Within the last few years, event processing has been gaining a lot of attention as it represents a powerful possibility to establish a real-time monitoring and situation detection. The real-time detection of situations allows a timely reaction and helps to reduce the damage made by harmful situations or increase the benefit of opportunities. The so called situation of interest (SOI) is described as a pattern, which represents a combination of events describing their temporal causality. Instead of just detecting SOIs, we search for possibilities to predict them. As patterns are the basis for reactive event processing, we also want to exploit them for proactive event processing. Therefore, we immerse into the world of pattern management to gain a better understanding of patterns, their structure and expressiveness.
{"title":"Partial pattern fulfillment and its application in event processing: poster","authors":"Suad Sejdovic","doi":"10.1145/2933267.2933537","DOIUrl":"https://doi.org/10.1145/2933267.2933537","url":null,"abstract":"Within the last few years, event processing has been gaining a lot of attention as it represents a powerful possibility to establish a real-time monitoring and situation detection. The real-time detection of situations allows a timely reaction and helps to reduce the damage made by harmful situations or increase the benefit of opportunities. The so called situation of interest (SOI) is described as a pattern, which represents a combination of events describing their temporal causality. Instead of just detecting SOIs, we search for possibilities to predict them. As patterns are the basis for reactive event processing, we also want to exploit them for proactive event processing. Therefore, we immerse into the world of pattern management to gain a better understanding of patterns, their structure and expressiveness.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"3 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":"125936122","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}
G. Marciani, M. Piu, M. Porretta, Matteo Nardelli, V. Cardellini
In this paper, we present a solution to the DEBS 2016 Grand Challenge that leverages Apache Flink, an open source platform for distributed stream and batch processing. We design the system architecture focusing on the exploitation of parallelism and memory efficiency so to enable an effective processing of high volume data streams on a distributed infrastructure. Our solution to the first query relies on a distributed and fine-grain approach for updating the post scores and determining partial ranks, which are then merged into a single final rank. Furthermore, changes in the final rank are identified so to update the output only if needed. The second query efficiently represents in-memory the evolving social graph and uses a customized Bron-Kerbosch algorithm to identify the largest communities active on a topic. We leverage on an in-memory caching system to keep the largest connected components which have been previously identified by the algorithm, thus saving computational time. The experimental results show that, on a portion of the dataset large half that provided for the Grand Challenge, our system can process up to 400 tuples/s with an average latency of 2.5 ms for the first query, and up to 370 tuples/s with an average latency of 2.7 ms for the second query.
{"title":"Real-time analysis of social networks leveraging the flink framework","authors":"G. Marciani, M. Piu, M. Porretta, Matteo Nardelli, V. Cardellini","doi":"10.1145/2933267.2933517","DOIUrl":"https://doi.org/10.1145/2933267.2933517","url":null,"abstract":"In this paper, we present a solution to the DEBS 2016 Grand Challenge that leverages Apache Flink, an open source platform for distributed stream and batch processing. We design the system architecture focusing on the exploitation of parallelism and memory efficiency so to enable an effective processing of high volume data streams on a distributed infrastructure. Our solution to the first query relies on a distributed and fine-grain approach for updating the post scores and determining partial ranks, which are then merged into a single final rank. Furthermore, changes in the final rank are identified so to update the output only if needed. The second query efficiently represents in-memory the evolving social graph and uses a customized Bron-Kerbosch algorithm to identify the largest communities active on a topic. We leverage on an in-memory caching system to keep the largest connected components which have been previously identified by the algorithm, thus saving computational time. The experimental results show that, on a portion of the dataset large half that provided for the Grand Challenge, our system can process up to 400 tuples/s with an average latency of 2.5 ms for the first query, and up to 370 tuples/s with an average latency of 2.7 ms for the second query.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"22 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":"129809512","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}
Kamalas Udomlamlert, Cosmas Krisna Adiputra, T. Hara
This paper presents our solution to 2016 DEBS Grand Challenge. We proposed our original program to efficiently calculate 2 continuous top-k queries on real-time social-network graph data. Our implementation tried to prevent processing of unaffected events by designing the algorithms to efficiently maintain the spare list of candidates of the top-k results. In addition, we improved the efficiency of the state-of-the-art algorithms to speed up the processing of the queries.
