• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.294-304
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• 2011

Enhancing continuous queries over data streams with temporal functions and predicates enriches the expressive power of those queries. While traditional continuous queries retrieve only the values of attributes, temporal continuous queries retrieve the valid time intervals of those values as well. Correctly evaluating such queries requires the coalescing of adjacent timestamps for value-equivalent tuples prior to evaluating temporal functions and predicates. For many stream applications, the available computing resources may be too limited to produce exact query results. These limitations are commonly addressed through load shedding and produce approximated query results. There have been many load shedding mechanisms proposed so far, but for temporal continuous queries, the presence of coalescing makes theses existing methods unsuitable. In this paper, we propose a new accuracy metric and load shedding algorithm that are suitable for temporal query processing when memory is insufficient. The accuracy metric uses a combination of the Jaccard coefficient to measure the accuracy of attribute values and $\mathcal{PQI}$ interval orders to measure the accuracy of the valid time intervals in the approximate query result. The algorithm employs a greedy strategy combining two objectives reflecting the two accuracy metrics (i.e., value and interval). In the performance study, the proposed greedy algorithm outperforms a conventional random load shedding algorithm by up to an order of magnitude in its achieved accuracy.

• Journal of KIISE:Databases
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• v.29 no.6
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• pp.429-440
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• 2002

Temporal databases manage time-evolving data. They provide built-in supports for efficient recording and querying of temporal data. The temporal aggregate in temporal databases is an extension of the conventional aggregate to include time concept on the domain and range of aggregation. This paper focuses on multidimensional temporal aggregation. In a multidimensional temporal aggregate, we use one or more general attributes as well as a time attribute on the range of aggregation, thus it is a useful operation for historical data warehouse, Call Data Records(CDR), etc. In this paper, we propose a structure for multidimensional temporal aggregation, called PTA-tree, and an aggregate processing method based on the PTA-tree. Through analyses and performance experiments, we also compare the PTA-tree with the simple extension of SB-tree that was proposed for temporal aggregation.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1418-1427
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• 1999

시간지원 데이타베이스 시스템은 자료의 과거 및 현재, 그리고 미래의 상태까지 관리함으로써, 사용자에게 시간에 따라 변화하는 자료에 대한 저장 및 질의 수단을 제공한다. 시간지원 데이타베이스는 경향 분석, 버전 관리, 의료 기록 관리 및 비디오 데이타 관리 등과 같이 자료의 시간적 특성이 중요시 되는 모든 분야에 폭 넓게 응용될 수 있다. 시간지원 데이타베이스에서의 집계는 시간 애트리뷰트를 고려하지 않은 기존의 집계와는 큰 차이가 있으며, 기존의 집계 처리 기법을 이용하여 효과적으로 처리될 수 없다. 본 논문에서는 시간지원 집계를 효율적으로 처리하기 위한 새로운 자료 구조인 PA-트리를 제안하고, 이를 이용한 시간지원 집계 처리 기법을 제안한다. 또한 본 논문에서는 제안된 PA-트리를 이용한 기법과 기존의 집계 트리를 이용한 기법의 성능을 최악 경우 분석과 실험을 통해 비교한다.Abstract Temporal databases manage time-evolving data. They provide built-in supports for efficient recording and querying of temporal data. Many application area such as trend analysis, version management, and medical record management have temporal aspects, and temporal databases can handle these temporal aspects efficiently. The aggregate in temporal databases, that is, temporal aggregate is an extension of conventional aggregate on the domain and range of aggregation to include time concept. The basic techniques behind computing aggregates in conventional databases are not efficient when applied to temporal databases. In this paper, we propose a new tree structure for temporal aggregation, called PA-tree, and aggregate processing method based on the PA-tree. We compare the PA-tree with the existing aggregation tree which has been proposed for temporal aggregate.

• Science of Emotion and Sensibility
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• v.22 no.4
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• pp.65-74
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• 2019

Extant research has proposed that the Object-Spatial-Verbal cognitive style can elucidate individual differences in the preference for modality-specific information. However, no studies have yet ascertained whether this type of information processing evinces structural correlations in the brain. Therefore, the current study used voxel-based morphometry (VBM) analyses to investigate individual differences in gray matter volumes based on the Object-Spatial-Verbal cognitive style. For this purpose, ninety healthy young adults were recruited to participate in the study. They were administered the Korean version of the Object-Spatial-Verbal cognitive style questionnaire, and their anatomical brain images were scanned. The VBM results demonstrated that the participants' verbal scores were positively correlated with regional gray matter volumes (rGMVs) in the right superior temporal sulcus/superior temporal gyrus, the bilateral parahippocampal gyrus/fusiform gyrus, and the left inferior temporal gyrus. In addition, the rGMVs in these regions were negatively correlated with the relative spatial preference scores obtained by individual participants. The findings of the investigation provide anatomical evidence that the verbal cognitive style could be decidedly relevant to higher-level language processing, but not to basic language processing.

