As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.
Park, Ji-Young;Suh, Ji-Hyun;Kim, Sung-Hee;Rhee, Seon-Min;Kim, Myoung-Hee
The KIPS Transactions:PartA
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v.15A
no.3
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pp.141-149
/
2008
In this research we present a novel method which combines and visualizes the design model and the FDM-based simulation result of solidification. Moreover we employ VR displays and visualize stereoscopic images to provide an effective analysis environment. First we reconstruct the solidification simulation result to a rectangular mesh model using a conventional simulation software. Then each point color of the reconstructed model represents a temperature value of its position. Next we map the two models by finding the nearest point of the reconstructed model for each point of the design model and then assign the point color of the design model as that of the reconstructed model. Before this mapping we apply mesh subdivision because the design model is composed of minimum number of points and that makes the point distribution of the design model not uniform compared with the reconstructed model. In this process the original shape is preserved in the manner that points are added to the mesh edge which length is longer than a predefined threshold value. The implemented system visualizes the solidification simulation data on the design model, which allows the user to understand the object geometry precisely. The immersive and realistic working environment constructed with use of VR display can support the user to discover the defect occurrence faster and more effectively.
Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).
For ten meteorological observatories running an automated synoptic observing system (ASOS), we classified the observation environments into five classes based on the World Meteorological Organization (WMO) classification guidelines. Obstacles (such as topography and buildings) and land-cover types were the main factors in evaluating the observation environments for the sunshine duration, air-temperature, and surface wind. We used the digital maps of topography, buildings, and land-cover types. The observation environment of the sunshine duration was most affected by the surrounding buildings when the solar altitude angle was low around the sunrise and sunset. The air-temperature observation environment was determined based on not only the solar altitude angle but the distance between the heat/water source and ASOS. There was no water source around the ASOSs considered in this study. Heat sources located near some ASOSs were not large enough to affect the observation environment. We evaluated the surface wind observation environment based on the roughness length around the ASOS and the distance between surrounding buildings and the ASOS. Most ASOSs lay at a higher altitude than the surroundings and the roughness lengths around the ASOSs were small enough to satisfy the condition for the best level.
Bolser, Dan;Dafas, Panos;Harrington, Richard;Schroeder, Michael;Park, Jong
Proceedings of the Korean Society for Bioinformatics Conference
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2003.10a
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pp.26-51
/
2003
Large scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in thePDB. PSIMAP incorporates both functional and evolutionary information into a single network. It makes it possible to age protein domains in terms of taxonomic diversity, interaction and function. One consequence of it is to predict the most important protein domain structure in evolution. We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: ${\bullet}$ Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. ${\bullet}$ Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. ${\bullet}$ Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. This led to the prediction of the oldest and most important protein domain in evolution of lift. ${\bullet}$ Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level.
Oh, Jang-Hoon;Kim, Hyug-Gi;Woo, Dong-Cheol;Rhee, Sun Jung;Lee, Soo Yeol;Jahng, Geon-Ho
Progress in Medical Physics
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v.29
no.1
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pp.29-41
/
2018
The objective of this study was to evaluate the chemical exchange saturation transfer (CEST) effect of amino acids and neurotransmitters, which exist in the human brain, depending on the concentration, pH, and amplitude of the saturation radiofrequency field. Phantoms were developed with asparagine (Asn), ${\gamma}-aminobutyric$ acid (GABA), glutamate (Glu), glycine (Gly), and myoinositol (MI). Each chemical had three different concentrations of 10, 30, and 50 mM and three different pH values of 5.6, 6.2, and 7.4. Full Z-spectrum CEST images for each phantom were acquired with a continuous-wave radiofrequency (RF) saturation pulse with two different $B_1$ amplitudes of $2{\mu}T$ and $4{\mu}T$ using an animal 9.4T MRI system. A voxel-based CEST asymmetry was mapped to evaluate exchangeable protons based on amide (-NH), amine ($-NH_2$), and hydroxyl (-OH) groups for the five target molecules. For all target molecules, the CEST effect was increased with increasing concentration and B1 amplitude; however, the CEST effect with varying pH displayed a different trend depending on the characteristics of the molecule. On CEST asymmetric maps, Glu and MI were well visualized around 3.0 and 0.9 ppm, respectively, and were well separated macroscopically at a pH of 7.4. The exchange rates of Asn, Glu, BABA, and Gly usually decreased with increasing pH. The CEST effect was dependent on the concentration, acidity of the target molecules, and B1 amplitude of the saturation RF pulse. The CEST effect for Asn can be observed in a 9.4T MRI system. The results of this study are based on applying the CEST technique in patients with neurodegenerative diseases when proteins in the brain are increased with disease progression.
