https://iccl.inf.tu-dresden.de/w/api.php?action=feedcontributions&feedformat=atom&user=Long+Cheng 2026-04-17T14:40:11Z Benutzerbeiträge MediaWiki 1.43.1 https://iccl.inf.tu-dresden.de/w/index.php?title=Article3035&diff=22889 2017-03-07T21:44:12Z

<p>Long Cheng: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Long |ErsterAutorNachname=Cheng |FurtherAuthors=Ilias Tachmazidis; Spyros Kotoulas; Grigoris Antoniou; }} {{Art…“</p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Ilias Tachmazidis; Spyros Kotoulas; Grigoris Antoniou; <br /> }}<br /> {{Article<br /> |Referiert=1<br /> |Title=Design and Evaluation of Small-Large Outer Joins in Cloud Computing Environments<br /> |To appear=1<br /> |Year=2017<br /> |Journal=Journal of Parallel and Distributed Computing<br /> |Publisher=Elsevier<br /> }}<br /> {{Publikation Details<br /> |Abstract=Large-scale analytics is a key application area for data processing and parallel computing research. One of the most common (and challenging) operations in this domain is the join. Though inner join approaches have been extensively evaluated in parallel and distributed systems, there is little published work providing analysis of outer joins, especially in the extremely popular cloud computing environments. A common type of outer join is the small-large outer join, where one relation is relatively small and the other is large. Conventional implementations on this condition, such as one based on hash redistribution, often incur significant network communication, while the duplication-based approaches are complex and inefficient. In this work, we present a new method called DDR (duplication and direct redistribution), which aims to enable efficient small-large outer joins in cloud computing environments while being easy to implement using existing predicates in data processing frameworks. We present the detailed implementation of our approach and evaluate its performance through extensive experiments over the widely used MapReduce and Spark platforms. We show that the proposed method is scalable and can achieve significant performance improvements over the conventional approaches. Compared to the state-of-art method, the DDR algorithm is shown to be easier to implement and can achieve very similar or better performance under different outer join workloads, and thus, can be considered as a new option for current data analysis applications. Moreover, our detailed experimental results also have provided insights of current small-large outer join implementations, thereby allowing system developers to make a more informed choice for their data analysis applications.<br /> |DOI Name=10.1016/j.jpdc.2017.02.007<br /> |Projekt=Cfaed, DIAMOND, HAEC, HAEC B08<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings3139/en&diff=22678 2017-02-08T21:31:17Z

<p>Long Cheng: Page created automatically by parser function on page Inproceedings3139</p> <hr /> <div>#REDIRECT [[Inproceedings3139]]</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings3139&diff=22677 2017-02-08T21:31:16Z

