In this paper a real-life counterfeit and theft detection scenario from pharmaceutical manufacturing is modelled using events encoded as XML and RDF. With Esper and Instans event processing platforms, the second one from the semantic web domain, the same task is configured and an experimental performance evaluation is carried out. Our results show that even though the starting points are very different, the same core task can be accomplished on both platforms. We provide quantitative performance comparisons that corroborate our analysis. For an understanding of what can be expected from each framework outside the core task, the differences between the two tools and their respective domains are qualitatively analysed.
{"title":"RFID-based logistics monitoring with semantics-driven event processing","authors":"M. Rinne, M. Solanki, Esko Nuutila","doi":"10.1145/2933267.2933300","DOIUrl":"https://doi.org/10.1145/2933267.2933300","url":null,"abstract":"In this paper a real-life counterfeit and theft detection scenario from pharmaceutical manufacturing is modelled using events encoded as XML and RDF. With Esper and Instans event processing platforms, the second one from the semantic web domain, the same task is configured and an experimental performance evaluation is carried out. Our results show that even though the starting points are very different, the same core task can be accomplished on both platforms. We provide quantitative performance comparisons that corroborate our analysis. For an understanding of what can be expected from each framework outside the core task, the differences between the two tools and their respective domains are qualitatively analysed.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"12 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":"131646185","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}
Martin Hirzel, R. Rabbah, Philippe Suter, O. Tardieu, M. Vaziri
Stream processing is a computational paradigm that allows the analysis of live data streams as they are produced. This paper describes a programming model, based on enhancements to spreadsheets, that enables users with limited programming experience to participate directly in the development of complex streaming applications. The programming model augments a conventional spreadsheet with streaming features that permit operating over unbounded data sets despite the finite interface provided by the spreadsheet. The new constructs include time-based windows and partitioning. We introduce a spreadsheet compiler that generates C++ code to achieve integration with existing stream processing systems. Our experimental study illustrates the expressivity of the new features and finds that our implementation is between 8x slower and 2x faster than hand-written stream programs.
{"title":"Spreadsheets for stream processing with unbounded windows and partitions","authors":"Martin Hirzel, R. Rabbah, Philippe Suter, O. Tardieu, M. Vaziri","doi":"10.1145/2933267.2933607","DOIUrl":"https://doi.org/10.1145/2933267.2933607","url":null,"abstract":"Stream processing is a computational paradigm that allows the analysis of live data streams as they are produced. This paper describes a programming model, based on enhancements to spreadsheets, that enables users with limited programming experience to participate directly in the development of complex streaming applications. The programming model augments a conventional spreadsheet with streaming features that permit operating over unbounded data sets despite the finite interface provided by the spreadsheet. The new constructs include time-based windows and partitioning. We introduce a spreadsheet compiler that generates C++ code to achieve integration with existing stream processing systems. Our experimental study illustrates the expressivity of the new features and finds that our implementation is between 8x slower and 2x faster than hand-written stream programs.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"14 2 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":"124620510","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}
Christos Vlassopoulos, Ioannis Kontopoulos, Michail Apostolou, A. Artikis, D. Vogiatzis
We present a system for analytics on streaming social media that computes the most active posts, based on the age and the amount of comments for each post, and tracks the largest communities that comprise friends that are fond of the same content. To deal with high velocity data streams, we implemented an algorithm for incrementally updating graphs expressing social networks. The evaluation of our system is based on the datasets of the DEBS 2016 challenge.
{"title":"Dynamic graph management for streaming social media analytics","authors":"Christos Vlassopoulos, Ioannis Kontopoulos, Michail Apostolou, A. Artikis, D. Vogiatzis","doi":"10.1145/2933267.2933515","DOIUrl":"https://doi.org/10.1145/2933267.2933515","url":null,"abstract":"We present a system for analytics on streaming social media that computes the most active posts, based on the age and the amount of comments for each post, and tracks the largest communities that comprise friends that are fond of the same content. To deal with high velocity data streams, we implemented an algorithm for incrementally updating graphs expressing social networks. The evaluation of our system is based on the datasets of the DEBS 2016 challenge.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"139 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":"129443139","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}
Peter M. Fischer, Io Taxidou, Bernhard Lutz, Michael Huber
Recent advances in social media have triggered a massive engagement of user population: a large part of people's lives has shifted to social media platforms and real events are reported while they are happening (e.g. in Twitter). As a result, such platforms have become an important source of information, being used by professionals as well, e.g. journalists, for fast access to news and events. Social media maintain an underlying network of social connections over which such information propagates. Information diffusion in social media has attracted attention, by analyzing how information is propagated from user to user and who is influenced by whom. Given the scale and speed of such information, systems that can keep up with such fast rates are required. In this poster, we present a system for real time reconstruction of information diffusion that encompass the challenges of analyzing fast data streams combined with large social graphs.
