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Recently, the paradigm shift in water management and the development of information technologies has made intelligent water management platform which can be two-way communication. In addition, it carried out various hydrologic information projects and research but It was still difficult for user to get a water information. And the integrated management of water information is not. Smart Water Grid (SWG) is the technology for the efficient operation and management of water resources which was distributed spatial. For this, especially it is important to increase quality and service of information through standardization. Globally the water information system has been applied to a data standards system like that WaterML. WaterML standards for Water Markup Language. It is a standardized dialect of XML designed for transmitting hydrological data. In this study propose a database standardization system for water management in Korea. Also it is to establish interoperability and linkages of National and Regional about water information.

Implementation of an analytical model of component based software into web based system is an important issue towards the effective software development. The mechanism towards such model implementation also enriches the quality of the model design. Formal specification would help to provide concrete and precise concept of a model. This paper has proposed a formal specification of an analytical model for a component based system, called Component-Class-Interface (CCI) which facilitates a sophisticated description of component model with mathematical explanation. A logical level implementation mechanism of such formally specified analytical model has been also implemented along with re-engineering facility. Moreover, a rule based transformation mechanism has been proposed to transform CCI into logical model. The correctness of such model transformation has been evaluated using Triple Graph Grammar. A comparative study has also been discussed to show the expressiveness of the proposed methodology. Moreover the concepts of the proposed approach have been implemented using a CASE tool.

Many data on smartphones and Internet development is communicated indiscriminately, thereby undergoing a rapid increase in data traffic. Especially in social commerce, the enormous information, the user becomes difficult yourself to find the necessary information. Even a company that provides the service, became difficult to be provided to understand the user needs information. Data that is not classified as an increase in senseless amount of data has become a new problem of this age. And is only collect the required data through the analysis, if the exact customized service is provided, it will be able to resolve the data traffic problems. Therefore, in this paper, we describe an efficient method of collecting big data to you customized advertising service users.

Bus information systems provide static (such as bus stops, bus routes, trip duration, and fares) and real time (bus arrival time, for example) information to bus passengers. This information is essential for the users to make their travel schedules intelligently. As electronic and electric components are getting smaller, computing power of smartphones is getting faster and memory capacity of smartphones is getting larger. In addition to that, many useful electronic gadgets including accurate sensors, GPS receivers, cameras and others are integrated into smartphones nowadays. Making use of these gadgets, we can find out most of information serviced by the current Bus Information Systems. This paper proposes an efficient method to recognize the bus route of a passenger's bus with GPS information from the passenger's smartphone and an efficient method of calculating trip duration under the current traffic status.

Inheritance and composition are two different and closely related techniques that allow a modeller to extend the properties of a class. The mantra “favor composition over inheritance” has been around for many years. Many examples have been given to support such a conclusion. In this paper we discuss a special type of inheritance . direct inheritance versus single member composition, and show that inheritance is more accurate and concise in these cases.

In cloud computing with host virtualization, it is necessary to understand the queue of storage for disk requests due to the competition for the storage requirements of Virtual Machine (VM). In this paper, we address schemes, sequence and dynamic balance allocation, for handling the VM queue to access the virtual storage device. SimPy, python-based simulation tool, is adopted to build the simulation models to get analytical data. The SimPy simulator incorporates the resource analyzing.

This paper proposes a formal approach for development of safe web applications. This approach involves the generation of a web application on both sides (users’ side (Ajax) and the web service side (Composition)) from formal specifications. First, we describe the application using symbolic notations then an automatic process is applied in order to translate them into formal specifications B. Using the B refinement process, a set of refinement rules, operating on data and operations. These phases of refinement are intended to make the final specifications close to the implementation language chosen so that the last coding phase becomes intuitive and natural. In general, the process of refinement is a manual task, relatively expensive; with character generic of these refinement rules, an assisted refinement tool can be achieved, enabling reduction the cost of the refining process. Finally, in the case study, we have developed a complete application (Travel Agency) in order to show the benefits of our approach.

