The Experts below are selected from a list of 24525 Experts worldwide ranked by ideXlab platform
Vesna Sesumcavic - One of the best experts on this subject based on the ideXlab platform.
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a space based Generic Pattern for self initiative load clustering agents
International Conference on Coordination Models and Languages, 2012Co-Authors: Eva Kuhn, Vesna Sesumcavic, Alexander Marek, Thomas Scheller, Michael Vogler, Stefan CrasAbstract:Load clustering is an important problem in distributed systems, which proper solution can lead to a significant performance improvement. It differs from load balancing as it considers a collection of loads, instead of normal data items, where a single load can be described as a task. Current approaches that treat load clustering mainly lack of provisioning a general framework and autonomy. They are neither agent-based nor configurable for many topologies. In this paper we propose a Generic framework for self-initiative load clustering agents (SILCA) that is based on autonomous agents and decentralized control. SILCA is a Generic architectural Pattern for load clustering. The SILCA framework is the corresponding implementation and thus supports exchangeable policies and allows for the plugging of different algorithms for load clustering. It is problem independent, so the best algorithm or combination of algorithms can be found for each specific problem. The Pattern has been implemented on two levels: In its basic version different algorithms can be plugged, and in the extended version different algorithms can be combined. The flexibility is proven by means of nine algorithms. Further contributions are the benchmarking of the algorithms, and the working out of their best combinations for different topologies.
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a space based Generic Pattern for self initiative load balancing agents
Lecture Notes in Computer Science, 2009Co-Authors: Eva Kuhn, Vesna SesumcavicAbstract:Load-Balancing is a significant problem in heterogeneous distributed systems. There exist many load balancing algorithms, however, most approaches are very problem specific oriented and a comparison is therefore complex. This paper proposes a Generic architectural Pattern for a load balancing framework that allows for the plugging of different load balancing algorithms, reaching from unintelligent to intelligent ones, to ease the selection of the best algorithm for a certain problem scenario. As in complex network environments there is no "one-fits-all solution", also the integration of several different algorithms shall be supported. The presented Pattern assumes autonomous agents and decentralized control. It can be composed towards arbitrary network topologies, foresees exchangeable policies for load-balancing, and uses a black-board based communication mechanism to achieve high software architecture agility. The Pattern has been implemented and first instantiations of it with three algorithms have been benchmarked.
Eva Kuhn - One of the best experts on this subject based on the ideXlab platform.
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a space based Generic Pattern for self initiative load clustering agents
International Conference on Coordination Models and Languages, 2012Co-Authors: Eva Kuhn, Vesna Sesumcavic, Alexander Marek, Thomas Scheller, Michael Vogler, Stefan CrasAbstract:Load clustering is an important problem in distributed systems, which proper solution can lead to a significant performance improvement. It differs from load balancing as it considers a collection of loads, instead of normal data items, where a single load can be described as a task. Current approaches that treat load clustering mainly lack of provisioning a general framework and autonomy. They are neither agent-based nor configurable for many topologies. In this paper we propose a Generic framework for self-initiative load clustering agents (SILCA) that is based on autonomous agents and decentralized control. SILCA is a Generic architectural Pattern for load clustering. The SILCA framework is the corresponding implementation and thus supports exchangeable policies and allows for the plugging of different algorithms for load clustering. It is problem independent, so the best algorithm or combination of algorithms can be found for each specific problem. The Pattern has been implemented on two levels: In its basic version different algorithms can be plugged, and in the extended version different algorithms can be combined. The flexibility is proven by means of nine algorithms. Further contributions are the benchmarking of the algorithms, and the working out of their best combinations for different topologies.
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a space based Generic Pattern for self initiative load balancing agents
Lecture Notes in Computer Science, 2009Co-Authors: Eva Kuhn, Vesna SesumcavicAbstract:Load-Balancing is a significant problem in heterogeneous distributed systems. There exist many load balancing algorithms, however, most approaches are very problem specific oriented and a comparison is therefore complex. This paper proposes a Generic architectural Pattern for a load balancing framework that allows for the plugging of different load balancing algorithms, reaching from unintelligent to intelligent ones, to ease the selection of the best algorithm for a certain problem scenario. As in complex network environments there is no "one-fits-all solution", also the integration of several different algorithms shall be supported. The presented Pattern assumes autonomous agents and decentralized control. It can be composed towards arbitrary network topologies, foresees exchangeable policies for load-balancing, and uses a black-board based communication mechanism to achieve high software architecture agility. The Pattern has been implemented and first instantiations of it with three algorithms have been benchmarked.
