References of "Lavangnananda, Kittichai"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailChaotic Traversal (CHAT): Very Large Graphs Traversal Using Chaotic Dynamics
Changaival, Boonyarit UL; Rosalie, Martin UL; Danoy, Grégoire UL et al

in International Journal of Bifurcation and Chaos (2017), 27(14), 1750215

Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first ... [more ▼]

Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first stepping stone into knowledge extraction from the graph which is now a popular topic. Classical solutions such as Breadth First Search (BFS) and Depth First Search (DFS) require huge amounts of memory for exploring very large graphs. In this research, we present a novel memoryless graph traversal algorithm, Chaotic Traversal (CHAT) which integrates chaotic dynamics to traverse large unknown graphs via the Lozi map and the Rössler system. To compare various dynamics effects on our algorithm, we present an original way to perform the exploration of a parameter space using a bifurcation diagram with respect to the topological structure of attractors. The resulting algorithm is an efficient and nonresource demanding algorithm, and is therefore very suitable for partial traversal of very large and/or unknown environment graphs. CHAT performance using Lozi map is proven superior than the, commonly known, Random Walk, in terms of number of nodes visited (coverage percentage) and computation time where the environment is unknown and memory usage is restricted. [less ▲]

Detailed reference viewed: 44 (2 UL)
Full Text
Peer Reviewed
See detailMeasuring data locality ratio in virtual MapReduce cluster using WorkflowSim
Wangsom, Peerasak; Lavangnananda, Kittichai; Bouvry, Pascal UL

in Proceedings of the 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2017 14th (2017, July)

The data locality is significant factor which has a direct impact on the performance of MapReduce framework. Several previous works have proposed alternative scheduling algorithms for improving the ... [more ▼]

The data locality is significant factor which has a direct impact on the performance of MapReduce framework. Several previous works have proposed alternative scheduling algorithms for improving the performance by increasing data locality. Nevertheless, their studies had focused the data locality on physical MapReduce cluster. As more and more deployment of MapReduce cluster have been on virtual environment, a more suitable evaluation of MapReduce cluster may be necessary. This study adopts a simulation based approach. Five scheduling algorithms were used for the simulation. WorkflowSim is extended by inclusion of three implemented modules to assess the new performance measure called `data locality ratio'. Comparison of their results reveals interesting findings. The proposed implementation can be used to assess `data locality ratio' and allows users prior to efficiently select and tune scheduler and system configurations suitable for an environment prior to its actual physical MapReduce deployment. [less ▲]

Detailed reference viewed: 54 (7 UL)
Full Text
Peer Reviewed
See detailMetaheuristic Based Clustering Algorithms for Biological Hypergraphs
Changaival, Boonyarit UL; Danoy, Grégoire UL; Ostaszewski, Marek UL et al

in Proceedings of META’2016, 6th International Conference on Metaheuristics and Nature Inspired computing (2016, October 27)

Hypergraphs are widely used for modeling and representing relationships between entities, one such field where their application is prolific is in bioinformatics. In the present era of big data, sizes and ... [more ▼]

Hypergraphs are widely used for modeling and representing relationships between entities, one such field where their application is prolific is in bioinformatics. In the present era of big data, sizes and complexity of these hypergraphs grow exponentially, it is impossible to process them manually or even visualize their interconnectivity superficially. A common approach to tackle their complexity is to cluster similar data nodes together in order to create a more comprehensible representation. This enables similarity discovery and hence, extract hidden knowledge within the hypergraphs. Several state-of-the-art algorithms have been proposed for partitioning and clustering of hypergraphs. Nevertheless, several issues remain unanswered, improvement to existing algorithms are possible, especially in scalability and clustering quality. This article presents a concise survey on hypergraph-clustering algorithms with the emphasis on knowledge-representation in systems biomedicine. It also suggests a novel approach to clustering quality by means of cluster-quality metrics which combines expert knowledge and measurable objective distances in existing biological ontology. [less ▲]

Detailed reference viewed: 87 (5 UL)
Full Text
Peer Reviewed
See detailToken Traversal Strategies of a Distributed Spanning Forest Algorithm in Mobile Ad hoc - Delay Tolerant Networks
Piyatumrong, Apivadee UL; Ruiz, Patricia UL; Bouvry, Pascal UL et al

in IAIT (2009)

This paper presents three distributed and decentralized strategies used for token traversal in spanning forest over Mobile Ad Hoc Delay Tolerant Networks. Such networks are characterized by behaviors like ... [more ▼]

This paper presents three distributed and decentralized strategies used for token traversal in spanning forest over Mobile Ad Hoc Delay Tolerant Networks. Such networks are characterized by behaviors like disappearance of mobile devices, connection disruptions, network partitioning, etc. Techniques based on tree topologies are well known for increasing the efficiency of network protocols and/or applications, such as Dynamicity Aware - Graph Relabeling System (DA-GRS). One of the main features of these tree based topologies is the existence of a token traversing in every tree. The use of tokens enables the creation and maintenance of spanning trees in dynamic environments. Subsequently, managing tree-based backbones relies heavily on the token behavior. An efficient and optimal token traversal can highly impact the design of the tree and its usage. In this article, we present a comparison of three distributed and decentralized techniques available for token management, which are Randomness, TABU and Depth First Search. [less ▲]

Detailed reference viewed: 64 (0 UL)