A smart cold chain management is an information system which monitors and maintains the proper temperature of the products during whole cold chain, and analyzes data to report abnormal environmental status and risks to producers and consumers. In this paper, we propose an application framework for smart cold chain management system based on Hadoop, Spark and IoT (Internet of Things) techniques. The proposed application framework for smart cold chain provides PaaS (Platform as a Service) and IaaS (Infra as a Service) so as that smart cold chain management systems can be developed and operated with low cost and in short time. Also, the proposed application framework for smart cold chain allows the heterogeneous IOT devices such as RFID tags, WSN sensor nodes, BLE (Bluetooth Low Energy) sensor nodes and so on. We design PaaS and IaaS based on Hadoop and Spark to store the large amount of data stream on salable storage and process stream data in real time to detect events and assess risks in cold chain. Through generalizing the functions of existing cold chain management systems, common components in smart cold chain management systems are drawn.

This paper suggests the optimization method of RDF Repository. When state data that occurred thing composing context information, this method processes in real-time using ontology technique in IoT environment. It needs to adapt meaning system about state to delivery correct state among different things. In other words, it is important to adapt technique to guarantee Semantic Interoperability. For this, People suggest a method such as SSNO based on ontology. However, techniques were center of modeling to express most things. They have a lack of paper about real-time reasoning engine that processes thing’s state and context. Also, ontology reasoning engine based on RDF repository currently optimized static data processing. Due to this reason, it can not be satisfy real-time processing that request from IoT service. Therefore, this paper research a method about optimizing RDF repository composition to solve this problem.

Soft testing is one way of assuring quality of the software. There are many testing techniques to test the software, however random testing is one among the best alternatives, which will generate the test inputs based on some dissemination. It is evaluated theoretically and concluded that the performance of random testing is very impressive when it is compared with partition testing even though it gives good results for testing. Here in this paper we addressed the difference in coverage between random testing and partition testing, here we have taken one example program and written the test cases for that. To test the program we have used automated tools JUnit for partition testing and Randoop for random testing. Once it is over using EclEmma the coverage of test cases is evaluated and proved RT is best.

In this paper, the customized team matching system for revitalizing the athletics in public is proposed in smart device environment. The proposed system, CTMS; Customized Team Matching System, collects profiles of community sports teams, analyzes those profiles, and recommends customized sport team. The proposed system is expected to design new service model which will improve revitalization of community sports in smart media era.

Cloud Computing is one of the emerging technologies. This research paper aimed to outline cloud computing and its features, and considered cloud computing for machine learning and data mining. The goal of the paper was to develop a recommendation and search system using big data platform on cloud environment. The main focus was on the study and understanding of Hadoop, one of the new technologies used in the cloud for scalable batch processing, and HBase data model which is a scalable database on top of the Hadoop file system. Accordingly, this project involved the design, analysis and implementation phases for developing the search and recommendation system for staffing purpose. So, mainly the action research method was being followed for this.

In this work we will create a system of recognition of Moroccan plate-style vehicle license plates using the invariant moments in features extraction and the support vectors machines such as Moroccan plate is slightly treated by researchers in pattern recognition field. In this context we propose a data set for Moroccan plates composed of 100 images capted with Samsung I9000 mobile camera with maximum resolution 2560 x 1920 dpi. For this purpose we have used in our system is composed by three main phases: the preprocessing of plates followed by the extraction of primitives with invariant moments method in order to convert each image into a vector number which is nothing other than an information extracted from this plate just to differentiate the others. Finally, our recognition system will end with a classification phase by the support vectors machines (SVM). Finally, the recognition system has been proved to be accurate and efficient by the experiment results.

The development of ICT has made it possible to incorporate innovative technologies into various university courses in order to complement conventional curriculum and educational settings. With the help of advanced information and communication technology, anytime and anywhere principles have been realized in both learning and teaching. The aim of this study is to investigate the impact of using ICT, especially the social networking system, as a course management platform for more authentic and interactive university instruction. Among various social networking systems, Facebook is chosen because of its popularity and convenience for both teachers and students. This study depicts a case study of implementing Facebook group as the course management platform and then explains how the use of Facebook group can make English academic writing instruction become more genuine and interactive in Korean EFL university context.

