Earticle

Until now, the interests of the research about sensor network security have been focused on the security services that provide authentication, confidentiality, integrity, and availability. However, the interest in the issue of actual identifier's exposure of sensor node is rapidly increasing. Also, the interest in the efficiency of creating encryption key that is used for the sensor network is increasing. Many schemes for providing node's anonymity in the existing Ad-Hoc network were suggested, but these schemes are not appropriate for sensor network that is energy-limited, so a scheme for providing anonymity that is suitable for sensor network's characteristics is required. Also, Sensor network maintains high limitation of resource because it performs many communications in order to create encryption keys. To solve these problems, this research suggests LA2EP Protocol. LA2EP Protocol can minimize resource and provide a new scheme for authentication and encryption that can provide anonymity of node for safe communication. To analyze the performance of the suggested protocols, a degree of anonymity that is provided by the scheme suggested by using an Entropy-based modeling was measured. As a result, when the suggested scheme was used, the degree of anonymity of sensor node was high. It showed that an important element to increase the degree of anonymity was to let the sensor's ID not known correctly. Also, as a result of calculating spaces for operation, communication, and storage while considering the characteristic of sensor network, which is limited in resource, it showed suitability for sensor network environment.

We present a prototype web application for children shoes suggestion. We are interested in attributes of foot and shoes that can comfort the children’s feet. The key attributes are types of shoes, size, and brands. For the feet, there are width, length, thickness, sole curve, toe and others. The application has the database that stores these children shoes and foot characteristics for training. The data mining model is created using decision tree, KNN, neural net and integrated to the application.

Increasing number of data is occurs because of high demand from organizations to run their daily business operations. Most of organizations have an information system in order to provide quality information to the organizations. In order to provide the quality information, the information system must be able to filter any dirty from data sources. One of the type dirty data is inconsistent data. Inconsistent data is occurs because of data structured from different data sources are different. Four latest techniques to detect and reduce inconsistent data have been identified. These techniques are rough set, logic analysis of inconsistent data, fuzzy multi attributes decision making and functional dependencies of corresponding relation variable. In this paper, these techniques have been studied described with suitable examples. The purpose of studied is to identify advantages, disadvantages and any potential enhancement in reducing inconsistent data from database.

In this study, we characterized traffic density modeled from coarse data by using data signatures to effectively and efficiently represent traffic flow behavior. Using the 2006 North Luzon Expressway North Bound (NLEX NB) Balintawak (Blk), Bocaue (Boc), Meycauayan (Mcy), and Marilao (Mrl) segments' hourly traffic volume and time mean speed data sets provided by the National Center for Transportation Studies (NCTS), we generated hourly traffic density data set. Each point in the data was represented by a 4D data signature where cluster models and 2D visualizations were formulated and varying traffic density behaviors were identified, i.e. high and low traffic congestions, outliers, etc. Best-fit curves, confidence bands and ellipses were generated in the visualizations for additional cluster information. From a finer-grained 6-minute interval NLEX Blk-NB density data set, the coarser-grained hourly density data set of Blk was validated for consistency and correctness of results. Finally, we ascertained probable causes of the behaviors to provide insights for better traffic management in the expressway.

The topics on human mobility model have long been researched by various academic and industrial elds. It also has been proven that human mobility has specic patterns and can be predicted up to the probability of 93%, since the mobility of a person cannot be random while peoples have their own frequent visiting places. As a basis of human mobility research, sets of positioning data is used widely. The positioning data of a human can be obtained by GPS or other similar positioning systems, however, it contains inherited environmental errors. It is clear that such position errors harm the correctness of human mobility re- lated results. In this paper we will present ltering method of erroneous positioning data of human mobility with the use of One-Class Support Vector Machine (OCSVM), and we adapted Radial Basis Function (RBF) as kernel function. Experimental values of the criti- cal parameter for RBF have been found for optimal ltering. With this optimal parameter, we ltered raw data set of human mobility trail in order to obtain accurate position data set for further research purpose. By iteratively applying the OCSVM based ltering, like hill climbing approach, we prove that researchers can lter arbitrary rate of raw data for their own purpose. With four sets of positioning data set from various sources, we demonstrate the usefulness of our ltering approach.

While the database collection types set, list, and record have received in-depth attention, the fourth type, array, is still far from being integrated into database modeling. Due to this lack of attention there is only insufficient array support by today's database technology. This is surpris-ing given that large, multi-dimensional arrays have manifold practical applications in earth sciences (such as remote sensing and climate modeling), life sciences (such as microarray data and human brain imagery), and many more areas. Consequently, flexible retrieval today is supported on metadata, but not on the observation and simulation data themselves. To overcome this, large, multi-dimensional arrays as first-class database citizens have been studied by various groups worldwide. Several formalisms and languages tailored for use in array databases have been proposed and more or less completely implemented, sometimes even in operational use. In the attempt towards a consolidation of the field we compare four important array models, AQL, AML, Array Algebra }, and RAM. As it turns out, Array Algebra is capable of expressing all other models, and additionally offers functionality not present in the other models. We show this by mapping all approaches to Array Algebra. This establishes a common representation suitable for comparison and allows us discussing the commonalities and differences found. Finally, we show feasibility of conceptual array models for describing optimization and architecture.

Clustering is a challenging task in data mining technique. The aim of clustering is to group the similar data into number of clusters. Various clustering algorithms have been developed to group data into clusters. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numerical data types in previous k-means algorithm was used but it is not accurate for large datasets. In this paper we cluster the mixed numeric and categorical data set in efficient manner. In this paper we present a clustering algorithm based on similarity weight and filter method paradigm that works well for data with mixed numeric and categorical features. We propose a modified description of cluster center to overcome the numeric data only limitation and provide a better characterization of clusters. The performance of this algorithm has been studied on benchmark data sets.

Querying a database is a common task for most database systems. To query a database is to find some answers from stored data. Traditional database systems return exactly what is being asked. This is a method of direct query answering and users are required to construct a query intelligently and properly. To remove the burden of intelligence from the database users, the concept of intelligent or cooperative query answering has emerged. The process of intelligent query answering consists of analyzing the intent of query, rewriting the query based on the intention and other kinds of knowledge, and providing answers in an intelligent way. Intelligent answers could be generalized, neighborhood or associated information relevant to the query. This concept is based on the assumption that some users might not have a clear idea of the database content and schema. Therefore, it is difficult to pose queries correctly to get some useful answers. Producing answers effectively depends largely on users' knowledge about the query language and the database schema. Knowledge, either intentional or extensional, is the key ingredient of intelligence. In order to improve effectiveness and convenience of querying databases, we design a systematic way to analyze user's request and revise the query with data mining models and materialized views. The models obtained from the automatic knowledge extraction process is a set of association rules discovered from the database contents. Materialized views are pre-computed and normally aggregated data from base tables to speed up the processing of frequently asked queries. This paper presents the knowledge acquisition method focusing on association pattern mining, its implementation, and a systematic method of rewriting query with association patterns and materialized views. We perform preliminary efficiency tests of the proposed system. The experimental results demonstrate the effectiveness of our system in answering queries sharing the same pattern as the available knowledge and the pre-computed views.

This paper presents the concepts of database systems as well as the overview of the use of Unified Modeling Language (UML) as a standard notation of real-world objects in developing object-oriented design methodology for computer applications. The UML is a tool for specifying software systems that include standardized diagrams to define, illustrate and visually map or model a software system's design and structure. UML diagrams include the use case diagram, class diagram, sequence diagram, statechart diagram, activity diagram, component diagram, and deployment diagram. The integration of these diagrams to different software processes have been discussed.