<p>Long Cheng: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Long |ErsterAutorNachname=Cheng |FurtherAuthors=Tao Li }} {{Inproceedings |Referiert=1 |Title=Efficient Data Red…“</p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Tao Li<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Efficient Data Redistribution to Speedup Big Data Analytics in Large Systems<br /> |To appear=0<br /> |Year=2016<br /> |Month=Dezember<br /> |Booktitle=Proc. 23rd IEEE International Conference on High Performance Computing (HiPC'16)<br /> |Pages=91-100<br /> |Publisher=IEEE<br /> }}<br /> {{Publikation Details<br /> |Abstract=The performance of parallel data analytics systems becomes increasingly important with the rise of Big Data. An essential operation in such environment is parallel join, which always incurs significant cost on network communication. State-of-the-art approaches have achieved performance improvements over conventional implementations through minimizing network traffic or communication time. However, these approaches still face performance issues in the presence of big data and/or large-scale systems, due to their heavy overhead of data redistribution scheduling. In this paper, we propose near-join, a network-aware redistribution approach targeting to efficiently reduce both network traffic and communication time of join executions. Particularly, near-join is lightweight and adaptable to processing large datasets over large systems. We present the details of our algorithm and its implementation. The experiments performed on a cluster of up to 400 nodes and datasets of about 100GB have demonstrated that our scheduling algorithm is much faster than the state-of-the-art methods. Moreover, our join implementation can also achieve speedups over the conventional approaches.<br /> |ISBN=978-1-5090-5411-4<br /> |Download=PID4490265.pdf<br /> |DOI Name=10.1109/HiPC.2016.020<br /> |Projekt=Cfaed, DIAMOND, HAEC, HAEC B08<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:PID4490265.pdf&diff=22676 2017-02-08T21:29:09Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings3079&diff=21059 2016-09-01T08:12:09Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas;<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Efficient Large Outer Joins over MapReduce<br /> |To appear=0<br /> |Year=2016<br /> |Month=August<br /> |Booktitle=Proc. 22nd International European Conference on Parallel Processing (Euro-Par'16)<br /> |Pages=334-346<br /> |Publisher=Springer<br /> }}<br /> {{Publikation Details<br /> |Abstract=Big Data analytics largely rely on being able to execute large joins efficiently. Though inner join approaches have been extensively evaluated in parallel and distributed systems, there is little published work providing analysis of outer joins, especially on the extremely popular MapReduce platform. In this paper, we studied several current algorithms/techniques used in large outer joins. We find that some of them could meet performance bottlenecks in the presence of data skew, while others could be complex and incur significant coordination overheads when applied to the MapReduce framework. In this light, we propose a new algorithm, called POPI (Partial Outer join &amp; Partial Inner join), which targets for efficient processing large outer joins, and most important, is lightweight and adapted to the processing model of MapReduce. We implement our method in Pig and evaluate its performance on a Hadoop cluster of up to 256 cores and datasets of 1 billion tuples. Experimental results show that our method is scalable, robust and outperforms current implementations, at least in the case of high skew.<br /> |ISBN=978-3-319-43658-6<br /> |ISSN=0302-9743<br /> |Download=2016-europar.pdf<br /> |DOI Name=10.1007/978-3-319-43659-3_25<br /> |Projekt=DIAMOND, HAEC B08<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:2016-europar.pdf&diff=21058 2016-09-01T08:09:54Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3019&diff=21057 2016-09-01T08:06:28Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas;<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Scale-Out Processing of Large RDF Datasets<br /> |To appear=0<br /> |Year=2015<br /> |Month=Dezember<br /> |Journal=IEEE Transactions on Big Data<br /> |Volume=1<br /> |Number=4<br /> |Pages=138-150<br /> |Publisher=IEEE<br /> }}<br /> {{Publikation Details<br /> |Abstract=Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Regardless, current methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose efficient methods for processing RDF using dynamic data re-partitioning to enable rapid analysis of large datasets. Our approach adopts a two-tier index architecture on each computation node: (1) a lightweight primary index, to keep loading times low, and (2) a series of dynamic, multi-level secondary indexes, calculated as a by-product of query execution, to decrease or remove inter-machine data movement for subsequent queries that contain the same graph patterns. In addition, we propose methods to replace some secondary indexes with distributed filters, so as to decrease memory consumption. Experimental results on a commodity cluster with 16 nodes show that the method presents good scale-out characteristics and can indeed vastly improve loading speeds while remaining competitive in terms of performance. Specifically, our approach can load a dataset of 1.1 billion triples at a rate of 2.48 million triples per second and provide competitive performance to RDF-3X and 4store for expensive queries.<br /> |Download=2015-tbd.pdf<br /> |Projekt=DIAMOND, HAEC B08<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> }}<br /> {{Forschungsgebiet Auswahl<br /> |Forschungsgebiet=Semantische Technologien<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=21056 2016-09-01T08:05:57Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=1<br /> |Title=Fast Compression of Large Semantic Web Data using X10<br /> |To appear=0<br /> |Year=2016<br /> |Month=September<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Volume=27<br /> |Number=9<br /> |Pages=2603-2617<br /> |Publisher=IEEE<br /> |Note=This paper is the extended journal version of the article [[Inproceedings4049/en|Efficient Parallel Dictionary Encoding for RDF Data]]. The source code in X10 is available at: https://github.com/longcheng11/rdf_encoding<br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Download=Encodings.pdf<br /> |DOI Name=10.1109/TPDS.2015.2496579<br /> |Projekt=DIAMOND, HAEC B08<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> }}<br /> {{Forschungsgebiet Auswahl<br /> |Forschungsgebiet=Semantische Technologien<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=21055 2016-09-01T08:05:30Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=1<br /> |Title=Fast Compression of Large Semantic Web Data using X10<br /> |To appear=1<br /> |Year=2016<br /> |Month=September<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Volume=27<br /> |Number=9<br /> |Pages=2603-2617<br /> |Publisher=IEEE<br /> |Note=This paper is the extended journal version of the article [[Inproceedings4049/en|Efficient Parallel Dictionary Encoding for RDF Data]]. The source code in X10 is available at: https://github.com/longcheng11/rdf_encoding<br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Download=Encodings.pdf<br /> |DOI Name=10.1109/TPDS.2015.2496579<br /> |Projekt=DIAMOND, HAEC B08<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> }}<br /> {{Forschungsgebiet Auswahl<br /> |Forschungsgebiet=Semantische Technologien<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3019&diff=21054 2016-09-01T08:04:16Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas;<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Scale-Out Processing of Large RDF Datasets<br /> |To appear=0<br /> |Year=2015<br /> |Journal=IEEE Transactions on Big Data<br /> |Volume=1<br /> |Number=4<br /> |Pages=138-150<br /> |Publisher=IEEE<br /> }}<br /> {{Publikation Details<br /> |Abstract=Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Regardless, current methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose efficient methods for processing RDF using dynamic data re-partitioning to enable rapid analysis of large datasets. Our approach adopts a two-tier index architecture on each computation node: (1) a lightweight primary index, to keep loading times low, and (2) a series of dynamic, multi-level secondary indexes, calculated as a by-product of query execution, to decrease or remove inter-machine data movement for subsequent queries that contain the same graph patterns. In addition, we propose methods to replace some secondary indexes with distributed filters, so as to decrease memory consumption. Experimental results on a commodity cluster with 16 nodes show that the method presents good scale-out characteristics and can indeed vastly improve loading speeds while remaining competitive in terms of performance. Specifically, our approach can load a dataset of 1.1 billion triples at a rate of 2.48 million triples per second and provide competitive performance to RDF-3X and 4store for expensive queries.<br /> |Download=2015-tbd.pdf<br /> |Projekt=DIAMOND, HAEC B08<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> }}<br /> {{Forschungsgebiet Auswahl<br /> |Forschungsgebiet=Semantische Technologien<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=20741 2016-07-11T20:05:15Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Wissensbasierte Systeme<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=1<br /> |Telefon=+49 351 463 43510<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=APB 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently working as a Post-Doctoral Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at TU Dresden. My research interests mainly include:<br /> <br /> * Distributed computing<br /> * Large-scale data processing<br /> * Data management<br /> * Semantic web.<br /> |Info EN=I am currently working as a Post-Doctoral Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at TU Dresden. My research interests mainly include:<br /> <br /> * Distributed computing<br /> * Large-scale data processing<br /> * Data management<br /> * Semantic web.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Alternative URI=http://www.win.tue.nl/~lcheng/<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings3079/en&diff=19354 2016-04-30T10:52:03Z