The personal name aliases are extremely significant in information retrieval to retrieve complete information about a personal name from the web, as some of the web pages of the person may also be referred by his or her alias name / nick name / real name. There is a rapid growth in people searching where the personal name aliases are concerned. We proposed a pattern generator which includes automatic: lexical pattern extraction algorithm and attribute extraction algorithm. We exploit three data set of known Personal names (consisting of alias name, real name, and nick name), Profession and location names of a person as training semi-structured data set to efficiently extract lexical patterns. The extracted patterns are ranked according to F-Score. It conveys information related to alias names from contingency table returned by web search engine. The extracted lexical patterns (profession pattern and location name pattern) are often used to optimize candidate personal name aliases with attributes of a person availed in the contingency table, the non-frequent items are discarded from the contingency table. Next, we ranking the candidate alias in contingency table, Graph mining ranking algorithm with various similarity measures are used then to measure the strength of association between a name and a candidate alias, co-occurrence statistics are computed.

Hadoop distributed file system (HDFS) is designed to store huge data set reliably, has been widely used for processing massive-scale data in parallel. In HDFS, the data locality problem is one of critical problem that causes the performance decrement of a file system. To solve the data locality problem, we propose an efficient data replication scheme based on access count prediction in a Hadoop framework. By the previous data access count, the existing data replication scheme predicts the next access count of data files using Lagrange’s interpolation. Then, the proposed data replication scheme determines the replication factor with the predicted data access count, whether it generates a new replica or it uses the loaded data as cache selectively. Finally, the proposed scheme provides improvement of data locality. By performance evaluation, proposed efficient data replication scheme is compared with default data replication setting of Hadoop that shows proposed scheme reduces averagely 8.9% of the task completion time in the map phase. Regarding the data locality, proposed scheme provides the increase of node locality by 6.6% and the decrease of rack and rack-off locality by 38.9% and 56.5%.

The customer satisfaction usually depends on quality of a product or a service. The results of customer satisfaction survey are utilizable for quality control or quality improvement. Especially, those survey results can be used as a guideline for evaluating and improving the quality. A cyber-infrastructure named as CyberLab was developed and has been introduced for the purpose of education and research in Korea. The main purpose of this cyber-infrastructure is supporting the bidirectional education and remote collaboration environment in research. To improve the quality of the system, user satisfaction surveys had been conducted three times for three consecutive years. This research identifies the customer preferences or annual changing pattern of quality, based on several conclusions drawn by analyzing the survey results. Comparison results regarding various quality-attributes in terms of time and customer group will be provided. Furthermore, several implications from the analyses including cause and effect analysis will be stated.

Due to the limited lifetime of nodes in ad hoc and sensor network, energy efficiency needs to be an important design consideration in any routing algorithm. Most of the existing Ant colony based routing algorithms grantee the packet delivery. However, they suffer from the high power consumption due to the huge number of control messages to establish and maintain a route from a source to a destination. This paper introduces two new power-aware ant colony routing algorithms for mobile ad hoc network under three main concurrent constraints. (1) Localized algorithms where only information about neighbors' nodes is needed. (2) Maximize the algorithms delivery rate. (3) Minimize the energy consumption. Our new algorithms are based on the idea of extracting a sub-graph of the original network topology and combine it with the advantage of ant based routing algorithms. Extensive experiments are conducted to prove that the new algorithms have significant improvement on the network lifetime (up to twice) without affecting the high delivery rate.

Managing and processing the large scale of data is the crucial part of the experimental high energy physics which uses a gigantic instrument in order to exploit the world’s largest particle accelerator at CERN. The WLCG has enabled the distributed computing technology, which is called the grid, and has proven its excellent performance by contributing their capability of data management and processing. In addition to the grid computing, a compact but powerful computing facility introducing the parallelism is designed. In this paper, we introduce the KIAF cluster which is designed to process large scale of data produced from the ALICE experiment at CERN LHC in parallel based on PROOF. The PROOF enables parallelism on a Linux cluster by exploiting the special characteristic of the data produced in high energy physics. Event processing performance, the number of event processed per second and the size of data processed per second, of the KIAF cluster is shown with the practical use case for the high energy physics. The performance of the KIAF shows pseudo-linearity increasing as the number of workers involved in the processing while it shows as well an evidence of the upper limitation of the scalability of PROOF cluster in terms of the performance.

