The Experts below are selected from a list of 3549 Experts worldwide ranked by ideXlab platform
Qiming Chen - One of the best experts on this subject based on the ideXlab platform.
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Page-Flow in Query Engine Grid
Studies in Computational Intelligence, 2020Co-Authors: Qiming Chen, Ren WuAbstract:As scaling out applications with multiple servers has become a popular industry practice, we investigate collaborating distributed Query Engines (QEs) to support graph-structured SQL dataflow processes. A SQL dataflow process consists of queries (optionally with UDFs) linked with relational dataflow. We focus on using Distributed Caching Platform (DCP) for inter-QEs data communication. While DCP has gained popularity lately, exchanging Query results tuple-by-tuple through DCP is often inefficient due to the tiny granularity of cache access and the overhead of data conversion and interpretation. This has motivated us to explore a new and more efficient mechanism for inter-QEs communication, taking advantage of DCP’s binary protocol. We propose the page-flow approach characterized by extending and externalizing the database buffer pool to DCP to allow the producer QE to put Query results as data pages (blocks) to the DCP to be retrieved by the consumer QE. In this way, the relational dataflow logically becomes binary page-flow; the tuples contained in the transferred pages are exactly in the format required by the relational operators thus can be feed in queries directly without any conversion. Further, using pages as mini-batches of tuples, enhances the latency of DCP access. We have implemented this mechanism on a cluster of PostgreSQL Engines. Our experiments results demonstrate its value.
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Cut-and-Rewind: Extending Query Engine for Continuous Stream Analytics
Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI, 2015Co-Authors: Qiming ChenAbstract:Combining data warehousing and stream processing technologies has great potential in offering low-latency data-intensive analytics. Unfortunately, such convergence has not been properly addressed so far. The current generation of stream processing systems is in general built separately from the data warehouse and Query Engine, which can cause significant overhead in data access and data movement, and is unable to take advantage of the functionalities already offered by the existing data warehouse systems.
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OTM Conferences (1) - SQL streaming process in Query Engine net
On the Move to Meaningful Internet Systems: OTM 2011, 2011Co-Authors: Qiming ChenAbstract:The massively growing data volume and the pressing need for low latency are pushing the traditional store-first-Query-later data warehousing technologies beyond their limits. Many enterprise applications are now based on continuous analytics of data streams. While integrating stream processing with Query processing takes advantage of SQL's expressive power and DBMS's data management capability, it raises serious challenges in dealing with complex dataflow, applying queries to unbounded stream data, and providing highly scalable, dynamically configurable, elastic infrastructure. To solve these problems, we model the general graph-structured, continuous dataflow analytics as a SQL Streaming Process with multiple connected and stationed continuous queries; then we extend the Query Engine to support cycle-based Query execution for processing unbounded stream data chunk-wise with sound semantics; and finally, we develop the Query Engine Net (QE-Net) over the Distributed Caching Platforms (DCP) as a dynamically configurable elastic infrastructure for parallel and distributed execution of SQL Streaming Processes. We extended the PostgreSQL Engines for building the QE-Net infrastructure. Our experience shows its merit in leveraging SQL and Query processing to analyze real-time, graph-structured and unbounded streams. Integrating it with a commercial and proprietary MPP based database cluster is being investigated.
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Globe - Query Engine grid for executing SQL streaming process
Lecture Notes in Computer Science, 2011Co-Authors: Qiming ChenAbstract:Many enterprise applications are based on continuous analytics of data streams. Integrating data-intensive stream processing with Query processing allows us to take advantage of SQL's expressive power and DBMS's data management capability. However, it also raises serious challenges in dealing with complex dataflow, applying queries to unbounded stream data, and providing highly scalable, dynamically configurable, elastic infrastructure. In this project we tackle these problems in three dimensions. First, we model the general graph-structured, continuous dataflow analytics as a SQL Streaming Process with multiple connected and stationed continuous queries. Next, we extend the Query Engine to support cycle-based Query execution for processing unbounded stream data in bounded chunks with sound semantics. Finally, we develop the Query Engine Grid (QE-Grid) over the Distributed Caching Platforms (DCP) as a dynamically configurable elastic infrastructure for parallel and distributed execution of SQL Streaming Processes. The proposed infrastructure is preliminarily implemented using PostgreSQL Engines. Our experience shows its merit in leveraging SQL and Query Engines to analyze real-time, graph-structured and unbounded streams. Integrating it with a commercial and proprietary MPP based database cluster is being investigated.
