Nikos Zacheilas, V. Kalogeraki, Y. Nikolakopoulos, Vincenzo Gulisano, M. Papatriantafilou, P. Tsigas
The problem of coping with the demands of determinism and meeting latency constraints is challenging in distributed data stream processing systems that have to process high volume data streams that arrive from different unsynchronized input sources. In order to deterministically process the streaming data, they need mechanisms that synchronize the order in which tuples are processed by the operators. On the other hand, achieving real-time response in such a system requires careful tradeoff between determinism and low latency performance. We build on a recently proposed approach to handle data exchange and synchronization in stream processing, namely ScaleGate, which comes with guarantees for determinism and an efficient lock-free implementation, enabling high scalability. Considering the challenge and trade-offs implied by real-time constraints, we propose a system which comprises (a) a novel data structure called Slack-ScaleGate (SSG), along with its algorithmic implementation; SSG enables us to guarantee the deterministic processing of tuples as long as they are able to meet their latency constraints, and (b) a method to dynamically tune the maximum amount of time that a tuple can wait in the SSG data-structure, relaxing the determinism guarantees when needed, in order to satisfy the latency constraints. Our detailed experimental evaluation using a traffic monitoring application deployed in the city of Dublin, illustrates the working and benefits of our approach.
{"title":"Maximizing Determinism in Stream Processing Under Latency Constraints","authors":"Nikos Zacheilas, V. Kalogeraki, Y. Nikolakopoulos, Vincenzo Gulisano, M. Papatriantafilou, P. Tsigas","doi":"10.1145/3093742.3093921","DOIUrl":"https://doi.org/10.1145/3093742.3093921","url":null,"abstract":"The problem of coping with the demands of determinism and meeting latency constraints is challenging in distributed data stream processing systems that have to process high volume data streams that arrive from different unsynchronized input sources. In order to deterministically process the streaming data, they need mechanisms that synchronize the order in which tuples are processed by the operators. On the other hand, achieving real-time response in such a system requires careful tradeoff between determinism and low latency performance. We build on a recently proposed approach to handle data exchange and synchronization in stream processing, namely ScaleGate, which comes with guarantees for determinism and an efficient lock-free implementation, enabling high scalability. Considering the challenge and trade-offs implied by real-time constraints, we propose a system which comprises (a) a novel data structure called Slack-ScaleGate (SSG), along with its algorithmic implementation; SSG enables us to guarantee the deterministic processing of tuples as long as they are able to meet their latency constraints, and (b) a method to dynamically tune the maximum amount of time that a tuple can wait in the SSG data-structure, relaxing the determinism guarantees when needed, in order to satisfy the latency constraints. Our detailed experimental evaluation using a traffic monitoring application deployed in the city of Dublin, illustrates the working and benefits of our approach.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"24 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":"124085094","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}
Daniel Ritter, Jonas Dann, Norman May, S. Rinderle-Ma
The growing number of (cloud) applications and devices massively increases the communication rate and volume pushing integration systems to their (throughput) limits. While the usage of modern hardware like Field Programmable Gate Arrays (FPGAs) led to low latency when employed for query and event processing, application integration adds yet unexplored processing opportunities. In this industry paper, we explore how to program integration semantics (e. g., message routing and transformation) in form of Enterprise Integration Patterns (EIP) on top of an FPGA, thus complementing the existing research on FPGA data processing. We focus on message routing, re-define the EIP for stream processing and propose modular hardware implementations as templates that are synthesized to circuits. For our real-world "connected car" scenario (i. e., composed patterns), we discuss common and new optimizations especially relevant for hardware integration processes. Our experimental evaluation shows competitive throughput compared to modern general-purpose CPUs and discusses the results.
{"title":"Hardware Accelerated Application Integration Processing: Industry Paper","authors":"Daniel Ritter, Jonas Dann, Norman May, S. Rinderle-Ma","doi":"10.1145/3093742.3093911","DOIUrl":"https://doi.org/10.1145/3093742.3093911","url":null,"abstract":"The growing number of (cloud) applications and devices massively increases the communication rate and volume pushing integration systems to their (throughput) limits. While the usage of modern hardware like Field Programmable Gate Arrays (FPGAs) led to low latency when employed for query and event processing, application integration adds yet unexplored processing opportunities. In this industry paper, we explore how to program integration semantics (e. g., message routing and transformation) in form of Enterprise Integration Patterns (EIP) on top of an FPGA, thus complementing the existing research on FPGA data processing. We focus on message routing, re-define the EIP for stream processing and propose modular hardware implementations as templates that are synthesized to circuits. For our real-world \"connected car\" scenario (i. e., composed patterns), we discuss common and new optimizations especially relevant for hardware integration processes. Our experimental evaluation shows competitive throughput compared to modern general-purpose CPUs and discusses the results.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"52 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":"115596417","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}
W. V. Raemdonck, T. V. Cutsem, K. S. Esmaili, M. Cortes, P. Dobbelaere, L. Hoste, Eline Philips, M. Roelands, L. Trappeniers
The connected car is likely to play a fundamental role in the foreseeable Internet of Things. The connectivity aspect in combination with the available data (e.g. from GPS, on-board diagnostics, road sensors) and video (e.g. from dashcams and traffic cameras) streams enable a range of new applications, e.g., accident avoidance, online route planning, energy optimization, etc. These applications, however, come with an additional set of requirements which are not accommodated by the state-of-the-art stream processing platforms. We have built World-Wide Streams (WWS), a novel stream processing platform that has been explicitly designed with those requirements in mind. In this demo presentation, we will show a number of connected car scenarios that we have built on top of WWS.