{"title":"Monitoring top-k on real-time dynamic social-network graphs","authors":"Kamalas Udomlamlert, Cosmas Krisna Adiputra, T. Hara","doi":"10.1145/2933267.2933510","DOIUrl":"https://doi.org/10.1145/2933267.2933510","url":null,"abstract":"This paper presents our solution to 2016 DEBS Grand Challenge. We proposed our original program to efficiently calculate 2 continuous top-k queries on real-time social-network graph data. Our implementation tried to prevent processing of unaffected events by designing the algorithms to efficiently maintain the spare list of candidates of the top-k results. In addition, we improved the efficiency of the state-of-the-art algorithms to speed up the processing of the queries.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"187 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":"121612352","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}
Sabeur Aridhi, Martin Brugnara, A. Montresor, Yannis Velegrakis
Distributed processing of large, dynamic graphs has recently received considerable attention, especially in domains such as the analytics of social networks, web graphs and spatial networks. k-core decomposition is one of the significant figures of merit that can be analyzed in graphs. Efficient algorithms to compute k-cores exist already, both in centralized and decentralized setting. Yet, these algorithms have been designed for static graphs, without significant support to deal with the addition or removal of nodes and edges. Typically, this challenge is handled by re-executing the algorithm again on the updated graph. In this work, we propose distributed k-core decomposition and maintenance algorithms for large dynamic graphs. The proposed algorithms exploit, as much as possible, the topology of the graph to compute all the k-cores and maintain them in streaming settings where edge insertions and removals happen frequently. The key idea of the maintenance strategy is that whenever the original graph is updated by the insertion/deletion of one or more edges, only a limited number of nodes need their coreness to be re-evaluated. We present an implementation of the proposed approach on top of the AKKA framework, and experimentally show the efficiency of our approach in the case of large dynamic networks.
{"title":"Distributed k-core decomposition and maintenance in large dynamic graphs","authors":"Sabeur Aridhi, Martin Brugnara, A. Montresor, Yannis Velegrakis","doi":"10.1145/2933267.2933299","DOIUrl":"https://doi.org/10.1145/2933267.2933299","url":null,"abstract":"Distributed processing of large, dynamic graphs has recently received considerable attention, especially in domains such as the analytics of social networks, web graphs and spatial networks. k-core decomposition is one of the significant figures of merit that can be analyzed in graphs. Efficient algorithms to compute k-cores exist already, both in centralized and decentralized setting. Yet, these algorithms have been designed for static graphs, without significant support to deal with the addition or removal of nodes and edges. Typically, this challenge is handled by re-executing the algorithm again on the updated graph. In this work, we propose distributed k-core decomposition and maintenance algorithms for large dynamic graphs. The proposed algorithms exploit, as much as possible, the topology of the graph to compute all the k-cores and maintain them in streaming settings where edge insertions and removals happen frequently. The key idea of the maintenance strategy is that whenever the original graph is updated by the insertion/deletion of one or more edges, only a limited number of nodes need their coreness to be re-evaluated. We present an implementation of the proposed approach on top of the AKKA framework, and experimentally show the efficiency of our approach in the case of large dynamic networks.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"5 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":"115518061","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}
Modern distributed stream processing systems (DSPS), such as Storm, typically provide a flexible programming model, where computation is specified as complicated UDFs and data is opaque to the system. While such a programming framework provides very high flexibility to the developers, it does not provide much semantic information to the system and hence it is hard to perform optimizations that has already been proved very effective in conventional stream systems. Examples include sharing computation among overlapping windows, co-partitioning operators to save communication overhead and efficient state migration during load balancing. In lieu of these challenges, we propose a new framework, which is designed to expose sufficient semantic information of the applications to enable the aforementioned effective optimizations, while on the other hand, maintaining the flexibility of Storm's original programming framework. Furthermore, we present new optimization algorithms to minimize the communication cost and state migration overhead for dynamic load balancing. We implement our framework on top of Storm and run an extensive experimental study to verify its effectiveness.