<p>Long Cheng: Page created automatically by parser function on page Inproceedings3079</p> <hr /> <div>#REDIRECT [[Inproceedings3079]]</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings3079&diff=19353 2016-04-30T10:52:01Z

<p>Long Cheng: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Long |ErsterAutorNachname=Cheng |FurtherAuthors=Spyros Kotoulas; }} {{Inproceedings |Referiert=1 |Title=Efficie…“</p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas; <br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Efficient Large Outer Joins over MapReduce<br /> |To appear=0<br /> |Year=2016<br /> |Month=August<br /> |Booktitle=Proc. 22nd International European Conference on Parallel Processing (Euro-Par'16)<br /> |Publisher=Springer<br /> }}<br /> {{Publikation Details<br /> |Abstract=Big Data analytics largely rely on being able to execute large joins efficiently. Though inner join approaches have been extensively evaluated in parallel and distributed systems, there is little published work providing analysis of outer joins, especially on the extremely popular MapReduce platform. In this paper, we studied several current algorithms/techniques used in large outer joins. We find that some of them could meet performance bottlenecks in the presence of data skew, while others could be complex and incur significant coordination overheads when applied to the MapReduce framework. In this light, we propose a new algorithm, called POPI (Partial Outer join &amp; Partial Inner join), which targets for efficient processing large outer joins, and most important, is lightweight and adapted to the processing model of MapReduce. We implement our method in Pig and evaluate its performance on a Hadoop cluster of up to 256 cores and datasets of 1 billion tuples. Experimental results show that our method is scalable, robust and outperforms current implementations, at least in the case of high skew.<br /> |Projekt=DIAMOND, HAEC B08<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3019/en&diff=17659 2015-12-02T09:31:41Z

<p>Long Cheng: Page created automatically by parser function on page Article3019</p> <hr /> <div>#REDIRECT [[Article3019]]</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3019&diff=17658 2015-12-02T09:31:41Z

<p>Long Cheng: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Long |ErsterAutorNachname=Cheng |FurtherAuthors=Spyros Kotoulas; }} {{Article |Referiert=0 |Title=Scale-Out Pro…“</p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas; <br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Scale-Out Processing of Large RDF Datasets<br /> |To appear=1<br /> |Year=2016<br /> |Journal=IEEE Transactions on Big Data<br /> |Note=In press<br /> }}<br /> {{Publikation Details<br /> |Abstract=Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Regardless, current methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose efficient methods for processing RDF using dynamic data re-partitioning to enable rapid analysis of large datasets. Our approach adopts a two-tier index architecture on each computation node: (1) a lightweight primary index, to keep loading times low, and (2) a series of dynamic, multi-level secondary indexes, calculated as a by-product of query execution, to decrease or remove inter-machine data movement for subsequent queries that contain the same graph patterns. In addition, we propose methods to replace some secondary indexes with distributed filters, so as to decrease memory consumption. Experimental results on a commodity cluster with 16 nodes show that the method presents good scale-out characteristics and can indeed vastly improve loading speeds while remaining competitive in terms of performance. Specifically, our approach can load a dataset of 1.1 billion triples at a rate of 2.48 million triples per second and provide competitive performance to RDF-3X and 4store for expensive queries.<br /> |Download=2015-tbd.pdf<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:2015-tbd.pdf&diff=17657 2015-12-02T09:29:37Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=17501 2015-11-10T10:25:24Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Fast Compression of Large Semantic Web using X10<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Note=This paper is the extended journal version of the article [[Inproceedings4049/en|Efficient Parallel Dictionary Encoding for RDF Data]]. The source code in X10 is available at: https://github.com/longcheng11/rdf_encoding <br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Download=Encodings.pdf<br /> |DOI Name=10.1109/TPDS.2015.2496579<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:Encodings.pdf&diff=17499 2015-11-10T10:23:30Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=17445 2015-10-29T11:05:17Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Fast Compression of Large Semantic Web using X10<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Note=This paper is the extended journal version of the article [[Inproceedings4049/en|Efficient Parallel Dictionary Encoding for RDF Data]].<br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Download=Tpds encoding.pdf<br /> |DOI Name=10.1109/TPDS.2015.2496579<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=17412 2015-10-27T22:51:57Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Fast Compression of Large Semantic Web using X10<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Note=This paper is the extended journal version of the article [[Inproceedings4049/en|Efficient Parallel Dictionary Encoding for RDF Data]].<br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Download=Tpds encoding.pdf<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:Tpds_encoding.pdf&diff=17411 2015-10-27T22:51:41Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=17408 2015-10-27T13:08:31Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Fast Compression of Large Semantic Web using X10<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Note=This paper is the extended journal version of the article [[Inproceedings4049/en|Efficient Parallel Dictionary Encoding for RDF Data]].<br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Download=Tpds encodings.pdf<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:Tpds_encodings.pdf&diff=17407 2015-10-27T13:08:23Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings4049&diff=17406 2015-10-27T12:57:18Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Efficient Parallel Dictionary Encoding for RDF Data<br /> |To appear=0<br /> |Year=2014<br /> |Month=Juni<br /> |Booktitle=Proc. 17th International Workshop on the Web and Databases (WebDB'14)<br /> |Note=An extended version of this work is the journal article [[Article3018/en|Fast Compression of Large Semantic Web using X10]]<br /> }}<br /> {{Publikation Details<br /> |Abstract=The SemanticWeb comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of SemanticWeb applications that perform computations over large volumes of information. A typical method for alleviating the impact of this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding for this purpose is particularly prevalent in Semantic Web database systems. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we describe a straightforward but very efficient encoding algorithm and evaluate its performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art MapReduce algorithm, we demonstrate a speedup of 2.6 - 7.4x and excellent scalability.<br /> |Download=2014-Efficient-Parallel.pdf<br /> |Projekt=DIAMOND<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=17405 2015-10-27T12:53:59Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Fast Compression of Large Semantic Web using X10<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Note=This paper is the extended journal version of the article [[Inproceedings4049/en|Efficient Parallel Dictionary Encoding for RDF Data]].<br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018/en&diff=17404 2015-10-27T12:50:00Z