{"title":"Building Connected Car Applications on Top of the World-Wide Streams Platform: Demo","authors":"W. V. Raemdonck, T. V. Cutsem, K. S. Esmaili, M. Cortes, P. Dobbelaere, L. Hoste, Eline Philips, M. Roelands, L. Trappeniers","doi":"10.1145/3093742.3095088","DOIUrl":"https://doi.org/10.1145/3093742.3095088","url":null,"abstract":"The connected car is likely to play a fundamental role in the foreseeable Internet of Things. The connectivity aspect in combination with the available data (e.g. from GPS, on-board diagnostics, road sensors) and video (e.g. from dashcams and traffic cameras) streams enable a range of new applications, e.g., accident avoidance, online route planning, energy optimization, etc. These applications, however, come with an additional set of requirements which are not accommodated by the state-of-the-art stream processing platforms. We have built World-Wide Streams (WWS), a novel stream processing platform that has been explicitly designed with those requirements in mind. In this demo presentation, we will show a number of connected car scenarios that we have built on top of WWS.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"17 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":"127994953","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}
A. Gal, Nicolo Rivetti, Arik Senderovich, D. Gunopulos, I. Katakis, N. Panagiotou, V. Kalogeraki
Applications targeting Smart Cities tackle common challenges, however solutions are seldom portable from one city to another due to the heterogeneity of city ecosystems. A major obstacle involves the differences in the levels of available information. In this demonstration we present REMI, a reusable elements framework to handle varying degrees of information availability by design from two complementary angles, namely graceful degradation (GRADE) and data enrichment (DARE). In a nutshell, we develop reusable machine learning black boxes for mining and aggregating streaming data, either to infer missing data from available data, or to adapt expected accuracy based on data availability. We illustrate the proposed approach using tram data from the city of Warsaw.
{"title":"REMI, Reusable Elements for Multi-Level Information Availability: Demo","authors":"A. Gal, Nicolo Rivetti, Arik Senderovich, D. Gunopulos, I. Katakis, N. Panagiotou, V. Kalogeraki","doi":"10.1145/3093742.3095091","DOIUrl":"https://doi.org/10.1145/3093742.3095091","url":null,"abstract":"Applications targeting Smart Cities tackle common challenges, however solutions are seldom portable from one city to another due to the heterogeneity of city ecosystems. A major obstacle involves the differences in the levels of available information. In this demonstration we present REMI, a reusable elements framework to handle varying degrees of information availability by design from two complementary angles, namely graceful degradation (GRADE) and data enrichment (DARE). In a nutshell, we develop reusable machine learning black boxes for mining and aggregating streaming data, either to infer missing data from available data, or to adapt expected accuracy based on data availability. We illustrate the proposed approach using tram data from the city of Warsaw.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"15 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":"126425612","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}
CPS are interconnected systems that observe and manipulate real objects and processes. They allow dynamic extension and show emergent behavior which leads to dynamic decision-making processes that can change at runtime. They cannot always be easily understood because of the high number of components involved. If an error occurs in such a process, it is difficult to comprehend which component involved in the decision process is responsible for that error. The decision therefore has a high degree of dependency on the nodes involved in the process. Therefore, errors are not easily traceable to their original source. In this paper, we present the idea of dependency trees, which should help to identify error sources in the event of a fault.