{"title":"Enorm: efficient window-based computation in large-scale distributed stream processing systems","authors":"Kasper Grud Skat Madsen, Yongluan Zhou, Li Su","doi":"10.1145/2933267.2933315","DOIUrl":"https://doi.org/10.1145/2933267.2933315","url":null,"abstract":"Modern distributed stream processing systems (DSPS), such as Storm, typically provide a flexible programming model, where computation is specified as complicated UDFs and data is opaque to the system. While such a programming framework provides very high flexibility to the developers, it does not provide much semantic information to the system and hence it is hard to perform optimizations that has already been proved very effective in conventional stream systems. Examples include sharing computation among overlapping windows, co-partitioning operators to save communication overhead and efficient state migration during load balancing. In lieu of these challenges, we propose a new framework, which is designed to expose sufficient semantic information of the applications to enable the aforementioned effective optimizations, while on the other hand, maintaining the flexibility of Storm's original programming framework. Furthermore, we present new optimization algorithms to minimize the communication cost and state migration overhead for dynamic load balancing. We implement our framework on top of Storm and run an extensive experimental study to verify its effectiveness.","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":"124582437","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}
Vinod Muthusamy, Aleksander Slominski, Vatche Isahagian, Rania Y. Khalaf, J. M. Reason, S. Rozsnyai
The execution of distributed applications are captured by the events generated by the individual components. However, understanding the behavior of these applications from their event logs can be a complex and error prone task, compounded by the fact that applications continuously change rendering any knowledge obsolete. We describe our experiences applying a suite of process-aware analytic tools to a number of real world scenarios, and distill our lessons learned. For example, we have seen that these tools are used iteratively, where insights gained at one stage inform the configuration decisions made at an earlier stage. As well, we have observed that data onboarding, where the raw data is cleaned and transformed, is the most critical stage in the pipeline and requires the most manual effort and domain knowledge. In particular, missing, inconsistent, and low-resolution event time stamps are recurring problems that require better solutions. The experiences and insights presented here will assist practitioners applying process analytic tools to real scenarios, and reveal to researchers some of the more pressing challenges in this space.
{"title":"Lessons learned using a process mining approach to analyze events from distributed applications","authors":"Vinod Muthusamy, Aleksander Slominski, Vatche Isahagian, Rania Y. Khalaf, J. M. Reason, S. Rozsnyai","doi":"10.1145/2933267.2933270","DOIUrl":"https://doi.org/10.1145/2933267.2933270","url":null,"abstract":"The execution of distributed applications are captured by the events generated by the individual components. However, understanding the behavior of these applications from their event logs can be a complex and error prone task, compounded by the fact that applications continuously change rendering any knowledge obsolete. We describe our experiences applying a suite of process-aware analytic tools to a number of real world scenarios, and distill our lessons learned. For example, we have seen that these tools are used iteratively, where insights gained at one stage inform the configuration decisions made at an earlier stage. As well, we have observed that data onboarding, where the raw data is cleaned and transformed, is the most critical stage in the pipeline and requires the most manual effort and domain knowledge. In particular, missing, inconsistent, and low-resolution event time stamps are recurring problems that require better solutions. The experiences and insights presented here will assist practitioners applying process analytic tools to real scenarios, and reveal to researchers some of the more pressing challenges in this space.","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":"116949878","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 many application scenarios, content based pub/sub systems are required to provide stringent service guarantees such as reliable delivery, high performance in terms of throughput and low latency for event notification to interested subscribers. Matching algorithm play a critical role in content based pub/sub systems. The aim of our work is design and development of parallel, scalable and high performance content based publish subscribe system. We parallelize event processing using thread based and multi GPU approaches. We achieved low latency and high throughput when pub/sub is deployed on Apache Storm, a real time event processing system. Throughput gain and reduction in matching time is nearly 48% and 40% respectively in multi GPGPU approach of event processing compared to earlier work mentioned in [1].
