• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.6
• /
• pp.2713-2719
• /
• 2012

According to increasing of various location-based services, distributed data stream processing techniques have been widely studied to provide high scalability and availability. In previous researches, in order to balance the load of distributed nodes, the geographic characteristics of spatial data stream are not considered. For this reason, distributed operations for adjacent spatial regions increases the overall system load. We propose a operation allocation scheme considering the characteristics of spatial operations to effectively processing spatial data stream in distributed computing environments. The proposed method presents the efficient share maximizing approach that preferentially distributes spatial operations sharing the common query regions to the same node in order to separate the adjacent spatial operations on overlapped regions.

• Journal of information and communication convergence engineering
• /
• v.1 no.1
• /
• pp.12-22
• /
• 2003

Real-time(RT) object-oriented(OO) distributed computing is a form of RT distributed computing realized with a distributed computer system structured in the form of an object network. Several approached proposed in recent years for extending the conventional object structuring scheme to suit RT applications, are briefly reviewed. Then the approach named the TMO (Time-triggered Message-triggered Object) structuring scheme was formulated with the goal of instigating a quantum productivity jump in the design of distributed time triggered simulation. The TMO scheme is intended to facilitate the pursuit of a new paradigm in designing distributed time triggered simulation which is to realize real-time computing with a common and general design style that does not alienate the main-stream computing industry and yet to allow system engineers to confidently produce certifiable distributed time triggered simulation for safety-critical applications. The TMO structuring scheme is a syntactically simple but semantically powerful extension of the conventional object structuring approached and as such, its support tools can be based on various well-established OO programming languages such as C++ and on ubiquitous commercial RT operating system kernels. The Scheme enables a great reduction of the designers efforts in guaranteeing timely service capabilities of application systems

• ETRI Journal
• /
• v.39 no.2
• /
• pp.202-212
• /
• 2017

With the advent of the Internet-of-Things paradigm, the amount of data production has grown exponentially and the user demand for responsive consumption of data has increased significantly. Herein, we present DART, a fast and lightweight stream processing framework for the IoT environment. Because the DART framework targets a geospatially distributed environment of heterogeneous devices, the framework provides (1) an end-user tool for device registration and application authoring, (2) automatic worker node monitoring and task allocations, and (3) runtime management of user applications with fault tolerance. To maximize performance, the DART framework adopts an actor model in which applications are segmented into microtasks and assigned to an actor following a single responsibility. To prove the feasibility of the proposed framework, we implemented the DART system. We also conducted experiments to show that the system can significantly reduce computing burdens and alleviate network load by utilizing the idle resources of intermediate edge devices.

• The Journal of Society for e-Business Studies
• /
• v.14 no.4
• /
• pp.75-94
• /
• 2009

Since XML emerged as a standard for data exchange on the web, it has been established as a core component in e-Commerce and efficient query processing over XML data in ubiquitous computing environment has been also receiving much attention. Recently, the techniques were proposed whereby an XML document is fragmented into XML fragments to be streamed and the mobile clients receive the stream while processing queries over it. In processing queries over an XML fragment stream, the average access time significantly depends on the order of fragments in the stream. As such, for query performance, an efficient organization of XML fragment stream is required as well as the indexing for energy-efficient query processing due to the reduction of tuning time. In this paper, a technique of XML fragment stream organization based on query frequencies, fragment size, fragment access frequencies, and an active XML-based indexing scheme are proposed. Through implementation and performance experiments, our techniques were shown to be efficient compared with the conventional XML fragment stream organizations.

• Journal of Korean Society for Geospatial Information Science
• /
• v.20 no.1
• /
• pp.21-31
• /
• 2012

Recently, according to the development of ubiquitous computing technology, the efficient management of locations of moving objects is increasing rapidly in various fields. However, MODBMS and DSMS can not support the efficient real-time management of spatio-temporal stream data of moving objects. Therefore, this paper designed and implemented a spatio-temporal DSMS which can support the efficient real-time management of spatio-temporal stream data of moving objects. Especially, to develop the spatio-temporal DSMS, we extended STREAM of Stanford University and used GEOS that supports spatial data types and spatial operators of OGC. Finally, this paper proved the efficiency of the spatio-temporal DSMS by applying it to the real-time monitoring field which requires the real-time management of spatio-temporal stream data of moving objects.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.1
• /
• pp.321-344
• /
• 2017

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.40 no.6
• /
• pp.22-31
• /
• 2003

Traditional concurrency control strategies for database system are inappropriate for mobile computing environments in which communication disconnection could occur frequently. The time available to access transactions may be extremely limited in mobile computing. In order to ensure that this time is utilized most effectively, the short-lived transactions must be a main stream of transactions. Service handoffs which occur due to communication disconnection, could degrade the performance of mobile transactions especially for short-lived transactions. Altruistic locking protocol is in its nature favorable to short-lived transactions since it applies the donation of locks to long-lived transactions as early as possible. Performance for short-lived transactions could be improved when we applied altruistic locking to mobile computing.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.04a
• /
• pp.147-150
• /
• 2002

본 논문에서는 이동 단말기 중 하나인 Windows CE 기반 PDA를 위한 적응적 멀티미디어 스트림 처리기를 설계 및 구현하였다. 제한된 시스템 환경을 갖고 있는 PDA 상에서 멀티미디어 데이터를 실시간으로 전송 및 저장할 때 생기는 메모리 문제를 효율적으로 해결하기 위하여 적응적 버퍼 관리 기법을 사용하였다. 단말기에 적합한 형태로 멀티미디어 데티터를 변환하여 전송하기 위하여 필터를 사용하였고, 필터 파이프라인을 구성하였다. 또한 다양한 형태의 미디움과 필터의 입출력을 일관성 있게 처리하기 위해서 DLM(Dynamic Linking Module) 구조를 사용하였다.

• Journal of Internet Computing and Services
• /
• v.10 no.4
• /
• pp.35-44
• /
• 2009

The emerging notion of data stream has brought many new challenges to the research communities as a consequence of its conceptual difference with conventional concepts of just data. One typical example is data stream processing in sensor networks. The range of data processing considerations in a sensor network is very wide, from physical resource restrictions such as bandwidth, energy, and memory to the peculiarities of query processing including continuous and specific types of queries. In this paper, as one of the physical constraints in data stream processing, we consider the problem of limited memory and propose a new scheme for data stream reduction based on the Principal Component Analysis (PCA) technique. PCA can transform a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables. We adapt PCA for the data stream of a sensor network assuming the cooperation of a query engine (or application) with a network base station. Our method exploits the spatio-temporal correlation among multiple measurements from different sensors. Finally, we present a new framework for data processing and describe a number of experiments under this framework. We compare our scheme with the wavelet transform and observe the effect of time stamps on the compression ratio. We report on some of the results.

• Journal of KIISE
• /
• v.42 no.7
• /
• pp.840-845
• /
• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.