{"title":"Cascading Data Corruption: About Dependencies in Cyber-Physical Systems: Poster","authors":"Stefan Gries, M. Hesenius, V. Gruhn","doi":"10.1145/3093742.3095092","DOIUrl":"https://doi.org/10.1145/3093742.3095092","url":null,"abstract":"CPS are interconnected systems that observe and manipulate real objects and processes. They allow dynamic extension and show emergent behavior which leads to dynamic decision-making processes that can change at runtime. They cannot always be easily understood because of the high number of components involved. If an error occurs in such a process, it is difficult to comprehend which component involved in the decision process is responsible for that error. The decision therefore has a high degree of dependency on the nodes involved in the process. Therefore, errors are not easily traceable to their original source. In this paper, we present the idea of dependency trees, which should help to identify error sources in the event of a fault.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"43 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":"128046304","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}
Stream Processors (SPs) continuously transform huge volumes of input streams with a computational model that is inherently distributed, scalable, and fault-tolerant. For these reasons they are used in application environments in which almost real-time computation is of paramount importance, such as stock option analysis, fraud detection systems, monitoring, and real-time data analytics for web applications. In many applicative domains, SPs are used in conjunction with data management systems such as transactional databases and data warehouses that store intermediate or final results produced by the SPs. However, SPs have no control on the consistency guarantees of the results produced on external components. We propose a novel approach that we name consistent stream processing that integrates the external state of databases within the SP and enforces consistency guarantees both on state updates and on external querying. We extend the computational model of SPs with transactions and we provide two possible strategies to enforce their transactional properties.
{"title":"Consistent Stream Processing: Doctoral Symposium","authors":"Lorenzo Affetti","doi":"10.1145/3093742.3093900","DOIUrl":"https://doi.org/10.1145/3093742.3093900","url":null,"abstract":"Stream Processors (SPs) continuously transform huge volumes of input streams with a computational model that is inherently distributed, scalable, and fault-tolerant. For these reasons they are used in application environments in which almost real-time computation is of paramount importance, such as stock option analysis, fraud detection systems, monitoring, and real-time data analytics for web applications. In many applicative domains, SPs are used in conjunction with data management systems such as transactional databases and data warehouses that store intermediate or final results produced by the SPs. However, SPs have no control on the consistency guarantees of the results produced on external components. We propose a novel approach that we name consistent stream processing that integrates the external state of databases within the SP and enforces consistency guarantees both on state updates and on external querying. We extend the computational model of SPs with transactions and we provide two possible strategies to enforce their transactional properties.","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":"131934885","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 this tutorial paper we present the results of recent research findings in the area of data streaming applied to stream processing systems. In particular, we introduce the data streaming model, detailing the main algorithmic results in this research field. We then move to detail how such algorithms can be applied to modern distributed stream processing systems to improve their efficiency. Finally we outline several open research directions in this field.
{"title":"Data Streaming and its Application to Stream Processing: Tutorial","authors":"Leonardo Querzoni, Nicolo Rivetti","doi":"10.1145/3093742.3095108","DOIUrl":"https://doi.org/10.1145/3093742.3095108","url":null,"abstract":"In this tutorial paper we present the results of recent research findings in the area of data streaming applied to stream processing systems. In particular, we introduce the data streaming model, detailing the main algorithmic results in this research field. We then move to detail how such algorithms can be applied to modern distributed stream processing systems to improve their efficiency. Finally we outline several open research directions in this field.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"62 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":"116973893","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}
Facebook is a social network that connects more than 1.8 billion people. To serve these many users requires infrastructure which is composed of thousands of interdependent systems that span geographically distributed data centers. But what is the guiding principle for building and operating these systems? For Facebook’s infrastructure teams the answer is: Systems must always be available and never lose data. This talk will explore this quest. We will focus on three aspects. Availability and consistency. What form of consistency do Facebook’s systems guarantee? Strong consistency makes understanding easy but has latency penalties, weak consistency is fast but difficult to reason for developers and users. We describe our usage of eventual consistency and delve into how Facebook constructs its caching and replicated storage systems to minimize the duration for achieving consistency. We share empirical data that measures the effectiveness of our design. Availability and correctness. With network partitions, relaxed forms of consistency, and software bugs, how do we guarantee a consistent state? We present two systems to find and repair structural errors in Facebook’s social graph, one batch and one real-time. Availability and scale. Sharding is one of the standard answers to operate at scale. But how can we develop one system that can shard storage as well as compute? We will introduce a new Sharding-as-a-Service component. We will show and evaluate how its design and service policies control for latency, failure tolerance and operationally efficiency. 1998 ACM Subject Classification Computer; C 1.4 Distributed Architectures; C.2.4 Distributed Systems; C.4 Fault Tolerance, Reliability, Availability and Serviceability; D 1.3 Distributed Programming; D 4.7 Distributed Systems; E 1 Distributed Data Structures