Mohammed J Zaki - One of the best experts on this subject based on the ideXlab platform.
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towards Generic Pattern mining
Pattern Recognition and Machine Intelligence, 2005Co-Authors: Mohammed J Zaki, Nagender Parimi, Benjarath Phoophakdee, Feng Gao, Mohammad Al Hasan, Joe Urban Vineet Chaoji, Saeed SalemAbstract:Frequent Pattern Mining (FPM) is a very powerful paradigm which encompasses an entire class of data mining tasks. The specific tasks encompassed by FPM include the mining of increasingly complex and informative Patterns, in complex structured and unstructured relational datasets, such as: Itemsets or co-occurrences [1] (transactional, unordered data), Sequences [2,8] (temporal or positional data, as in text mining, bioinformatics), Tree Patterns [9] (XML/semistructured data), and Graph Patterns [4,5,6] (complex relational data, bioinformatics). Figure [1] shows examples of these different types of Patterns; in a Generic sense a Pattern denotes links/relationships between several objects of interest. The objects are denoted as nodes, and the links as edges. Patterns can have multiple labels, denoting various attributes, on both the nodes and edges.
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towards Generic Pattern mining
International Conference on Formal Concept Analysis, 2005Co-Authors: Mohammed J Zaki, Nagender Parimi, Benjarath Phoophakdee, Joe Urban, Feng Gao, Vineet Chaoji, Mohammad Al Hasan, Saeed SalemAbstract:Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful Patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of Generic containers and algorithms for FPM, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. Our experiments show that DMTL is competitive with special purpose algorithms designed for a particular Pattern type, especially as database sizes increase.
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Generic Pattern mining via data mining template library
Lecture Notes in Computer Science, 2004Co-Authors: Mohammed J Zaki, Nilanjana De, Paolo Palmerini, Nagender Parimi, Jeevan Pathuri, Benjarath Phoophakdee, Joe UrbanAbstract:Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful Patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of Generic containers and algorithms for data mining, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. A detailed set of experiments show that DMTL is competitive with special purpose algorithms designed for a particular Pattern type, especially as database sizes increase.
Joe Urban - One of the best experts on this subject based on the ideXlab platform.
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towards Generic Pattern mining
International Conference on Formal Concept Analysis, 2005Co-Authors: Mohammed J Zaki, Nagender Parimi, Benjarath Phoophakdee, Joe Urban, Feng Gao, Vineet Chaoji, Mohammad Al Hasan, Saeed SalemAbstract:Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful Patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of Generic containers and algorithms for FPM, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. Our experiments show that DMTL is competitive with special purpose algorithms designed for a particular Pattern type, especially as database sizes increase.
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Generic Pattern mining via data mining template library
Lecture Notes in Computer Science, 2004Co-Authors: Mohammed J Zaki, Nilanjana De, Paolo Palmerini, Nagender Parimi, Jeevan Pathuri, Benjarath Phoophakdee, Joe UrbanAbstract:Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful Patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of Generic containers and algorithms for data mining, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. A detailed set of experiments show that DMTL is competitive with special purpose algorithms designed for a particular Pattern type, especially as database sizes increase.
Stefan Cras - One of the best experts on this subject based on the ideXlab platform.
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a space based Generic Pattern for self initiative load clustering agents
International Conference on Coordination Models and Languages, 2012Co-Authors: Eva Kuhn, Vesna Sesumcavic, Alexander Marek, Thomas Scheller, Michael Vogler, Stefan CrasAbstract:Load clustering is an important problem in distributed systems, which proper solution can lead to a significant performance improvement. It differs from load balancing as it considers a collection of loads, instead of normal data items, where a single load can be described as a task. Current approaches that treat load clustering mainly lack of provisioning a general framework and autonomy. They are neither agent-based nor configurable for many topologies. In this paper we propose a Generic framework for self-initiative load clustering agents (SILCA) that is based on autonomous agents and decentralized control. SILCA is a Generic architectural Pattern for load clustering. The SILCA framework is the corresponding implementation and thus supports exchangeable policies and allows for the plugging of different algorithms for load clustering. It is problem independent, so the best algorithm or combination of algorithms can be found for each specific problem. The Pattern has been implemented on two levels: In its basic version different algorithms can be plugged, and in the extended version different algorithms can be combined. The flexibility is proven by means of nine algorithms. Further contributions are the benchmarking of the algorithms, and the working out of their best combinations for different topologies.