{"title":"Design and development of high performance, scalable content based publish subscribe system: doctoral symposium","authors":"Mrs. M. A. Shah, Walchand","doi":"10.1145/2933267.2933428","DOIUrl":"https://doi.org/10.1145/2933267.2933428","url":null,"abstract":"In many application scenarios, content based pub/sub systems are required to provide stringent service guarantees such as reliable delivery, high performance in terms of throughput and low latency for event notification to interested subscribers. Matching algorithm play a critical role in content based pub/sub systems. The aim of our work is design and development of parallel, scalable and high performance content based publish subscribe system. We parallelize event processing using thread based and multi GPU approaches. We achieved low latency and high throughput when pub/sub is deployed on Apache Storm, a real time event processing system. Throughput gain and reduction in matching time is nearly 48% and 40% respectively in multi GPGPU approach of event processing compared to earlier work mentioned in [1].","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"114 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":"117075842","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}
We propose, OMen, a distributed system for dynamically maintaining overlays for topic-based publish/subscribe (pub/sub) systems. In particular, OMen supports churn-resistant construction of topic-connected overlays (TCO), which organizes all nodes interested in the same topic in a directly connected dissemination sub-overlay. While aiming at pub/sub deployments in data centers, OMen internally leverages selected peer-to-peer technologies, such as T-Man as the underlying topology maintenance protocol. Existing approaches for constructing pub/sub TCOs are (i) centralized algorithms that guarantee low node degrees at the cost of prohibitive running time and (ii) decentralized protocols that are time efficient while lacking bounds on node degrees. We show both analytically and experimentally that OMen combines the best from both worlds. Namely, OMen achieves (i) low node degrees, close to centralized algorithms, and (ii) high efficiency, scalability, and load balance, comparable to decentralized protocols. Our evaluation uses both synthetic pub/sub workloads and real-world ones extracted from Facebook and Twitter. We generate churn traces with Google cluster data.
{"title":"OMen: overlay mending for topic-based publish/subscribe systems under churn","authors":"Chen Chen, R. Vitenberg, H. Jacobsen","doi":"10.1145/2933267.2933305","DOIUrl":"https://doi.org/10.1145/2933267.2933305","url":null,"abstract":"We propose, OMen, a distributed system for dynamically maintaining overlays for topic-based publish/subscribe (pub/sub) systems. In particular, OMen supports churn-resistant construction of topic-connected overlays (TCO), which organizes all nodes interested in the same topic in a directly connected dissemination sub-overlay. While aiming at pub/sub deployments in data centers, OMen internally leverages selected peer-to-peer technologies, such as T-Man as the underlying topology maintenance protocol. Existing approaches for constructing pub/sub TCOs are (i) centralized algorithms that guarantee low node degrees at the cost of prohibitive running time and (ii) decentralized protocols that are time efficient while lacking bounds on node degrees. We show both analytically and experimentally that OMen combines the best from both worlds. Namely, OMen achieves (i) low node degrees, close to centralized algorithms, and (ii) high efficiency, scalability, and load balance, comparable to decentralized protocols. Our evaluation uses both synthetic pub/sub workloads and real-world ones extracted from Facebook and Twitter. We generate churn traces with Google cluster data.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"61 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":"123464645","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}
Software Defined Networking (SDN) has emerged as an attractive solution to allow cloud-to-cloud interconnection and federation. SDN technologies, such as OpenFlow, use both reactive hop-by-hop and proactive approaches to program the switches. The reactive strategy incurs substantial scalability problems for large networks due to the hop-by-hop behavior while the proactive approach is hard to implement in practice due to the need to forecast all possible forwarding rules ahead-of-time. An attractive and more realistic alternative is the proactive overlay SDN approach, however, many challenges must first be overcome to realize it. Existing techniques to program the switches use low-level programming abstractions, which are error-prone and cannot scale. Middleware-based solutions, e.g., using XMPP, are stateful and hence also incur substantial scalability issues. Although content-based publish/subscribe (pub/sub) solutions have been used in the past for SDN, they rely on brokers, which is problematic and incurs unnecessary additional infrastructure elements that pollute the SDN architecture. To address these issues, this paper demonstrates how the strengths of the data-centric, broker-less pub/sub paradigm can be exploited to realize proactive overlay SDN for inter cloud domain federation. To that end, we first present the design rationale and architecture of our solution called POSEIDON (Proactive brOkerless SubscribEr Interest-Defined Overlay Networking). Second, we present the messaging protocol between the controller and switches. Finally, we present results of evaluating POSEIDON and illustrate how it improves data delivery and provides high performance at the network-level in proactive overlay SDN.