<p>Long Cheng: Page created automatically by parser function on page Article3018</p> <hr /> <div>#REDIRECT [[Article3018]]</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3018&diff=17403 2015-10-27T12:49:59Z

<p>Long Cheng: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Long |ErsterAutorNachname=Cheng |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theod…“</p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Fast Compression of Large Semantic Web using X10<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Parallel and Distributed Systems<br /> |Note=in press<br /> }}<br /> {{Publikation Details<br /> |Abstract=The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings4049&diff=17402 2015-10-27T12:44:45Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Avinash Malik; Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Efficient Parallel Dictionary Encoding for RDF Data<br /> |To appear=0<br /> |Year=2014<br /> |Month=Juni<br /> |Booktitle=Proc. 17th International Workshop on the Web and Databases (WebDB'14)<br /> }}<br /> {{Publikation Details<br /> |Abstract=The SemanticWeb comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of SemanticWeb applications that perform computations over large volumes of information. A typical method for alleviating the impact of this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding for this purpose is particularly prevalent in Semantic Web database systems. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we describe a straightforward but very efficient encoding algorithm and evaluate its performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art MapReduce algorithm, we demonstrate a speedup of 2.6 - 7.4x and excellent scalability.<br /> |Download=2014-Efficient-Parallel.pdf<br /> |Projekt=DIAMOND<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3016&diff=17401 2015-10-27T12:40:34Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas;<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Efficient Skew Handling for Outer Joins in a Cloud Computing Environment<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Cloud Computing<br /> |Note=In press.<br /> }}<br /> {{Publikation Details<br /> |Abstract=Outer joins are ubiquitous in many workloads and Big Data systems. The question of how to best execute outer joins in large parallel systems is particularly challenging, as real world datasets are characterized by data skew leading to performance issues. Although skew handling techniques have been extensively studied for inner joins, there is little published work solving the corresponding problem for parallel outer joins, especially in the extremely popular Cloud computing environment. Conventional approaches to the problem such as ones based on hash redistribution often lead to load balancing problems while duplication-based approaches incur significant overhead in terms of network communication. In this paper, we propose a new approach for efficient skew handling in outer joins over a Cloud computing environment. We present an efficient implementation of our approach over the Spark framework. We evaluate the performance of our approach on a 192-core system with large test datasets in excess of 100GB and with varying skew. Experimental results show that our approach is scalable and, at least of in cases of high skew, significantly faster than the state-of-the-art.<br /> |Download=2015-tcc-cheng.pdf<br /> |DOI Name=10.1109/TCC.2015.2487965<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3016&diff=17180 2015-10-05T15:06:36Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas;<br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Efficient Skew Handling for Outer Joins in a Cloud Computing Environment<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Cloud Computing<br /> |Note=In press.<br /> }}<br /> {{Publikation Details<br /> |Abstract=Outer joins are ubiquitous in many workloads and Big Data systems. The question of how to best execute outer joins in large parallel systems is particularly challenging, as real world datasets are characterized by data skew leading to performance issues. Although skew handling techniques have been extensively studied for inner joins, there is little published work solving the corresponding problem for parallel outer joins, especially in the extremely popular Cloud computing environment. Conventional approaches to the problem such as ones based on hash redistribution often lead to load balancing problems while duplication-based approaches incur significant overhead in terms of network communication. In this paper, we propose a new approach for efficient skew handling in outer joins over a Cloud computing environment. We present an efficient implementation of our approach over the Spark framework. We evaluate the performance of our approach on a 192-core system with large test datasets in excess of 100GB and with varying skew. Experimental results show that our approach is scalable and, at least of in cases of high skew, significantly faster than the state-of-the-art.<br /> |Download=2015-tcc-cheng.pdf<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:2015-tcc-cheng.pdf&diff=17178 2015-10-05T15:06:18Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3016/en&diff=17161 2015-10-02T11:09:12Z

<p>Long Cheng: Page created automatically by parser function on page Article3016</p> <hr /> <div>#REDIRECT [[Article3016]]</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Article3016&diff=17160 2015-10-02T11:09:12Z

<p>Long Cheng: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Long |ErsterAutorNachname=Cheng |FurtherAuthors=Spyros Kotoulas; }} {{Article |Referiert=0 |Title=Efficient Ske…“</p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas; <br /> }}<br /> {{Article<br /> |Referiert=0<br /> |Title=Efficient Skew Handling for Outer Joins in a Cloud Computing Environment<br /> |To appear=1<br /> |Year=2015<br /> |Journal=IEEE Transactions on Cloud Computing<br /> |Note=In press.<br /> }}<br /> {{Publikation Details<br /> |Abstract=Outer joins are ubiquitous in many workloads and Big Data systems. The question of how to best execute outer joins in large parallel systems is particularly challenging, as real world datasets are characterized by data skew leading to performance issues. Although skew handling techniques have been extensively studied for inner joins, there is little published work solving the corresponding problem for parallel outer joins, especially in the extremely popular Cloud computing environment. Conventional approaches to the problem such as ones based on hash redistribution often lead to load balancing problems while duplication-based approaches incur significant overhead in terms of network communication. In this paper, we propose a new approach for efficient skew handling in outer joins over a Cloud computing environment. We present an efficient implementation of our approach over the Spark framework. We evaluate the performance of our approach on a 192-core system with large test datasets in excess of 100GB and with varying skew. Experimental results show that our approach is scalable and, at least of in cases of high skew, significantly faster than the state-of-the-art.<br /> |Projekt=DIAMOND, HAEC<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=HAEC&diff=17159 2015-10-02T11:03:38Z