As the service for big data increases, the number of analysis servers for big data grows rapidly. For example, the scale of analysis servers for the big data from Large Hadron Collier in Swiss CERN is generally beyond tens of thousands of servers. Therefore, tasks for the selection of the appropriate local routing protocol and the efficient configuration of the routing protocol for those servers are getting more important portion in the local network administration in big data center. In this paper, we introduce BGP as a routing protocol for the local network of big data center even though it is developed for WAN. The concept we introduced in this paper for the separation of big data traffic in local data center network is created based on the concept of the partition of the conference room in convention center. The main conference room can be partitioned by multiple small meeting rooms according to users’ requests. Each small room can be protected from the noise of the neighbor meeting rooms by partition walls. In the same way, the huge number of servers can be grouped into various server farms and then each of server farms can be identified Autonomous System number that is used for the BGP routing domain for network administrator. Using the techniques of AS numbering we can control the big data traffic more efficiently than ever in data center. We introduced new application method of AS number for a service to recognize certain traffic. Therefore, we can separate and control big data traffic more in detail. We tried to prove that BGP is superior to IGP and overlay networking with respect to the routing control in big data center.

To extract hand tracks and hand shape features from continuous sign language videos for gesture classification using backpropagation neural network. Horn Schunck optical flow (HSOF) extracts tracking features and Active Contours (AC) extract shape features. A feature matrix characterizes the signs in continuous sign videos. A neural network object with backpropagation training algorithm classifies the signs into various words sequences in digital format. Digital word sequences are translated into text with matching and the suiting text is voice translated using windows application programmable interface (Win-API). Ten signers, each doing sentences having 30 words long tests the performance of the algorithm by computing word matching score (WMS). The WMS is varying between 88 and 91 percent when executed on different cross platforms on various processors such as Windows8 with Inteli3, Windows8.1 with inteli3 and windows10 with inteli3 running MATLAB13(a).

Renewable energy resources for electric power generation have got the spotlight by industry and academia as greenhouse effect has become more severe. Micro Grid is an isolated power system that has generators and power loads. For isolated area Micro Grid is an ideal power system for generating electrical power with renewable energy resources like the solar and wind energy. However generations with renewable energy resources cannot meet the demand of loads securely. So smart applications with communication infrastructure are required to control and monitor the power system. The communication infrastructure for smart power grid can be classified as Home Area network (HAN), Neighborhood Area Network (NAN) and Wide Area Network (WAN). Many technologies including cellular network and PLC are competing for communication in Micro Grid. This paper provides discussion of power line communication technologies for Micro Grid.

In an earlier study, a multi-layered PHD management system for a very large number of PHDs was proposed. The purpose of this study is to measure the performance of the multi-layered PHD management system in order to assess the usefulness of the system in managing a very large number of PHDs. For this paper, performance experiments on 4, 5 and 6-layered systems were conducted. Through the experiments, it was found that the multi-layered system proposed in the earlier study is very useful for the PHD management system with a large number of PHDs. In addition, it was also found that the message integration scheme is very effective with a very large number of PHDs to reduce traffic loads.

Trending strategy for better management of system complexity is following object-oriented approach in development of software. Object-oriented (OO) approach increases the chances of upgrading quality. Few researches have been in direction of OO software development and metrics utility. Also, evidence shows for inadequate of empirically linking the OO methodology and project outcomes. Need of hour is advancement in research for better understanding the parameters influencing software quality and other project effects such as productivity in OO software development. This has been accepted as one of the prevalent challenges in OO methodology and is trying to resolve these issues. This paper discusses the role of CK metric suite in elucidating object-oriented software bugs at a class level. The analyses deals with projects following OO approach during development highlighting the correlation of the metrics in CK suite with the bugs present. The motivational drive is to explicitly accounting that larger classes will have more number of bugs and we tried to develop model based on certain proposed hypotheses. Our approach is to study the effect of complexity that arises due to interaction among various CK metrics suite and bugs.