{"title":"Challenges to Achieving High Availability at Scale","authors":"W. Shulte","doi":"10.1145/3093742.3097270","DOIUrl":"https://doi.org/10.1145/3093742.3097270","url":null,"abstract":"Facebook is a social network that connects more than 1.8 billion people. To serve these many users requires infrastructure which is composed of thousands of interdependent systems that span geographically distributed data centers. But what is the guiding principle for building and operating these systems? For Facebook’s infrastructure teams the answer is: Systems must always be available and never lose data. This talk will explore this quest. We will focus on three aspects. Availability and consistency. What form of consistency do Facebook’s systems guarantee? Strong consistency makes understanding easy but has latency penalties, weak consistency is fast but difficult to reason for developers and users. We describe our usage of eventual consistency and delve into how Facebook constructs its caching and replicated storage systems to minimize the duration for achieving consistency. We share empirical data that measures the effectiveness of our design. Availability and correctness. With network partitions, relaxed forms of consistency, and software bugs, how do we guarantee a consistent state? We present two systems to find and repair structural errors in Facebook’s social graph, one batch and one real-time. Availability and scale. Sharding is one of the standard answers to operate at scale. But how can we develop one system that can shard storage as well as compute? We will introduce a new Sharding-as-a-Service component. We will show and evaluate how its design and service policies control for latency, failure tolerance and operationally efficiency. 1998 ACM Subject Classification Computer; C 1.4 Distributed Architectures; C.2.4 Distributed Systems; C.4 Fault Tolerance, Reliability, Availability and Serviceability; D 1.3 Distributed Programming; D 4.7 Distributed Systems; E 1 Distributed Data Structures","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"23 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":"115845317","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}
Current developments both in the area of sensors and actuators that can be planted within the human body, along with the advancements in the areas of big data analysis, and autonomous intelligent systems serves as a major revolution the approach to healthcare. Furthermore, it has a potential to make a major revolution in life as we know it by providing the gate to singularity that brings with it eternal (or at least very long) life. This is part of a general trend of autonomous systems that brings hope for the next level in the human evolution along with paradoxes and dilemmas. The tutorial concentrates upon the description of the current development that enable event processing within the human body, the notion of body area networks in general, and applications of this area to medical and non-medical applications. Some predictions about future trends in this area, especially the vision of singularity are discussed, and some philosophical observations about the implications are also discussed in detail
{"title":"Human Body Related Event Processing: Tutorial","authors":"O. Etzion","doi":"10.1145/3093742.3095109","DOIUrl":"https://doi.org/10.1145/3093742.3095109","url":null,"abstract":"Current developments both in the area of sensors and actuators that can be planted within the human body, along with the advancements in the areas of big data analysis, and autonomous intelligent systems serves as a major revolution the approach to healthcare. Furthermore, it has a potential to make a major revolution in life as we know it by providing the gate to singularity that brings with it eternal (or at least very long) life. This is part of a general trend of autonomous systems that brings hope for the next level in the human evolution along with paradoxes and dilemmas. The tutorial concentrates upon the description of the current development that enable event processing within the human body, the notion of body area networks in general, and applications of this area to medical and non-medical applications. Some predictions about future trends in this area, especially the vision of singularity are discussed, and some philosophical observations about the implications are also discussed in detail","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":"114997609","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}
Aurélien Havet, Rafael Pires, P. Felber, Marcelo Pasin, Romain Rouvoy, V. Schiavoni
The growing adoption of distributed data processing frameworks in a wide diversity of application domains challenges end-to-end integration of properties like security, in particular when considering deployments in the context of large-scale clusters or multi-tenant Cloud infrastructures. This paper therefore introduces SecureStreams, a reactive middleware framework to deploy and process secure streams at scale. Its design combines the high-level reactive dataflow programming paradigm with Intel®'s low-level software guard extensions (SGX) in order to guarantee privacy and integrity of the processed data. The experimental results of SecureStreams are promising: while offering a fluent scripting language based on Lua, our middleware delivers high processing throughput, thus enabling developers to implement secure processing pipelines in just few lines of code.
{"title":"SecureStreams: A Reactive Middleware Framework for Secure Data Stream Processing","authors":"Aurélien Havet, Rafael Pires, P. Felber, Marcelo Pasin, Romain Rouvoy, V. Schiavoni","doi":"10.1145/3093742.3093927","DOIUrl":"https://doi.org/10.1145/3093742.3093927","url":null,"abstract":"The growing adoption of distributed data processing frameworks in a wide diversity of application domains challenges end-to-end integration of properties like security, in particular when considering deployments in the context of large-scale clusters or multi-tenant Cloud infrastructures. This paper therefore introduces SecureStreams, a reactive middleware framework to deploy and process secure streams at scale. Its design combines the high-level reactive dataflow programming paradigm with Intel®'s low-level software guard extensions (SGX) in order to guarantee privacy and integrity of the processed data. The experimental results of SecureStreams are promising: while offering a fluent scripting language based on Lua, our middleware delivers high processing throughput, thus enabling developers to implement secure processing pipelines in just few lines of code.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"7 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":"124317525","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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