<p>Long Cheng: </p> <hr /> <div>{{Projekt<br /> |Kurzname=HAEC<br /> |Name=Highly Adaptive Energy-Efficient Computing (Sonderforschungsbereich 912)<br /> |Name EN=Highly Adaptive Energy-Efficient Computing (Collaborative Research Centre SFB 912)<br /> |Beschreibung DE=Die Art und Weise, wie wir heutzutage das Internet verwenden, hat einen enormen ökonomischen Einfluss auf den Energieverbrauch unserer modernen Gesellschaft. Nimmt man zum Beispiel alle Server des Internets zusammen und berechnet Ihren Stromverbrauch, dann kommt man auf eine Zahl, die ungefähr 2% des jährlichen Stromverbrauchs der USA1 und ca. 26% des Gesamtenergieverbrauchs in Deutschland entspricht. Die Mission des Sonderforschungsbereichs „Hochadaptive Energieeffiziente Systeme“ (HAEC) ist es daher hochgradig energieeffiziente Systeme für unsere moderne IT Infrastruktur zu entwickeln, ohne dabei auf das Leistungspotential dieser Systeme zu verzichten.<br /> <br /> Natürlich wäre es ein einfacher Weg den Energieverbrauch jeder einzelnen Systemkomponente zu minimieren. Allerdings arbeiten die meisten Hard- und Softwarekomponenten heute schon auf ihrem Energie/Leistungsoptimum. Zielführender ist es daher ein generelles Verständnis dafür zu entwickeln, wie Software auf Hardwarekomponenten angepasst werden kann und umgekehrt, wie sich Hardwarekomponenten an den schwankenden Ressourcenbedarf der Software anpassen können. Für eine solche Anpassung sind neue Methoden und Werkzeuge nötig, die es Programmierern erlauben energiebewusste Software zu entwickeln. Neue Interaktions- und Kommunikationslösungen werden gebraucht, die eine Vielzahl an Softwarekomponenten auf einem hochgradig parallelen System unterstützten. Mit anderen Worten, wir brauchen einen neuen integrierten Ansatz zur Entwicklung hochadaptiver energieeffizienter Systeme, der alle Ebenen moderner Systeme umfasst.<br /> <br /> Der Sonderforschungsbereich HAEC ist ein erster Versuch eines solchen integrativen Ansatzes. Auf Schaltkreisebene versuchen wir innovative Ideen für optische und kabellose Chip zu Chip Kommunikationsmedien zu entwickeln. In der nächsthöheren Netzwerkschicht erforschen wir sichere Hochleistungskodierungsschemata für Netzwerkströme. Kontroll- und Koordinierungslösungen werden untersucht, um die Anpassung der Hardware- und Softwarekomponenten zu steuern. Neue Techniken zur Softwareentwicklung werden durch energiebewusste Ausführungsumgebungen bereitgestellt, die eng mit den Ressourcen-, Strom- und Konfigurationsmanagement Algorithmen auf Betriebssystem -und Anwendungsebene zusammenspielen und neue Internet Applikationen werden durch energiebewusste Dienste ermöglicht. Darüber hinaus erforschen wir formale Methoden, die in der Lage sein werden, ein neues Level an Vertrauen in die entwickelten Systeme zu liefern, da sie die Einhaltung von Garantien formal beweisen können. Unser Ziel ist es die Ergebnisse all diese Entwicklungen in einem Prototypen zu demonstrieren – der HAEC Box – und somit die antreibende Kraft hinter der akademischen und industriellen Entwicklung zukünftiger energieeffizienter Systeme zu werden.<br /> |Beschreibung EN=The way we use the Internet today has an enormous ecological impact on the increasing energy demand of modern society. For example, the electricity required by the servers that make up the Internet relates to about 2% of the overall electricity consumption in the US1 and to about 26% of the overall energy consumption in Germany. The mission of the collaborative research center “Highly Adaptive Energy-Efficient Computing” (HAEC) is to enable high energy efficiency in today’s computing systems without compromising on high performance.<br /> <br /> <br /> Certainly a straightforward way for improving energy efficiency would be to reduce the energy consumption of every individual hardware component involved. However, for many components the optimal energy performance point has already been reached. More important is an understanding of how software can adapt to hardware components and vice versa to address the computational problems of modern society in an energy efficient way. This requires new methods and tools to write energy-aware programs, new ways of interaction between the individual pieces that collaborate to solve a problem, new communication technologies to enable this interaction between pieces that are spread across highly scalable parallel systems, and a new multi-layer coordination infrastructure to bring together these technologies. In other words, we need an integrated approach for highly adaptive energy-efficient computing to approach energy-efficient computing at all involved technology levels. <br /> <br /> The collaborative research center HAEC is a first attempt to achieve high adaptivity and energy efficiency in such an integrated approach. At the circuit level, we focus on innovative ideas for optical and wireless chip-to-chip communication. At the network level, we research secure, high performance network coding schemes for wired and wireless board-to-board communication. Innovative results at the hardware/software interface level will include energy control loops, which allow hardware to adapt to varying software requirements and vice versa. Software development in general is supported by energy-aware runtimes, energy-aware resource, stream and configuration management schemes and by an analysis framework for high performance/low energy applications. New internet applications are supported by innovations in energy-aware service execution. And, last but not least, formal methods are developed to offer a new quality of assurance in our systems of tomorrow. Demonstrating our results in a joint prototype - the HAEC Box - our goal is to become a pace setter for industry and academia on the design of future energy efficient-computing systems.<br /> |Kontaktperson=Gerhard Fettweis<br /> |URL=http://tu-dresden.de/sfb912<br /> |Start=2011/07/01<br /> |Ende=2019/06/30<br /> |Finanziert von=DFG<br /> |Projektstatus=aktiv<br /> |Logo=HAEC Logo.png<br /> |Person=Franz Baader, Markus Krötzsch, Anni-Yasmin Turhan, Alexander Krause, Veronika Thost, Long Cheng, <br /> |Forschungsgruppe=Automatentheorie, Knowledge Systems<br /> }}<br /> {{Forschungsgebiet Auswahl<br /> |Forschungsgebiet=Beschreibungslogiken<br /> }}<br /> {{Forschungsgebiet Auswahl<br /> |Forschungsgebiet=Semantische Technologien<br /> }}<br /> {{Forschungsgebiet Auswahl<br /> |Forschungsgebiet=Wissensrepräsentation und logisches Schließen<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings3054/en&diff=17038 2015-09-17T20:52:19Z

