态协调与发现服务:经验论文

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3093910

Kyoungho An, S. Khare, A. Gokhale, Akram Hakiri

{"title":"An Autonomous and Dynamic Coordination and Discovery Service for Wide-Area Peer-to-peer Publish/Subscribe: Experience Paper","authors":"Kyoungho An, S. Khare, A. Gokhale, Akram Hakiri","doi":"10.1145/3093742.3093910","DOIUrl":"https://doi.org/10.1145/3093742.3093910","url":null,"abstract":"Industrial Internet of Things (IIoT) applications are mission-critical, which require a scalable data sharing and dissemination platform that supports quality of service (QoS) properties such as timeliness, resilience, and security. Although the Object Management Group (OMG)'s Data Distribution Service (DDS), which is a data-centric, peer-to-peer publish/subscribe standard supporting multiple QoS properties, is well-suited to meet the requirements of IIoT applications, its design and current technology limitations constrains its use to local area networks only. Moreover, although broker-based bridging services exist to inter-connect isolated DDS networks, these solutions lack autonomous and dynamic coordination and discovery capabilities that are needed to bridge multiple, isolated networks on demand. To address these limitations, and enable a practical and readily deployable solution for IIoT, this paper presents and empirically validates PubSubCoord, which is an autonomous, coordination and discovery service for DDS endpoints operating over wide area networks.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130749995","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: 11

The DEBS 2017 Grand Challenge DEBS 2017大挑战

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3096342

Vincenzo Gulisano, Zbigniew Jerzak, R. Katerinenko, M. Strohbach, H. Ziekow

引用次数: 24

Challenges with Image Event Processing: Poster 图像事件处理的挑战:海报

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3095095

Asra Aslam, S. Hasan, E. Curry

引用次数: 3

Solution Recommender for System Failure Recovery via Log Event Pattern Matching on a Knowledge Graph: Demo 通过知识图上的日志事件模式匹配恢复系统故障的解决方案推荐:演示

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3095094

Miyuru Dayarathna, Prabhash Akmeemana, S. Perera, Malith Jayasinghe

引用次数: 3

Demonstration of a Prototype for Credit Card Fraud Management: Demo 信用卡诈骗管理的原型演示:演示

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3095096

Ivo Correia, A. Artikis, Nikos Katzouris, Chris Baber, Natan Morar, Inna Skarbovsky, Fabiana Fournier, G. Paliouras

引用次数: 1

The Event Crowd: A Novel Approach for Crowd-Enabled Event Processing 事件群体:群体事件处理的一种新方法

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3093922

Piyush Yadav, U. Hassan, S. Hasan, E. Curry

{"title":"The Event Crowd: A Novel Approach for Crowd-Enabled Event Processing","authors":"Piyush Yadav, U. Hassan, S. Hasan, E. Curry","doi":"10.1145/3093742.3093922","DOIUrl":"https://doi.org/10.1145/3093742.3093922","url":null,"abstract":"Event processing systems involve the processing of high volume and variety data which has inherent uncertainties like incomplete event streams, imprecise event recognition etc. With the emergence of crowdsourcing platforms, the performance of event processing systems can be enhanced by including 'human-in-the-loop' to leverage their cognitive ability. The resulting crowd-sourced event processing can cater to the problem of event uncertainty and veracity by using humans to verify the results. This paper introduces the first hybrid crowd-enabled event processing engine. The paper proposes a list of five event crowd operators that are domain and language independent and can be used by any event processing framework. These operators encapsulate the complexities to deal with crowd workers and allow developers to define an event-crowd hybrid workflow. The operators are: Annotate, Rank, Verify, Rate, and Match. The paper presents a proof of concept of event crowd operators, schedulers, poolers, aggregators in an event processing system. The paper demonstrates the implementation of these operators and simulates the system with various performance metrics. The experimental evaluation shows that throughput of the system was 7.86 events per second with average latency of 7.16 seconds for 100 crowd workers. Finally, the paper concludes with avenues for future research in crowd-enabled event processing.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121518672","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: 5

Multilateral Context Analysis based on the Novel Visualization of Network Tomography: Poster 基于新型网络断层扫描可视化的多边语境分析:海报

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3095090

Young Yoon, Yongjun Choi, Suchul Shin

引用次数: 1

FlowDB: Integrating Stream Processing and Consistent State Management FlowDB:集成流处理和一致状态管理

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3093929

Lorenzo Affetti, Alessandro Margara, G. Cugola

{"title":"FlowDB: Integrating Stream Processing and Consistent State Management","authors":"Lorenzo Affetti, Alessandro Margara, G. Cugola","doi":"10.1145/3093742.3093929","DOIUrl":"https://doi.org/10.1145/3093742.3093929","url":null,"abstract":"Recent advances in stream processing technologies led to their adoption in many large companies, where they are becoming a core element in the data processing stack. In these settings, stream processors are often used in combination with various kinds of data management frameworks to build software architectures that combine data storage, processing, retrieval, and mining. However, the adoption of separate and heterogeneous subsystems makes these architectures overmuch complex, and this hinders the design, development, maintenance, and evolution of the overall system. We address this issue by proposing a new model that integrates data management within a distributed stream processor. The model enables individual stream processing operators to persist data and make it visible and queryable from external components. It offers flexible mechanisms to control the consistency of data, including transactional updates plus ordering and integrity constraints. The paper contributes to the research on stream processing in various ways: we introduce a new model that has the potential to simplify complex data-intensive applications by integrating data management capabilities within a stream processing system; we define data consistency guarantees and show how they are enforced within this new model; we implement the model into the FlowDB prototype, and study its overhead with respect to a pure stream processing system using real world case studies and synthetic workloads. Finally, we further prove the benefits of the proposed model by showing that FlowDB can outperform a state-of-the-art, in-memory distributed database in data management tasks.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114763183","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: 21

Runtime Anomaly Detection Method in Smart Factories using Machine Learning on RDF Event Streams: Grand Challenge 基于RDF事件流的机器学习智能工厂运行时异常检测方法:重大挑战

Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems Pub Date : 2017-06-08 DOI: 10.1145/3093742.3095104

Joong-Hyun Choi, Kang-Woo Lee, Hyungkun Jung, Eun-Sun Cho

引用次数: 0