Journal of the Korean association of regional geographers
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v.18
no.4
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pp.464-472
/
2012
To assess the space-time clustering of FMD(Foot-and-Mouth Disease) epidemic occurred in Korea between November 2010 to April 2011, geographical information system (GIS)-based spatial analysis technique was used. Farm address and geographic data obtained from a commercial portal site were integrated into GIS software, which we used to map out the color-shading geographic features of the outbreaks through a process called thematic mapping, and to produce a visual representation of the relationship between epidemic course and time throughout the country. FMD cases reported in northern area of Gyounggi province were clustered in space and time within small geographic areas due to the environmental characteristics which livestock population density is high enough to ease transmit FMD virus to the neighboring farm, whereas FMD cases were clustered in space but not in time for southern and eastern area of Gyounggi province. When analyzing the data for 7-day interval, the mean radius of the spatial-time clustering was 25km with minimum 5.4km and maximum 74km. In addition, the radius of clustering was relatively small in the early stage of FMD epidemic, but the size was geographically expanded over the epidemic course. Prior to implementing control measures during the outbreak period, assessment of geographic units potentially affected and identification of risky areas which are subsequently be targeted for specific intervention measures is recommended.
Korean Journal of Agricultural and Forest Meteorology
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v.17
no.2
/
pp.102-107
/
2015
This study was conducted to classify agro-climatic zones in Northeast district of China. For agro-climatic zoning, monthly mean temperature and precipitation data from Global Modeling and Assimilation Office (GMAO) of National Aeronautics and Space Administration (NASA, USA) between 1979 and 2010 (http://disc.sci.gsfc.nasa.gov/) were collected. Altitude and vegetation fraction of East Asia from Weather Research and Forecasting (WRF) were also used to classify them. The criteria of agro-climatic classification were altitude (200 m, between 200-800 m, 800 m), vegetation fraction (60%), annual mean temperature ($0^{\circ}C$), temperature in the hottest month ($22^{\circ}C$), and annual precipitation (700 mm). In Northeast district of China, mean annual temperature, annual precipitation, and solar radiation were $3.4^{\circ}C$, 613.2 mm, and $4,414.2MJ/m^2$ between 2009 and 2013, respectively. Twenty-two agro-climatic zones identified in Northeast district of China by metrics classification method, from which the map of agro-climatic zones for Northeast district of China was derived. The results could be useful as information for estimating agro-meteorological characteristics and predicting crop development and crop yield of Northeast district of China as well as those of North Korea.
Korean Journal of Agricultural and Forest Meteorology
/
v.7
no.1
/
pp.115-123
/
2005
Quantitative understanding of spatial characteristics of the study site is a prerequisite to investigate water and carbon cycles in agricultural and forest ecosystems, particularly with complex, heterogeneous landscapes. The spatial characteristics of variables related with topography, vegetation and soil in Gwangneung forest watershed are quantified in this study. To characterize topography, information on elevation, slope and aspect extracted from DEM is analyzed. For vegetation and soil, a land-cover map classified from LANDSAT TM images is used. Four satellite images are selected to represent different seasons (30 June 1999, 4 September 2000, 23 September 2001 and 14 February 2002). As a flux index for CO₂ and water vapor, normalized difference vegetation index (NDVI) is calculated from satellite images for three different grid sizes: MODIS grid (7km x 7km), intensive observation grid (3km x 3km), and unit grid (1km x 1km). Then, these data are analyzed to quantify the spatial scale of heterogeneity based on semivariogram analysis. As expected, the scale of heterogeneity decreases as the grid size decreases and are sensitive to seasonal changes in vegetation. For the two unit grids where the two 40 m flux towers are located, the spatial scale of heterogeneity ranges from 200 to 1,000m, which correspond well to the climatology of the computed tower flux footprint.
Kim, Seong Hoon;Oh, Hyunje;Jung, Jinhong;Kim, Weonjae;Yoon, Young H.
Journal of the Korea Academia-Industrial cooperation Society
/
v.13
no.12
/
pp.6143-6150
/
2012
The current society, namely information society is now moving to a specific topic which is SMART. In this sense, recently a variety of social areas including communications and SOC domains are moving fast to this topic. In Korea, The electric power area has been doing a pioneering job relatively successfully and the water supply area is just now taking the first step. The purpose of this research is to develop a technical Framework for Smart Water Grid Service. Related researches has been studied and the 4 constituting technical element areas were defined first. For each of the four areas, a framework modeling was fulfilled and as a result, a TRM(Technical Road Map) was suggested for each of the area. Finally, an Enterprise TRM covering all of the 4 areas was described. Furthermore, the currently suggested Framework model was compared to a related model and it was found that the integration of the models is desirable to wholly cover from Macro to Micro level applications and services. It is expected that the current approach contribute, more or less, to the smart implementation in the areas of water management.
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