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Recently, Web 2.0 and Semantic Web become popular, and the Semantic Web fusion (Mashup) service using Open API and Linked Data is drawing attention, because it enables new services to be provided in a rapid and convenient way by combining various Linked Data with Open API. The Mashup service is defined as creating a new service by mixing information, contents, or Open API services provided on the Web. Mashup service developed for a specific need, not just a combination of information, may contribute to creating new added values on the basis of the practicality and convenience. This study is on Web standard-based Web platform which may increase accessibility to data in various applications by applying Linked Data technology and using RDF/XML standard format for data.

This paper is aimed to propose a grid density clustering algorithm for spatio-temporal data that is based on the adaptation of the grid density based clustering algorithm. The algorithm is based on AGRID+ algorithm with 7 steps: partitioning, computing distance threshold, calculating densities, compensating densities, calculating density threshold (DT), clustering and removing noises. The adaptation is for the partitioning and calculating the distance threshold (r). The data utilized in this study is spatio-temporal fishery data located around the India Ocean from year 2000 until 2004. We utilized the fishery data in three types of aggregate , daily data, weekly data and monthy data. The result of this study shows that the time complexity for ST-AGRID is outperform the AGRID+. ST-AGRID improves the time complexity and at the same time maintains the accuracy. By utilizing the thresholding technique, clustering result of the ST-AGRID algorithm is identified as the potential fishing zone.

A method of distinguishing several events included in sensor data was proposed, when various events were sensed simultaneously to the data several sensors sensed and reported and individual event data were mixed in an environment in which a data stream was sequentially obtained. This study had each sensor distinguish each event through a fast analysis of the data sensed by each sensor in this condition. For this purpose, clustering was made with sensor data, and first, internal variance of event cluster classified by each sensor was calculated, and how this changed with the passage of time was checked. Next, the cluster of each sensor sensing the same event was compared. Through this process, the sensed event could be more clearly distinguished. This study suggested the measure to divide a small quantity of sensors when it senses the several events. This measure can be used as application for the small mobile vehicle or robot to sense the peripheral situation.

Today’s software development is defined by continuous evolution of software products. These products are regularly updated during their usage. In most of the cases systems grow inevitably by adding new features or by changing the system architecture due to new technologies or business plans. It is more than a decade; objects oriented paradigm is adopted as the most efficient passage to build flexible software, and promptly supported by industry. Though, the benefits of object oriented paradigm are supported by many, but its usage does not necessarily result in general, adaptable systems. These huge systems are often suffering from improper use of object oriented techniques, like inheritance and the lack of object oriented methods being regulated towards the building of families of systems instead of developing single applications. These growing technologies make the systems more difficult to maintain and improve. So, there is growing demand for reengineering of object-based systems. The main intention of this paper is to discover important research directions in the area of reengineering of object oriented legacy systems, which necessitate further attention in order to build more effective and efficient reengineering technique for these systems. The paper first discusses the state of art in reengineering of legacy system and its need. Paper also discuss the benefits of component based system over object oriented system and later outlines the techniques for reengineering of object oriented legacy system. In this paper we presented statistical analysis based on more than a decade data.

Pairwise testing is known to be effective in finding errors while considering only a small fraction of input space. Recently, a new testing technique that generates pairwise tests from the cause-effect graph has been presented. This technique assumes the use of the Alloy analyzer to generate pairwise tests from the cause-effect graph. Since, however, the Alloy analyzer does not support APIs that build and manipulate the Alloy formulas, the task of test generation may be tedious and error-prone. In this paper, we introduce a tool named CEGPairGen that automates the process of generating pairwise tests from the cause-effect graph without any human intervention. CEGPairGen produces a test generator written in Java KodKod APIs that generates pairwise tests from a given cause-effect graph. KodKod has been designed to efficiently construct, manipulate, and solve constraints. CEGPairGen takes an incremental test generation strategy for situations where the number of pairs to be covered grows quickly. We show a case study where experimental assessment of tests produced by CEGPairGen has been carried out.

The purpose of this paper was designing a fall detection algorithm as a solution so victims who are unable to move after accident could get help. Data were gathered by experimenting using accelerometer to find standby threshold and fall threshold. Data for standby threshold were processed by calculating all the differences and thus use the maximum difference for a threshold. Data for fall threshold were also processed by calculating all the differences and thus use the minimum difference from activities that were ended with a fall. Those thresholds were implemented into prototype application and tested again to find its accuracy. Results were 1 on sensitivity and 0.96 on specificity. It could be concluded that this fall detection algorithm has a 0.98 on its accuracy.