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SQL streaming process in Query Engine net
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011Co-Authors: Qiming Chen, Meichun HsuAbstract:Many enterprise applications are based on continuous analytics of data streams. Integrating data-intensive stream processing with Query processing allows us to take advantage of SQL's expressive power and DBMS's data management capability. However, it also raises serious challenges in dealing with complex dataflow, applying queries to unbounded stream data, and providing highly scalable, dynamically configurable, elastic infrastructure. In this project we tackle these problems in three dimensions. First, we model the general graph-structured, continuous dataflow analytics as a SQL Streaming Process with multiple connected and stationed continuous queries. Next, we extend the Query Engine to support cycle-based Query execution for processing unbounded stream data in bounded chunks with sound semantics. Finally, we develop the Query Engine Grid (QE-Grid) over the Distributed Caching Platforms (DCP) as a dynamically configurable elastic infrastructure for parallel and distributed execution of SQL Streaming Processes. The proposed infrastructure is preliminarily implemented using PostgreSQL Engines. Our experience shows its merit in leveraging SQL and Query Engines to analyze real-time, graph-structured and unbounded streams. Integrating it with a commercial and proprietary MPP based database cluster is being investigated. © 2011 Springer-Verlag.
Mark D Wilkinson - One of the best experts on this subject based on the ideXlab platform.
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share a semantic web Query Engine for bioinformatics
Asian Semantic Web Conference, 2009Co-Authors: Ben Vandervalk, Luke E Mccarthy, Mark D WilkinsonAbstract:Driven by the goal of automating data analyses in the field of bioinformatics, SHARE (Semantic Health and Research Environment) is a specialized SPARQL Engine that resolves queries against Web Services and SPARQL endpoints. Developed in conjunction with SHARE, SADI (Semantic Automated Discovery and Integration) is a standard for native-RDF services that facilitates the automated assembly of services into workflows, thereby eliminating the need for ad hoc scripting in the construction of a bioinformatics analysis pipeline.
Ruben Verborgh - One of the best experts on this subject based on the ideXlab platform.
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comunica a modular sparql Query Engine for the web
International Semantic Web Conference, 2018Co-Authors: Ruben Taelman, Joachim Van Herwegen, Miel Vander Sande, Ruben VerborghAbstract:Query evaluation over Linked Data sources has become a complex story, given the multitude of algorithms and techniques for single- and multi-source Querying, as well as the heterogeneity of Web interfaces through which data is published online. Today’s Query processors are insufficiently adaptable to test multiple Query Engine aspects in combination, such as evaluating the performance of a certain join algorithm over a federation of heterogeneous interfaces. The Semantic Web research community is in need of a flexible Query Engine that allows plugging in new components such as different algorithms, new or experimental SPARQL features, and support for new Web interfaces. We designed and developed a Web-friendly and modular meta Query Engine called Open image in new window that meets these specifications. In this article, we introduce this Query Engine and explain the architectural choices behind its design. We show how its modular nature makes it an ideal research platform for investigating new kinds of Linked Data interfaces and Querying algorithms. Comunica facilitates the development, testing, and evaluation of new Query processing capabilities, both in isolation and in combination with others.
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International Semantic Web Conference (2) - Comunica: A Modular SPARQL Query Engine for the Web
Lecture Notes in Computer Science, 2018Co-Authors: Ruben Taelman, Joachim Van Herwegen, Miel Vander Sande, Ruben VerborghAbstract:Query evaluation over Linked Data sources has become a complex story, given the multitude of algorithms and techniques for single- and multi-source Querying, as well as the heterogeneity of Web interfaces through which data is published online. Today’s Query processors are insufficiently adaptable to test multiple Query Engine aspects in combination, such as evaluating the performance of a certain join algorithm over a federation of heterogeneous interfaces. The Semantic Web research community is in need of a flexible Query Engine that allows plugging in new components such as different algorithms, new or experimental SPARQL features, and support for new Web interfaces. We designed and developed a Web-friendly and modular meta Query Engine called Open image in new window that meets these specifications. In this article, we introduce this Query Engine and explain the architectural choices behind its design. We show how its modular nature makes it an ideal research platform for investigating new kinds of Linked Data interfaces and Querying algorithms. Comunica facilitates the development, testing, and evaluation of new Query processing capabilities, both in isolation and in combination with others.
Charbel El Kaed - One of the best experts on this subject based on the ideXlab platform.
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FOrTÉ: A Federated Ontology and Timeseries Query Engine
2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber Physical and , 2017Co-Authors: Charbel El Kaed, Matthieu BoujonnierAbstract:The adoption of the Internet of things and cloud-connected objects promoted the proliferation of high-level applications aiming to analyze IoT generated data in order to propose value-added services. Such applications distinguish between at least two types of data: contextual information and timeseries. The contextual one, or graph, captures specific information regarding the connected things and their environment, while timeseries provide sampled values over time. Different storage technologies have emerged targeting exclusively graphs and ontologies or massive data which is more suited to timeseries data. However, combining the two worlds in order to provide a semantic scalable data storage seems to be required. We introduce FOrTE, a scalable federated Query Engine capable of bridging the gap between both storage technologies.