Electric vehicles (EVs) charging algorithms considering a photovoltaic (PV) system have been studied in this paper. The power needed to charge the EVs comes from grid-connected photovoltaic generation or the utility. This work includes the issues such as how to maximize photovoltaic generated power and reduce energy cost of EVs charging, that is important for the integration of efficient EVs charging stations in smartgrids. A model predictive control (MPC) with linear programming (LP) is used for optimal control, and the time-of-use (TOU) price is included to calculate the energy costs. Simulation results show that the reductions of energy cost can be achieved using the proposed algorithms

The Classifier Integration Model (CIM) with Convolutional Neural Networks (CNNs) as its local classifiers is applied to an object classification task for vehicle safety system in this paper. The Convolutional Neural Networks adopted in this paper has a very unique advantage when compared with conventional neural network models because CNNs do not require any feature extraction procedure prior to the classification process while other existing classification methods require rather very complex feature extraction process. Several CNN models are first designed as local classifiers and these models are then combined to make a decision in Classifier Integration Model for our classification task. Experiments on real data sets obtained for our experiments show that the CNN-based CIM scheme gives a promising performance in terms of training speed and classification accuracy.

One of the most important challenges to achieve high-performance in multiprocessor environments such as parallel and distributed systems is task scheduling. In such architectures, each program is decomposed into the smaller and dependent segments so-called tasks. To formulate the problem, execution times of the tasks, precedence constrains and communication costs among them are modeled using a directed acyclic graph (DAG) named task graph. The goal is to minimize the program completion-time (makespan) by means of mapping the tasks to a predefined number of identical processors in such a way that precedence constrains are preserved. This problem is shown to be NP-hard in general form, and hence, a number of heuristic approaches to solve it have been introduced. A large number of proposed approaches in the literature use list-scheduling technique in which a list of tasks is created based on some priority measurements, and then in each step, the most priority task in the list is selected to schedule on the processor that allows the earliest start time (EST) until all tasks are scheduled. Assigning tasks to the processors using the EST is based on the two different techniques: insertion-based assigning (IBA) and non-insertion based (non-IBA). In this paper, we survey these two different approaches in details, and analyze the strategies and philosophies behind them. In addition, a comprehensive set of experiments and evaluations from different points of view has been done, and various results and conclusions have been presented.

Unlike physical systems consisting of hardware, virtual machines are conceptual computers in logical configurations made of software. Although programs implemented in virtual machine environments have platform-independent characteristics, their performance is low in terms of implementation speed and they cannot implement those parts that are dependent on the platform by themselves. In addition, they have the shortcoming of being unable to reuse those libraries and programs that are written in the native language. In the present paper, a study for provision of native interfaces of smart virtual machines (SVMs) is introduced. The native interfaces of existing systems were analyzed to define a native interface protocol for SVMs, an interface definition language and an interface generator were designed for effective use of the defined native interface protocol, and the operation of the implemented native interface was verified through experiments. The native interface proposed in the present paper consists of items and rules necessary for information transmission between virtual machine environments and native environments, interface definition files necessary to deliver information on native functions to virtual machines, and functions and data types necessary for native environment to receive information from virtual machine environments. Through such native interfaces, virtual machine environment developers can use environment-dependent function without modifying virtual machine environments.

The purpose of this study is to analyze data from Open Source Software (OSS) community with an objective of identifying community metrics that can predict quality of OSS projects. We experimented with data from Apache OfBiz and Apache httpd-2 server OSS projects. We applied linear regression technique to the dataset to assess the strength of possible relationships of variables and also examined possible trends amongst variables. From the analysis, we found out that the size of user mailing list has a correlation with number of reported bugs. We concluded that the size of user mailing list community may not be an accurate representation of the entire user community that adopted the project basing on quality. However Backlog Management Index was found to be a better metric for assessing how projects manage issues reported by users.