MPI system being used diversely abroad is also using the method that distinguishes individuals in combination with the private information. The system to judge the combination of private information quickly and exactly is necessary in this system. In this paper, Suggest the phonetic similar code algorithm of Korean name data for the combination of private information and the algorithms that can extract correct the candidate group by using Korean name features

Duplicate detection is the special case of data matching that discovers groups of records within a single database that belong to same real world entity. It is also an inevitable part of data cleansing because duplicate records can strongly influence the results of later data mining or processing. In this process, one record is compared to all other records. Different data representations, formats, terminologies and data entry errors make this task complex. Involvement of heavy volume databases adds more complexity. To reduce comparison of records, indexing algorithms are used that partition the data and perform comparisons with in that partition. Sorted Neighborhood Method (SNM) is a standard indexing algorithm that sorts dataset by using defined “sorting key” and moves fixed size window to compare records within that window. Duplicate Count Strategy-Multi record increase (DCS++) is latest improvement in SNM that adapts the window size for every duplicate in current window. We propose Innovative Windows (Inn Win) algorithm that assumes i) detected duplicate in sorted dataset raises the probability of finding more duplicates in neighborhood ii) Series of consecutive non-duplicates drops the probability of duplicates in neighborhood. Using this concept, it adapts window both for duplicates and non-duplicates and avoids unnecessary comparisons without losing effectiveness. We prove that Inn Win is a better alternative in windowing algorithms.

In an environment in which several events are sensed in a complex manner and sequentially obtained, a clue can be obtained for inference of situations by classifying each event and analyzing the aspect of change of each event. The study proposes a method to efficiently decide the cluster centers in each subsequent time slot for efficient classification of events and inference of situations in a data stream environment. For the data stream under this condition, each time slot classified at a certain interval is set up, the events using clustering in each time slot are carried out, and to recognize how the aspect of change of each event sensed in a continuous time slot is carried out, the cluster centers are allowed to be rapidly captured.

Prioritization And Selection (PAS) of requirements is an important concern of Agile software development. Existing PAS techniques are primarily binary selection of requirements and postponing lower-priority requirements to the future. As a result, lower-priority requirements may be constantly delayed until they never have a chance to be implemented at all. However, ignoring lower-priority requirements may threaten the quality of software. In this paper, we propose the concept of partial selection as an alternative to binary selection of Agile software requirements rather than ignoring them altogether or postponing them to the future releases. We further contribute a goal-oriented framework that allows requirements to be partially satisfied when tolerated. The framework specifies the fuzzy priority of requirements and accomplishes partial selection through relaxing the satisfaction level of requirements with respect to their priority in the release backlog of software. The experimental results show that partial selection of requirements will reduce the number of neglected requirements when compared to binary selection.

The u-GIS spatial information technology requires the processing of Geosensor information. Geosensor information processing generates real-time and high-volume location and data streams related to locations. To process this kind of data, data stream management systems (DSMS) and spatio-temporal data stream management system (ST-DSMS) are being researched. However DSMS deals with general data streams that do not consider spatial and temporal domains. ST-DSMS supports the processing of spatial and temporal domains. However, ST-DSMS is based on continuous query processing over sliding windows the queries in ST- DSMS that are limited to expressing various spatio-temporal predicates. Moreover, studies on ST-DSMS lacked analyses on multiple attributes and various spatio-temporal. In this paper, we present a new spatio-temporal sliding window for achieving spatio-temporal data stream with multiple attributes. The method is based on periodical query processing over sliding windows and maintains in-out time and value histories of objects. So, in this paper, we can support various spatio-temporal predicates, deal with multiple attributes, and provide complex query syntax analyzing various spatio-temporal correlations

Wearable sensor mobile technologies and machine learning techniques are considered as two of the key research areas in the computer science and healthcare application industries. Our main aim is to build a simple yet accurate mobile application that is capable of real-time diagnosis and monitoring of patients with Coronary Artery Disease (CAD) or heart disease which is a major cause of death worldwide. Most available mobile healthcare systems focus on the data acquisition and monitoring component with little attention paid to real-time diagnosis. In this work, we build an intelligent classifier that is capable of predicting a heart disease problem based on clinical data entered by the user or the doctor and by using machine learning algorithms. This diagnosis component is integrated in the mobile application with a real-time monitoring component that continuously monitors the patient and raises an alarm whenever an emergency occurs. Our results show that the proposed diagnosis component has proved successful with a classification performance accuracy of more than 85% with the cross-validation test. Moreover, the monitoring algorithm provided a 100% detection rate.

Web service is a software system designed to support interoperable machine-to-machine interaction over a network. Currently, it has been EMPHASIZED quality of web services. Many companies and colleges thus are introducing web service-based business processes, being aware of the advantages. For a successful use of a web service-based business process, a way to evaluate the quality of web service and manage the web service application. Therefore, we described web service models such as SOAP, WSDL, UDDI in this paper. And we analyzed the characteristics of service oriented architecture. Eventually, we presented a management aspect of web service quality.

This paper presents Q-mark model which is for quality of R&D projects in ETRI since 2008. The model has the simplified tailoring processes with selection & concentration based on ISO/IEC 12207 & 15288 for quality of R&D projects. There are 5 processes for quality of R&D projects in Q-mark model such as the requirement definition process [RDP] as staring point of project, testing process [TP] as ending point of project, inspection as peer review & requirement tracing phase between RDP and TP, project execution management process with WBS, issue, and risk management, and configuration management and quality assurance process for quality of R&D products such as documents and development technology. There are also survey results such as R/D product’s quality upgrade level, technology transfer and business impacts, and improvement consideration as deployment expectation of Q-mark model in this paper. The Q-mark model has been adapted and improved continuously for quality upgrade of R&D projects, and inner and outer customer satisfaction of products.

