Planning Ahead: Stream-Driven Linked-Data Access Under Update-Budget Constraints

Gao, Shen; Dell’Aglio, Daniele; Dehghanzadeh, Soheila; Bernstein, Abraham; Valle, Emanuele Della; Mileo, Alessandra

doi:10.1007/978-3-319-46523-4_16

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…Wrappers were created for the C-SPARQL engine, its internal stream representation, and a result listener was created to allow it to receive data from and push data to Kafka. An instance of the C-SPARQL engine 26 was created and set to listen the couch and TV sensor streams. Two C-SPARQL queries were set up to reduce the noise and volume of the data the data in the two streams.…”

Section: Methodsmentioning

confidence: 99%

“…The background data is often maintained externally, and RSP systems therefore usually maintain a local (or cached) version of the relevant parts for efficiency; however, current RSP models do not provide any means for refreshing this data. There has been some work on updating parts of the cached data automatically to maximize the freshness of the results [26], but current RSP implementations tend to treat background knowledge as completely static during the lifetime of a query.…”

Section: Limitations Of Current Rsp Approachesmentioning

confidence: 99%

“…Listing 2.10 shows a SPIN template defined based on the query in Listing 2.9. The vocabulary can also be used to define SPARQL functions and rules, which are the core mechanisms of the inferencing engine provided with the TopBraid SPIN API 26 . Hereafter we will refer to these vocabularies collectively simply as the SPIN vocabulary.…”

Section: Spinmentioning

confidence: 99%

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Towards Semantically Enabled Complex Event Processing

Keskisärkkä¹

2017

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The Semantic Web provides a framework for semantically annotating data on the web, and the Resource Description Framework (RDF) supports the integration of structured data represented in heterogeneous formats. Traditionally, the Semantic Web has focused primarily on more or less static data, but information on the web today is becoming increasingly dynamic. RDF Stream Processing (RSP) systems address this issue by adding support for streaming data and continuous query processing. To some extent, RSP systems can be used to perform complex event processing (CEP), where meaningful high-level events are generated based on low-level events from multiple sources; however, there are several challenges with respect to using RSP in this context. Event models designed to represent static event information lack several features required for CEP, and are typically not well suited for stream reasoning. The dynamic nature of streaming data also greatly complicates the development and validation of RSP queries. Therefore, reusing queries that have been prepared ahead of time is important to be able to support real-time decision- The research leading to the results reported in this thesis has been carried out at the

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Section: Methodsmentioning

confidence: 99%

Section: Limitations Of Current Rsp Approachesmentioning

confidence: 99%

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Towards Semantically Enabled Complex Event Processing

Keskisärkkä¹

2017

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“…In other words, the challenge is how to achieve the integration of the local and remote data without losing responsiveness. The initial effort [35,55] works in the setting of linked data integration of streaming and contextual data for query evaluation purposes. The idea is to adopt caches where to store a portion of the remote data, updating it depending on the recent stream content.…”

Section: Parallelizing and Distributing The Stream Reasonersmentioning

confidence: 99%

Stream reasoning: A survey and outlook

Dell’Aglio

Valle

Harmelen

et al. 2017

Self Cite

110

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Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and in this article we analyse its growth. In the first part, we trace the main results obtained so far, by presenting the most prominent studies. We start by an overview of the most relevant studies developed in the context of semantic web, and then we extend the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: in the second part, we present a set of open challenges and issues that stream reasoning will face in the next future.

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“…The only follow up work of ACQUA that we are aware of is [10]. It studied the maintenance process for a class of queries that extends the 1:1 join relationship of ACQUA work to M:N join, but that does not include FILTER clauses.…”

Section: Related Workmentioning

confidence: 99%

Using Rank Aggregation in Continuously Answering SPARQL Queries on Streaming and Quasi-static Linked Data

Zahmatkesh

Valle

Dell’Aglio

2017

Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems

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Web applications that combine dynamic data stream with distributed background data are getting a growing attention in recent years. Answering in a timely fashion, i.e., reactiveness, is one of the most important performance indicators for those applications.The Semantic Web community showed that RDF Stream Processing (RSP) is an adequate framework to develop this type of applications. However, RSP engines may lose their reactiveness due to the time necessary to access the background data when it is distributed over the Web. State-of-the-art RSP engines remain reactive using a local replica of the background data, but it progressively becomes stale if not updated to re ect the changes in the remote background data. For this reason, recently, the RSP community has investigated maintenance policies of the local replica that guarantee reactiveness while maximizing the freshness of the replica. Previous works simpli ed the problem with several assumptions.In this paper, we investigate how to remove some of those simplication assumptions. In particular, we target a class of queries for which multiple policies may be used simultaneously and we show that rank aggregation can be e ectively used to fairly consider their alternative suggestions. We provide extensive empirical evidence that rank aggregation is key to move a step forward to the practical solution of this problem in the RSP context.

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Planning Ahead: Stream-Driven Linked-Data Access Under Update-Budget Constraints

Cited by 8 publications

References 18 publications

Towards Semantically Enabled Complex Event Processing

Towards Semantically Enabled Complex Event Processing

Stream reasoning: A survey and outlook

Using Rank Aggregation in Continuously Answering SPARQL Queries on Streaming and Quasi-static Linked Data

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