{"title":"Data-centric publish/subscribe routing middleware for realizing proactive overlay software-defined networking","authors":"Akram Hakiri, A. Gokhale","doi":"10.1145/2933267.2933314","DOIUrl":"https://doi.org/10.1145/2933267.2933314","url":null,"abstract":"Software Defined Networking (SDN) has emerged as an attractive solution to allow cloud-to-cloud interconnection and federation. SDN technologies, such as OpenFlow, use both reactive hop-by-hop and proactive approaches to program the switches. The reactive strategy incurs substantial scalability problems for large networks due to the hop-by-hop behavior while the proactive approach is hard to implement in practice due to the need to forecast all possible forwarding rules ahead-of-time. An attractive and more realistic alternative is the proactive overlay SDN approach, however, many challenges must first be overcome to realize it. Existing techniques to program the switches use low-level programming abstractions, which are error-prone and cannot scale. Middleware-based solutions, e.g., using XMPP, are stateful and hence also incur substantial scalability issues. Although content-based publish/subscribe (pub/sub) solutions have been used in the past for SDN, they rely on brokers, which is problematic and incurs unnecessary additional infrastructure elements that pollute the SDN architecture. To address these issues, this paper demonstrates how the strengths of the data-centric, broker-less pub/sub paradigm can be exploited to realize proactive overlay SDN for inter cloud domain federation. To that end, we first present the design rationale and architecture of our solution called POSEIDON (Proactive brOkerless SubscribEr Interest-Defined Overlay Networking). Second, we present the messaging protocol between the controller and switches. Finally, we present results of evaluating POSEIDON and illustrate how it improves data delivery and provides high performance at the network-level in proactive overlay SDN.","PeriodicalId":277061,"journal":{"name":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems","volume":"321 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":"133040985","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}
E. Kharlamov, S. Brandt, M. Giese, Ernesto Jiménez-Ruiz, Y. Kotidis, S. Lamparter, T. Mailis, C. Neuenstadt, Ö. Özçep, C. Pinkel, A. Soylu, C. Svingos, D. Zheleznyakov, Ian Horrocks, Y. Ioannidis, R. Möller, A. Waaler
Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work, we show how Semantic Technologies implemented in our system Optique can simplify such complex diagnostics by providing an abstraction layer---ontology---that integrates heterogeneous data. In a nutshell, Optique allows complex diagnostic tasks to be expressed with just a few high-level semantic queries, which can be easily formulated with our visual query formulation system. Optique can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment.
{"title":"Enabling semantic access to static and streaming distributed data with optique: demo","authors":"E. Kharlamov, S. Brandt, M. Giese, Ernesto Jiménez-Ruiz, Y. Kotidis, S. Lamparter, T. Mailis, C. Neuenstadt, Ö. Özçep, C. Pinkel, A. Soylu, C. Svingos, D. Zheleznyakov, Ian Horrocks, Y. Ioannidis, R. Möller, A. Waaler","doi":"10.1145/2933267.2933290","DOIUrl":"https://doi.org/10.1145/2933267.2933290","url":null,"abstract":"Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work, we show how Semantic Technologies implemented in our system Optique can simplify such complex diagnostics by providing an abstraction layer---ontology---that integrates heterogeneous data. In a nutshell, Optique allows complex diagnostic tasks to be expressed with just a few high-level semantic queries, which can be easily formulated with our visual query formulation system. Optique can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment.","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":"125390818","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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