<p>Long Cheng: Page created automatically by parser function on page Inproceedings3054</p> <hr /> <div>#REDIRECT [[Inproceedings3054]]</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings3054&diff=17037 2015-09-17T20:52:18Z

<p>Long Cheng: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Long |ErsterAutorNachname=Cheng |FurtherAuthors=Spyros Kotoulas; Tomas E Ward; Georgios Theodoropoulos }} {{Inp…“</p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas; Tomas E Ward; Georgios Theodoropoulos<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=High Throughput Indexing for Large-scale Semantic Web Data<br /> |To appear=0<br /> |Year=2015<br /> |Month=April<br /> |Booktitle=Proc. 30th ACM/SIGAPP Symposium On Applied Computing (SAC'15)<br /> |Pages=416-422<br /> |Publisher=ACM<br /> }}<br /> {{Publikation Details<br /> |Abstract=Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Currently, several such systems have been proposed, however, their indexing methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose a high throughout index to enable rapid analysis of large datasets. We adopt a hybrid structure to combine the loading speed of similar-size based methods with the execution speed of graph-based approaches, using dynamic data repartitioning over query workloads. We introduce the design and detailed implementation of our method. Experimental results show that the proposed index can indeed vastly improve loading speeds while remaining competitive in terms of performance. Therefore, the method could be considered as a good choice for RDF analysis in large-scale distributed scenarios. <br /> |Download=2015-High-Throughput.pdf<br /> |Link=http://dl.acm.org/citation.cfm?doid=2695664.2695920<br /> |DOI Name=10.1145/2695664.2695920<br /> |Projekt=DIAMOND<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Datei:2015-High-Throughput.pdf&diff=17036 2015-09-17T20:43:37Z

