Distributed SPARQL Query Answering over RDF Data Streams

Business process logs are composed of event records generated, collected and analyzed at different locations, asynchronously and under the responsibility of different authorities. Their analysis is often delegated to auditors who have a mandate for monitoring processes and computing metrics but do not always have the rights to access the individual events used to compute them. A major challenge of this scenario is reconciling the requirements of privacy and access control with the need to continuously monitor and assess the business process. In this paper, we present a model, a language and a software toolkit for controlling access to process data where logs are made available as streams of RDF triples referring to some company-specific business ontology. Our approach is based on the novel idea of dynamic enforcement: we incrementally build dynamic filters for each process instance, based on the applicable access control policy and on the current prefix of the event stream. The implementation and performance validation of our solution is also presented.

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“…Zeus, our process analysis platform [43], is a PMon system based on the data model introduced in the previous section. As illustrated in Fig.…”

Section: Software Architecturementioning

confidence: 99%

“…In other words, when a monitor detects an activity, it generates the RDF triples representing it and sends them to the message queue. A message listener reads the message queue and inserts the triples into the Zeus triple store [43]. The Zeus triple store exposes a SPARQL query service allowing external applications to query it.…”

Section: Software Architecturementioning

confidence: 99%

Dynamic Access Control to Semantics-Aware Streamed Process Logs

et al. 2019

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“…Marcello Leida et al [58] propose a query processing architecture that can be used to efficiently process RDF graphs that are distributed over a local data grid. The architecture has no single point of failure and no specialized nodes -which is a different than Hadoop.…”

Section: Distributed Sparqlmentioning

confidence: 99%

Distributed SPARQL over Big RDF Data: A Comparative Analysis Using Presto and MapReduce

Mammo

Bansal

2015

2015 IEEE International Congress on Big Data

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The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational databases management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example. The results of the experiment show that Presto has a much higher performance than Hive can be used to process big RDF data. The thesis also proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data.ii DEDICATION This thesis is dedicated my parents for their love and support and to my brother who initiated the idea of coming to the US for my master's study and who sponsored my education.iii ACKNOWLEDGMENTS

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“…The query engine, unlike Facebook Presto, uses MapReduce similar to Husain, McGlothlin, Masud, Khan, & Thuraisingham, 2011 approach of using Hadoop MapReduce framework to store large RDF graphs and query them. Leida & Chu, 2013 propose a query processing architecture that can be used to efficiently 7 Apache Jena SPARQL Processor: jena.apache.org/documentation/query process RDF graphs that are distributed over a local data grid. They propose a sophisticated non-memory query planning and execution algorithm based on streaming RDF triples.…”

Section: Distributed Sparqlmentioning

confidence: 99%

“…Complex graph patterns can be constructed by combining few basic graph pattern techniques. The W3C classifies SPARQL graph pattern matching into five smaller patterns (Leida & Chu, 2013) Constraints can also be applied to optional graph patterns.…”

Section: Sparql Graph Patternsmentioning

confidence: 99%

Distributed SPARQL Querying Over Big RDF Data Using Presto-RDF

Mammo¹,

Hassan²,

Bansal³

2015

STBD

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The processing of large volumes of RDF data requires an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational database management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example. This paper proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data. We evaluate the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done with RDF datasets of size 10, 20, and 30 million triples. The results of the experiments show that Presto-RDF has a much higher performance than Hive and can be used to process big RDF data.

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Distributed SPARQL Query Answering over RDF Data Streams

Cited by 8 publications

References 15 publications

Dynamic Access Control to Semantics-Aware Streamed Process Logs

Dynamic Access Control to Semantics-Aware Streamed Process Logs

Distributed SPARQL over Big RDF Data: A Comparative Analysis Using Presto and MapReduce

Distributed SPARQL Querying Over Big RDF Data Using Presto-RDF

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