Analyzing the unstructured information in the source code (that is, the comments and identifiers) is based on the idea that the unstructured information reveals, to some extent, the concepts of the problem domain of the software. This information adds a new layer of source code semantic information and captures the domain semantics of the software. Developers use identifiers, method names, and comments to incorporate components of the solution domain of the software. Topic models reveal topics from the corpus, which embody real world concepts by analyzing words that frequently co-occur. These topics have been found to be effective mechanisms for describing the major themes spanning a corpus. Recently, software engineering researchers established that topic models can be effective in structuring various software artifacts, such as bug reports and requirements documents. In this paper, we extract topic models from the textual content of source code by conducting a case study on the source code of Java-based open-source systems, ArgoUML, Checkstyle, JHotDraw and jEdit. The paper investigates the effectiveness of LDA in comprehending large open-source software systems.

Sentiment analysis is now in focus of companies to extract information from customers’ reviews. Usually the analysis classification is positive, negative and neutral. In this research we focus on the reviews for electronic products. The demographic and technical expertise level of consumers and reviewers are diverse hence there is difference in the way they review a product. Some review contains technical solutions, improvised ways of tackling problems, frustrations, joy etc. This differences in review calls for a wider classification scheme to contain these differences. We thereby introduced a five classification scheme namely positive, negative, advice, no sentiment and neutral at the sentence. We crawled data from amazon.com and used open source natural language processing tools to get the sentiment out of the review.

AM-heap is a heap data structure with O(1) amortized insertion time and O(logn) deletion time complexities. This paper presents cache-aligned 4-ary AM-heap in which every node has four children except that the root has three children. In this heap, the leftmost node of each sibling has an index of a multiple of four. Also, every node index matches its corresponding array index and every sibling nodes smoothly fits into a single cache block. Our experimental results show that cache-aligned 4-ary AM-heap is faster than AM-heap as well as post-order heap.

Performance dashboard is one of the way to improve a company’s performance, a hospital in this study, due to the ease it provides for the executive entities of the hospital in measuring, supervising, and evaluating the performance of the hospital based on the Key Performance Indicator (KPI). The information requried by the executive entities in making a strategic decision involves numerous separate source of data. By implementing performance dashboard, the existing data sources will be consolidated and demonstrated in an informative visual form through an interface screen in order to allow the executives to easily comprehend and analyze those data, therefore assisting the process of strategic decision making. Currently, a lot of hospitals uses performance dashboard. By applying it, hospitals are capable of identifying the strengths and weaknesses pertaining to the them, and eventually, use such information to improve the performance. By an increase in performance, the loyalty of patients will be maintained and the hospital’s competitiveness will also increase.

Recently, overseas countries have shown an increasing interest in Massive Open Online Source (hereafter referred to as MOOC), a new open education platform from the world's best universities including Stanford, MIT, Harvard and others. In this situation, one of the first things to consider in a growing atmosphere of participation in MOOC services by many universities/institutions in Korea is how to configure MOOC platform suitable for educational environment of Korea. This study investigated research trends of MOOC standards at home and abroad, and analyzed functions of MOOC platform according to standard items. Based on the results, it extracted features that K-MOOC platform should have and proposed K-MOOC standardization measures that reflect these features.

Managing software changes and achieving an effective change acceptance decision is one of the crucial factors in ensuring the software project success. Accepting too many changes will cause expense and delay and rejecting the changes may cause customer dissatisfaction. One of the inputs that help in achieving an effective change acceptance decision is by having reliable estimation on the change effort. From software development perspective, the estimation has to take into account the inconsistent states of software artifacts across project lifecycle i.e., fully developed or partially developed. These inconsistent states require different ways of estimation such as the fully developed artifacts may have different calculation compared to the partially developed artifacts. Many change effort estimation approaches have been developed and one of them is using impact analysis. One main challenge of this technique from software development perspective is that this technique is specifically used for software maintenance phase in which all software artifacts have been completely developed. This research introduces a new change effort estimation approach that is able to use different estimation techniques for different states of software artifacts. The outcome of this research is a new change effort estimation approach for software development phase using the extended version of the static and dynamic analysis techniques from our previous works.

Ubiquitous health u-Health system is focused on the concept of providing medical service and assistance to the patients "anywhere and anytime" regardless of their locations. In u-Healthcare environment, large amounts of important medical information is processed through wireless communication. Mobile health has been receiving more and more attention recently as an emerging paradigm that brings together the evolution of advanced mobile and wireless communication technologies with the vision of “connected health” aiming to deliver the right care in the right place at the right time. This study is expected to help ensure an excellent workforce in the healthcare sector using smart phones, and to help reduce medical expenses by improving the health of citizens.