<p>Long Cheng: </p> <hr /> <div></div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings4055&diff=17035 2015-09-17T20:38:33Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Design and Evaluation of Parallel Hashing over Large-scale Data<br /> |To appear=0<br /> |Year=2014<br /> |Month=Dezember<br /> |Booktitle=Proc. 21st IEEE International Conference on High Performance Computing (HiPC'14)<br /> |Pages=1-10<br /> |Publisher=IEEE<br /> }}<br /> {{Publikation Details<br /> |Abstract=High-performance analytical data processing systems often run on servers with large amounts of memory. A common data structure used in such environment is the hash tables. This paper focuses on investigating efficient parallel hash algorithms for processing large-scale data. Currently, hash tables on distributed architectures are accessed one key at a time by local or remote threads while shared-memory approaches focus on accessing a single table with multiple threads. A relatively straightforward “bulk-operation” approach seems to have been neglected by researchers. In this work, using such a method, we propose a high-level parallel hashing framework, Structured Parallel Hashing, targeting efficiently processing massive data on distributed memory.<br /> <br /> <br /> We present a theoretical analysis of the proposed method and describe the design of our hashing implementations. The evaluation reveals a very interesting result - the proposed straightforward method can vastly outperform distributed hashing methods and can even offer performance comparable with approaches based on shared memory supercomputers which use specialized hardware predicates. Moreover, we characterize the performance of our hash implementations through extensive experiments, thereby allowing system developers to make a more informed choice for their high-performance applications.<br /> |Download=2014-Design-Evaluation.pdf<br /> |Link=http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7116909<br /> |DOI Name=10.1109/HiPC.2014.7116909<br /> |Projekt=DIAMOND<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings4052&diff=17034 2015-09-17T20:36:49Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Spyros Kotoulas; Tomas E. Ward; Georgios Theodoropoulos<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Robust and Skew-resistant Parallel Joins in Shared-nothing Systems<br /> |To appear=0<br /> |Year=2014<br /> |Month=November<br /> |Booktitle=Proc. 23rd ACM International Conference on Information and Knowledge Management (CIKM'14)<br /> |Pages=1399-1408<br /> |Publisher=ACM<br /> }}<br /> {{Publikation Details<br /> |Abstract=The performance of joins in parallel database management systems is critical for data intensive operations such as querying. Since data skew is common in many applications, poorly engineered join operations result in load imbalance and performance bottlenecks. State-of-the-art methods designed to handle this problem offer significant improvements over naive implementations. However, performance could be further improved by removing the dependency on global skew knowledge and broadcasting. In this paper, we propose PRPQ (partial redistribution &amp; partial query), an efficient and robust join algorithm for processing large-scale joins over distributed systems. We present the detailed implementation and a quantitative evaluation of our method. The experimental results demonstrate that the proposed PRPQ algorithm is indeed robust and scalable under a wide range of skew conditions. Specially, compared to the state-of-art PRPD method, we achieve 16% - 167% performance improvement and 24% - 54% less network communication under different join workloads.<br /> |Download=2014-Robust-Skew.pdf<br /> |Link=http://dl.acm.org/citation.cfm?doid=2661829.2661888<br /> |DOI Name=10.1145/2661829.2661888<br /> |Projekt=DIAMOND<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings4053&diff=17033 2015-09-17T20:34:14Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Ilias<br /> |ErsterAutorNachname=Tachmazidis<br /> |FurtherAuthors=Long Cheng; Spyros Kotoulas; Grigoris Antoniou; Tomas E Ward<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Massively Parallel Reasoning under the Well-Founded Semantics using X10<br /> |To appear=0<br /> |Year=2014<br /> |Month=November<br /> |Booktitle=Proc. 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'14)<br /> |Pages=162-169<br /> |Publisher=IEEE<br /> }}<br /> {{Publikation Details<br /> |Abstract=Academia and industry are investigating novel approaches for processing vast amounts of data coming from enterprises, the Web, social media and sensor readings in an area that has come to be known as Big Data. Logic programming has traditionally focused on complex knowledge structures/programs. The question arises whether and how it can be applied in the context of Big Data. In this paper, we study how the well-founded semantics can be computed over huge amounts of data using mass parallelization. Specifically, we propose and evaluate a parallel approach based on the X10 programming language. Our experiments demonstrate that our<br /> approach has the ability to process up to 1 billion facts within minutes.<br /> |Download=2014-Massively- Parallel.pdf<br /> |Projekt=DIAMOND<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings4054&diff=17032 2015-09-17T20:32:16Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Long<br /> |ErsterAutorNachname=Cheng<br /> |FurtherAuthors=Yue Ma<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Investigating Distributed Approaches to Efficiently Extract Textual Evidences for Biomedical Ontologies<br /> |To appear=0<br /> |Year=2014<br /> |Month=November<br /> |Booktitle=Proc. 14th IEEE International Conference on BioInformatics and BioEngineering (BIBE'14)<br /> |Pages=220-225<br /> |Publisher=IEEE<br /> }}<br /> {{Publikation Details<br /> |Abstract=Heterogeneous data resources in biomedicine become available both in structured and unstructured formats, such as scientific publications, healthcare guidelines, controlled vocabularies, and formal ontologies. Bridging the gaps among these heterogeneous data is useful to discovery implicit knowledge. To make this happen, efficient computational approaches are a necessity for applications in such a knowledge- and data- intensive domain. In this paper, we first define a particular task, relation alignment, which is to identify textual evidences for biomedical ontologies. Then, we investigate two parallel approaches for this task over distributed systems and present the details of their implementations. Moreover, we characterize the performance of our methods through extensive experiments, thereby allowing researchers to make a more informed choice in the presence of large-scale biomedical data.<br /> |Download=2014-Inves-Distributed.pdf<br /> |Projekt=DIAMOND<br /> |Forschungsgruppe=Automatentheorie, Knowledge Systems<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=16932 2015-09-02T13:53:36Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 43510<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=APB 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently working as a Post-Doctoral Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at TU Dresden. My research interests mainly include:<br /> <br /> * Distributed computing<br /> * Large-scale data processing<br /> * Data management<br /> * Semantic web.<br /> |Info EN=I am currently working as a Post-Doctoral Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at TU Dresden. My research interests mainly include:<br /> <br /> * Distributed computing<br /> * Large-scale data processing<br /> * Data management<br /> * Semantic web.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Alternative URI=http://lat.inf.tu-dresden.de/~lcheng/<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=16704 2015-07-27T17:00:38Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 38043<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=APB 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently working as a Post-Doctoral Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at TU Dresden. My research interests mainly include:<br /> <br /> * Distributed computing<br /> * Large-scale data processing<br /> * Data management<br /> * Semantic web. <br /> |Info EN=I am currently working as a Post-Doctoral Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at TU Dresden. My research interests mainly include:<br /> <br /> * Distributed computing<br /> * Large-scale data processing<br /> * Data management<br /> * Semantic web. <br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Alternative URI=http://lat.inf.tu-dresden.de/~lcheng/<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=9734 2015-02-19T20:02:51Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 38043<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=APB 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014).<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and an Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include Distributed computing, Large-scale data processing, Data management and Semantic web.<br /> |Info EN=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014).<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and an Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include Distributed computing, Large-scale data processing, Data management and Semantic web.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Alternative URI=http://lat.inf.tu-dresden.de/~lcheng/<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=9728 2015-02-18T20:51:27Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 38043<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=APB 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014).<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include Distributed computing, Large-scale data processing, Data management and Semantic web.<br /> |Info EN=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014).<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include Distributed computing, Large-scale data processing, Data management and Semantic web.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Alternative URI=http://lat.inf.tu-dresden.de/~lcheng/<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=9478 2014-12-12T10:13:43Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 38043<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=APB 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014).<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing, Distributed data management systems and Semantic web.<br /> |Info EN=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014).<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing, Distributed data management systems and Semantic web.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=9477 2014-12-12T10:13:02Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 38043<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=APB 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014) and advised by [http://www.eeng.nuim.ie/~tward/ Dr. Tomas Ward], [http://www.gtheodoropoulos.com/ Prof. Georgios Theodoropoulos] and [http://researcher.watson.ibm.com/researcher/view.php?person=ie-Spyros.Kotoulas Dr. Spyros Kotoulas].<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing (especially for RDF data, Wikidata and Biomedical data), Distributed data management systems and Semantic web.<br /> |Info EN=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014) and advised by [http://www.eeng.nuim.ie/~tward/ Dr. Tomas Ward], [http://www.gtheodoropoulos.com/ Prof. Georgios Theodoropoulos] and [http://researcher.watson.ibm.com/researcher/view.php?person=ie-Spyros.Kotoulas Dr. Spyros Kotoulas].<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing (especially for RDF data, Wikidata and Biomedical data), Distributed data management systems and Semantic web.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=9114 2014-11-17T09:02:37Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 38043<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=INF 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014) and advised by [http://www.eeng.nuim.ie/~tward/ Dr. Tomas Ward], [http://www.gtheodoropoulos.com/ Prof. Georgios Theodoropoulos] and [http://researcher.watson.ibm.com/researcher/view.php?person=ie-Spyros.Kotoulas Dr. Spyros Kotoulas].<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing (especially for RDF data, Wikidata and Biomedical data), Distributed data management systems and Semantic web.<br /> |Info EN=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014) and advised by [http://www.eeng.nuim.ie/~tward/ Dr. Tomas Ward], [http://www.gtheodoropoulos.com/ Prof. Georgios Theodoropoulos] and [http://researcher.watson.ibm.com/researcher/view.php?person=ie-Spyros.Kotoulas Dr. Spyros Kotoulas].<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing (especially for RDF data, Wikidata and Biomedical data), Distributed data management systems and Semantic web.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Alternative URI=http://lat.inf.tu-dresden.de/~lcheng/<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Long_Cheng&diff=8935 2014-11-07T12:26:56Z