{"title":"Distributed streaming reconstruction of information diffusion: poster","authors":"Peter M. Fischer, Io Taxidou, Bernhard Lutz, Michael Huber","doi":"10.1145/2933267.2933294","DOIUrl":"https://doi.org/10.1145/2933267.2933294","url":null,"abstract":"Recent advances in social media have triggered a massive engagement of user population: a large part of people's lives has shifted to social media platforms and real events are reported while they are happening (e.g. in Twitter). As a result, such platforms have become an important source of information, being used by professionals as well, e.g. journalists, for fast access to news and events. Social media maintain an underlying network of social connections over which such information propagates. Information diffusion in social media has attracted attention, by analyzing how information is propagated from user to user and who is influenced by whom. Given the scale and speed of such information, systems that can keep up with such fast rates are required. In this poster, we present a system for real time reconstruction of information diffusion that encompass the challenges of analyzing fast data streams combined with large social graphs.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"163 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":"130012927","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}
The recent rise in scale of sensors has led to the need for faster processing of events from multiple sensor data streams in a variety of real-world applications. We need an approach to model real-world entities and their interrelationships, and specify the process of moving from sensor data streams to event detection to event-based goal planning. Recent advances in analysis of temporal data, such as time series shapelets, provide methods for identifying these discriminative events for classification. In this dissertation, I make connections between event processing and time series data mining as part of a comprehensive event detection and representation framework.
{"title":"Modeling and recognition of events from multidimensional data: doctoral symposium","authors":"O. Patri","doi":"10.1145/2933267.2933434","DOIUrl":"https://doi.org/10.1145/2933267.2933434","url":null,"abstract":"The recent rise in scale of sensors has led to the need for faster processing of events from multiple sensor data streams in a variety of real-world applications. We need an approach to model real-world entities and their interrelationships, and specify the process of moving from sensor data streams to event detection to event-based goal planning. Recent advances in analysis of temporal data, such as time series shapelets, provide methods for identifying these discriminative events for classification. In this dissertation, I make connections between event processing and time series data mining as part of a comprehensive event detection and representation framework.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"26 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":"126179950","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}
Data monitoring over distributed streams is a fundamental problem, as represented by modern applications, e.g., sensor network and financial data monitoring. Such applications need a technique which continuously monitors user-requiring data and achieves not only time and space efficiencies but also communication efficiency. In addition, result diversification is also required to increase user satisfaction, thus has been receiving significant attention recently. This motivates us to consider a problem of monitoring k-diverse data over distributed streams. Result diversification is well known to be NP-hard, so the natures of NP-hardness and dynamic distributed data bring non-trivial challenges, e.g., impracticably of centralized approaches. In this paper, we propose a novel algorithm that monitors k-diverse data with time, space, and communication efficiencies. The results of our experiments using both real and synthetic data confirm the effectiveness of our algorithm.
{"title":"Diversified set monitoring over distributed data streams","authors":"Daichi Amagata, T. Hara","doi":"10.1145/2933267.2933298","DOIUrl":"https://doi.org/10.1145/2933267.2933298","url":null,"abstract":"Data monitoring over distributed streams is a fundamental problem, as represented by modern applications, e.g., sensor network and financial data monitoring. Such applications need a technique which continuously monitors user-requiring data and achieves not only time and space efficiencies but also communication efficiency. In addition, result diversification is also required to increase user satisfaction, thus has been receiving significant attention recently. This motivates us to consider a problem of monitoring k-diverse data over distributed streams. Result diversification is well known to be NP-hard, so the natures of NP-hardness and dynamic distributed data bring non-trivial challenges, e.g., impracticably of centralized approaches. In this paper, we propose a novel algorithm that monitors k-diverse data with time, space, and communication efficiencies. The results of our experiments using both real and synthetic data confirm the effectiveness of our algorithm.","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":"126367331","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}
Malith Jayasinghe, Anoukh Jayawardena, Bhagya Rupasinghe, Miyuru Dayarathna, S. Perera, Sriskandarajah Suhothayan, I. Perera
The ACM DEBS Grand Challenge 2016 focuses on analysing the properties of a time evolving social-network graph generated using LDBC (Linked Data Benchmark Council) Social Network Benchmark. In this paper we present how we used WSO2 CEP, an open source, commercially available Complex Event Processing Engine, to solve the problem. On a 4-core/8 GB virtual machine, our solution processed 90,000 events per second with a mean latency of 6 ms for query 1. For query 2 it processed 210,000 events per second with a mean latency of only 0.3 ms. The paper describes the solution we propose, the experiments' results, and presents how we optimized the performance of our solution.