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iThings/GreenCom/CPSCom/SmartData - FOrTÉ: A Federated Ontology and Timeseries Query Engine
2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber Physical and , 2017Co-Authors: Charbel El Kaed, Matthieu BoujonnierAbstract:The adoption of the Internet of things and cloudconnected objects promoted the proliferation of high-level applications aiming to analyze IoT generated data in order to propose value-added services. Such applications distinguish between at least two types of data: contextual information and timeseries. The contextual one, or graph, captures specific information regarding the connected things and their environment, while timeseries provide sampled values over time. Different storage technologies have emerged targeting exclusively graphs and ontologies or massive data which is more suited to timeseries data. However, combining the two worlds in order to provide a semantic scalable data storage seems to be required. We introduce FOrTE, a scalable federated Query Engine capable of bridging the gap between both storage technologies.
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WF-IoT - SQenloT: Semantic Query Engine for industrial Internet-of-Things gateways
2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 2016Co-Authors: Charbel El Kaed, Hicham Hossayni, Imran Khan, Philippe NappeyAbstract:The Advent of Internet-of-Things (IoT) paradigm has brought exciting opportunities to solve many real-world problems. IoT in industries is poised to play an important role not only to increase productivity and efficiency but also to improve customer experiences. Two main challenges that are of particular interest to industry include: handling device heterogeneity and getting contextual information to make informed decisions. These challenges can be addressed by IoT along with proven technologies like the Semantic Web. In this paper, we present our work, SQenIoT: a Semantic Query Engine for Industrial IoT. SQenIoT resides on a commercial product and offers Query capabilities to retrieve information regarding the connected things in a given facility. We also propose a things Query language, targeted for resource-constrained gateways and non-technical personnel such as facility managers. Two other contributions include multi-level ontologies and mechanisms for semantic tagging in our commercial products. The implementation details of SQenIoT and its performance results are also presented.
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SQenloT: Semantic Query Engine for industrial Internet-of-Things gateways
2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 2016Co-Authors: Charbel El Kaed, Hicham Hossayni, Philippe NappeyAbstract:The Advent of Internet-of-Things (IoT) paradigm has brought exciting opportunities to solve many real-world problems. IoT in industries is poised to play an important role not only to increase productivity and efficiency but also to improve customer experiences. Two main challenges that are of particular interest to industry include: handling device heterogeneity and getting contextual information to make informed decisions. These challenges can be addressed by IoT along with proven technologies like the Semantic Web. In this paper, we present our work, SQenIoT: a Semantic Query Engine for Industrial IoT. SQenIoT resides on a commercial product and offers Query capabilities to retrieve information regarding the connected things in a given facility. We also propose a things Query language, targeted for resource-constrained gateways and non-technical personnel such as facility managers. Two other contributions include multi-level ontologies and mechanisms for semantic tagging in our commercial products. The implementation details of SQenIoT and its performance results are also presented.
Frank Van Harmelen - One of the best experts on this subject based on the ideXlab platform.
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Spinning the Semantic Web - Sesame: An Architecture for Storin gand Querying RDF Data and Schema Information.
2020Co-Authors: Jeen Broekstra, Arjohn Kampman, Frank Van HarmelenAbstract:RDF and RDF Schema provide the first W3C standard to enrich the Web with machine-processable semantic data. However, to be able to use this semantic data, a scalable, persistent RDF store and a powerful Query Engine using an expressive Query language are needed. Sesame is an extensible architecture implementing both of these. Sesame can be based on arbitrary repositories, ranging from traditional Data Base Management Systems, to dedicated RDF triple stores. Sesame also implements a Query Engine for RQL, the most powerful RDF/RDF Schema Query language to date.
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sesame an architecture for storin gand Querying rdf data and schema information
Spinning the Semantic Web, 2003Co-Authors: Jeen Broekstra, Arjohn Kampman, Frank Van HarmelenAbstract:RDF and RDF Schema provide the first W3C standard to enrich the Web with machine-processable semantic data. However, to be able to use this semantic data, a scalable, persistent RDF store and a powerful Query Engine using an expressive Query language are needed. Sesame is an extensible architecture implementing both of these. Sesame can be based on arbitrary repositories, ranging from traditional Data Base Management Systems, to dedicated RDF triple stores. Sesame also implements a Query Engine for RQL, the most powerful RDF/RDF Schema Query language to date.
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sesame a generic architecture for storing and Querying rdf and rdf schema
International Semantic Web Conference, 2002Co-Authors: Jeen Broekstra, Arjohn Kampman, Frank Van HarmelenAbstract:RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data.We have developed Sesame, an architecture for efficient storage and expressive Querying of large quantities of metadata in RDF and RDF Schema. Sesame's design and implementation are independent from any specific storage device. Thus, Sesame can be deployed on top of a variety of storage devices, such as relational databases, triple stores, or object-oriented databases, without having to change the Query Engine or other functional modules. Sesame offers support for concurrency control, independent export of RDF and RDFS information and a Query Engine for RQL, a Query language for RDF that offers native support for RDF Schema semantics. We present an overview of Sesame as a generic architecture, as well as its implementation and our first experiences with this implementation.