<p>Long Cheng: </p> <hr /> <div>{{Mitarbeiter<br /> |Vorname=Long<br /> |Nachname=Cheng<br /> |Akademischer Titel=Dr.<br /> |Forschungsgruppe=Knowledge Systems<br /> |Stellung=Wissenschaftlicher Mitarbeiter<br /> |Ehemaliger=0<br /> |Telefon=+49 351 463 38043<br /> |Fax=+49 351 463 37959<br /> |Email=long.cheng@tu-dresden.de<br /> |Raum=INF 3034<br /> |Bild=Long Cheng.jpg<br /> |Info=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014) and advised by [http://www.eeng.nuim.ie/~tward/ Dr. Tomas Ward], [http://www.gtheodoropoulos.com/ Prof. Georgios Theodoropoulos] and [http://researcher.watson.ibm.com/researcher/view.php?person=ie-Spyros.Kotoulas Dr. Spyros Kotoulas].<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing (especially for RDF data, Wikidata and Biomedical data), Distributed data management systems, Semantic web and Kownledge representation and reasoning.<br /> |Info EN=I am currently a Postdoc Researcher in the [[Forschungsgruppe::Knowledge Systems]] Group led by Dr. [[Miterarbeiter:: Markus Krötzsch]] at the Technische Universität Dresden. Before this, I was a PhD student at the National University of Ireland Maynooth (2011-2014) and advised by [http://www.eeng.nuim.ie/~tward/ Dr. Tomas Ward], [http://www.gtheodoropoulos.com/ Prof. Georgios Theodoropoulos] and [http://researcher.watson.ibm.com/researcher/view.php?person=ie-Spyros.Kotoulas Dr. Spyros Kotoulas].<br /> <br /> I got my B.Eng degree from Harbin Insititute of Technology, China (2007) and received the M.Sc degree from Universität Duisburg-Essen, Germany (2010). Additionally, I worked as an Engineer in Huawei Technologies Germany (Düsseldorf) in 2011 and a Research Assistant in IBM Research Ireland (Dublin) during 2011-2014.<br /> <br /> I joined in TU Dresden in June 2014. My research interests include High performance computing, Large-scale data processing (especially for RDF data, Wikidata and Biomedical data), Distributed data management systems, Semantic web and Kownledge representation and reasoning.<br /> |Google Scholar=http://scholar.google.de/citations?user=aI-bwLgAAAAJ&amp;hl=en<br /> |Alternative URI=http://lat.inf.tu-dresden.de/~lcheng/<br /> |Publikationen anzeigen=1<br /> |Abschlussarbeiten anzeigen=0<br /> }}</div>

Long Cheng https://iccl.inf.tu-dresden.de/w/index.php?title=Inproceedings4053&diff=8506 2014-11-02T16:42:49Z

<p>Long Cheng: </p> <hr /> <div>{{Publikation Erster Autor<br /> |ErsterAutorVorname=Ilias<br /> |ErsterAutorNachname=Tachmazidis<br /> |FurtherAuthors=Long Cheng; Spyros Kotoulas; Grigoris Antoniou; Tomas E Ward<br /> }}<br /> {{Inproceedings<br /> |Referiert=1<br /> |Title=Massively Parallel Reasoning under the Well-Founded Semantics using X10<br /> |To appear=0<br /> |Year=2014<br /> |Month=November<br /> |Booktitle=Proc. 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'14)<br /> |Publisher=IEEE<br /> }}<br /> {{Publikation Details<br /> |Abstract=Academia and industry are investigating novel approaches for processing vast amounts of data coming from enterprises, the Web, social media and sensor readings in an area that has come to be known as Big Data. Logic programming has traditionally focused on complex knowledge structures/programs. The question arises whether and how it can be applied in the context of Big Data. In this paper, we study how the well-founded semantics can be computed over huge amounts of data using mass parallelization. Specifically, we propose and evaluate a parallel approach based on the X10 programming language. Our experiments demonstrate that our<br /> approach has the ability to process up to 1 billion facts within minutes.<br /> |Download=2014-Massively- Parallel.pdf<br /> |Forschungsgruppe=Knowledge Systems<br /> }}</div>

Long Cheng