• Spatial Information Research
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• v.22 no.5
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• pp.9-18
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• 2014

According to the recent development of the ubiquitous computing environment, the use of spatio-temporal data from sensors with GPS is increasing, and studies on the Semantic Sensor Web using spatio-temporal data for providing different kinds of services are being actively conducted. Especially, the W3C developed the SSNO(Semantic Sensor Network Ontology) which uses sensor-related standards such as the SWE(Sensor Web Enablement) of OGC and defines classes and properties for expressing sensor data. Since these studies are available for the query processing about non-spatio-temporal sensor data, it is hard to apply them to spatio-temporal sensor data processing which uses spatio-temporal data types and operators. Therefore, in this paper, we developed the SWE based on SSNO which supports the spatio-temporal sensor data types and operators expanding spatial data types and operators in "OpenGIS Simple Feature Specification for SQL" by OGC. The system receives SensorML(Sensor Model Language) and O&M (Observations and Measurements) Schema and converts the data into SSNO. It also performs the efficient query processing which supports spatio-temporal operators and reasoning rules. In addition, we have proved that this system can be utilized for the web service by applying it to a virtual scenario.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.535-544
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• 2018

As the spread of mobile devices equipped with various sensors and high-quality wireless network communications functionsexpands, the amount of spatio-temporal data generated from mobile devices in various service fields is rapidly increasing. In conventional research into processing a large amount of real-time spatio-temporal streams, it is very difficult to apply a Hadoop-based spatial big data system, designed to be a batch processing platform, to a real-time service for spatio-temporal data streams. This paper extends the MapReduce online framework to support real-time query processing for continuous-input, spatio-temporal data streams, and proposes a load management method to distribute overloads for efficient query processing. The proposed scheme shows a dynamic load balancing method for the nodes based on the inflow rate and the load factor of the input data based on the space partition. Experiments show that it is possible to support efficient query processing by distributing the spatial data stream in the corresponding area to the shared resources when load management in a specific area is required.

• Wind and Structures
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• v.21 no.4
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• pp.425-442
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• 2015

This paper discusses the processing of the wind loads measured in wind tunnel tests by means of multi-channel pressure scanners, in order to compute the response of 3D structures to atmospheric turbulence in the time domain. Data compression and the resulting computational savings are still a challenge in industrial contexts due to the multiple trial configurations during the construction stages. The advantage and robustness of the bi-orthogonal decomposition (BOD) is demonstrated through an example, a sail glass of the Fondation Louis Vuitton, independently from any tentative physical interpretation of the spatio-temporal decomposition terms. We show however that the energy criterion for the BOD has to be more rigorous than commonly admitted. We find a level of 99.95 % to be necessary in order to recover the extreme values of the loads. Moreover, frequency limitations of wind tunnel experiments are sometimes encountered in passing from the scaled model to the full scale structure. These can be alleviated using a spectral extension of the temporal function terms of the BOD.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.399-409
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• 2019

Three-dimensional (3D) human pose reconstruction from single-view image is a difficult and challenging topic. Existing approaches mostly process frame-by-frame independently while inter-frames are highly correlated in a sequence. In contrast, we introduce a novel spatial-temporal 3D human pose reconstruction framework that leverages both intra and inter-frame relationships in consecutive 2D pose sequences. Orthogonal matching pursuit (OMP) algorithm, pre-trained pose-angle limits and temporal models have been implemented. Several quantitative comparisons between our proposed framework and recent works have been studied on CMU motion capture dataset and Vietnamese traditional dance sequences. Our framework outperforms others by 10% lower of Euclidean reconstruction error and more robust against Gaussian noise. Additionally, it is also important to mention that our reconstructed 3D pose sequences are more natural and smoother than others.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.844-855
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• 2005

This paper proposes a human action recognition scheme for Intelligent Web Browser. Based on the principle that a human action can be defined as a combination of multiple articulation movements, the inference of stochastic grammars is applied to recognize each action. Human actions in 3 dimensional (3D) world coordinate are measured, quantized and made into two sets of 4-chain-code for xy and zy projection planes, consequently they are appropriate for applying the stochastic grammar inference method. We confirm this method by experiments, that various physical actions can be classified correctly against a set of real world 3D temporal data. The result revealed a comparatively successful achievement of $93.8\%$ recognition rate through the experiments of 8 movements of human head and $84.9\%$ recognition rate of 60 movements of human upper body. We expect that this scheme can be used for human-machine interaction commands in a web browser.