{"title":"Continuous analytics on graph data streams using WSO2 complex event processor","authors":"Malith Jayasinghe, Anoukh Jayawardena, Bhagya Rupasinghe, Miyuru Dayarathna, S. Perera, Sriskandarajah Suhothayan, I. Perera","doi":"10.1145/2933267.2933508","DOIUrl":"https://doi.org/10.1145/2933267.2933508","url":null,"abstract":"The ACM DEBS Grand Challenge 2016 focuses on analysing the properties of a time evolving social-network graph generated using LDBC (Linked Data Benchmark Council) Social Network Benchmark. In this paper we present how we used WSO2 CEP, an open source, commercially available Complex Event Processing Engine, to solve the problem. On a 4-core/8 GB virtual machine, our solution processed 90,000 events per second with a mean latency of 6 ms for query 1. For query 2 it processed 210,000 events per second with a mean latency of only 0.3 ms. The paper describes the solution we propose, the experiments' results, and presents how we optimized the performance of our solution.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"66 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":"130841047","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}
Real-time analytics of anomalous phenomena on streaming data typically relies on processing a large variety of continuous outlier detection requests, each configured with different parameter settings. The processing of such complex outlier analytics workloads is resource consuming due to the algorithmic complexity of the outlier mining process. In this work we propose a sharing-aware multi-query execution strategy for outlier detection on data streams called SOP. The key insight of SOP is to transform the problem of handling a multi-query outlier analytics workload into a single-query skyline computation problem. SOP achieves minimal utilization of both computational and memory resources for the processing of these complex outlier analytics workload.
{"title":"Multi-query outlier detection over data streams: poster","authors":"Lei Cao, Jiayuan Wang, Elke A. Rundensteiner","doi":"10.1145/2933267.2933292","DOIUrl":"https://doi.org/10.1145/2933267.2933292","url":null,"abstract":"Real-time analytics of anomalous phenomena on streaming data typically relies on processing a large variety of continuous outlier detection requests, each configured with different parameter settings. The processing of such complex outlier analytics workloads is resource consuming due to the algorithmic complexity of the outlier mining process. In this work we propose a sharing-aware multi-query execution strategy for outlier detection on data streams called SOP. The key insight of SOP is to transform the problem of handling a multi-query outlier analytics workload into a single-query skyline computation problem. SOP achieves minimal utilization of both computational and memory resources for the processing of these complex outlier analytics workload.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"16 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":"125646731","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}
Given the advent of cyber-physical systems (CPS), event-based control paradigms such as complex event processing (CEP) are vital enablers for adaptive analytical control mechanisms. CPS are becoming a high-profile research topic as they are key to disruptive digital innovations such as autonomous driving, industrial internet, smart grid and ambient assisted living. However, organizational and technological scalability of today's CEP approaches is limited by their monolithic architectures. This leads to the research idea for atomic CEP entities and the hypothesis that a network of small event-based control services is better suited for CPS development and operation than current centralised approaches. In addition, the paper summarizes preliminary results of the presented doctoral work and outlines questions for future research as well as an evaluation plan.
{"title":"Adaptive steering of cyber-physical systems with atomic complex event processing services: doctoral symposium","authors":"Julius Ollesch","doi":"10.1145/2933267.2933427","DOIUrl":"https://doi.org/10.1145/2933267.2933427","url":null,"abstract":"Given the advent of cyber-physical systems (CPS), event-based control paradigms such as complex event processing (CEP) are vital enablers for adaptive analytical control mechanisms. CPS are becoming a high-profile research topic as they are key to disruptive digital innovations such as autonomous driving, industrial internet, smart grid and ambient assisted living. However, organizational and technological scalability of today's CEP approaches is limited by their monolithic architectures. This leads to the research idea for atomic CEP entities and the hypothesis that a network of small event-based control services is better suited for CPS development and operation than current centralised approaches. In addition, the paper summarizes preliminary results of the presented doctoral work and outlines questions for future research as well as an evaluation plan.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"2006 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":"123913262","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}
In sensor cloud services, the expense is charged based on the amount of resource usage, e.g. data requests. This paper originally presents an expense-minimizing framework for top-k monitoring in sensor cloud services where the expense is denoted by the costs of data requests. Instead of fetching all the latest data in each timestamp, we propose a novel ε-top-k query delivering approximate top-k answers with a probabilistic guarantee on the selectively-fetched dataset which is a combination of certain and uncertain data (modelled by their age). In addition, using a cloud environment as well as our proposed method to process ε-top-k queries can alleviate the computing-intensive computations, so it is not only cheaper but even faster than an ordinary top-k calculation method. The extensive experiments on the real-world climate datasets demonstrate that our methods can reduce the expense by more than half with desirable accuracy.
{"title":"Reducing expenses of top-k monitoring in sensor cloud services","authors":"Kamalas Udomlamlert, T. Hara","doi":"10.1145/2933267.2935090","DOIUrl":"https://doi.org/10.1145/2933267.2935090","url":null,"abstract":"In sensor cloud services, the expense is charged based on the amount of resource usage, e.g. data requests. This paper originally presents an expense-minimizing framework for top-k monitoring in sensor cloud services where the expense is denoted by the costs of data requests. Instead of fetching all the latest data in each timestamp, we propose a novel ε-top-k query delivering approximate top-k answers with a probabilistic guarantee on the selectively-fetched dataset which is a combination of certain and uncertain data (modelled by their age). In addition, using a cloud environment as well as our proposed method to process ε-top-k queries can alleviate the computing-intensive computations, so it is not only cheaper but even faster than an ordinary top-k calculation method. The extensive experiments on the real-world climate datasets demonstrate that our methods can reduce the expense by more than half with desirable accuracy.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"27 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":"123918270","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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