Search results for: link data

Authors: Chien-Hung Huang, Chia-Wei Weng, Chang-Chih Chiang, Shih-Hua Wu, Chih-Hsien Huang, Ka-Lok Ng

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MicroRNAs (miRNAs) are a class of non-coding RNAs that hybridize to mRNAs and induce either translation repression or mRNA cleavage. Recently, it has been reported that miRNAs could possibly play an important role in human diseases. By integrating miRNA target genes, cancer genes, miRNA and mRNA expression profiles information, a database is developed to link miRNAs to cancer target genes. The database provides experimentally verified human miRNA target genes information, including oncogenes and tumor suppressor genes. In addition, fragile sites information for miRNAs, and the strength of the correlation of miRNA and its target mRNA expression level for nine tissue types are computed, which serve as an indicator for suggesting miRNAs could play a role in human cancer. The database is freely accessible at http://ppi.bioinfo.asia.edu.tw/mirna_target/index.html.

Keywords: MicroRNA, miRNA expression profile, mRNAexpression profile, cancer genes, oncogene, tumor suppressor gene

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Authors: Ghassan A. Abed, Samir I. Badrawi

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The advancement in wireless technology with the wide use of mobile devices have drawn the attention of the research and technological communities towards wireless environments, such as Wireless Local Area Networks (WLANs), Wireless Wide Area Networks (WWANs), and mobile systems and ad-hoc networks. Unfortunately, wired and wireless networks are expressively different in terms of link reliability, bandwidth, and time of propagation delay and by adapting new solutions for these enhanced telecommunications, superior quality, efficiency, and opportunities will be provided where wireless communications were otherwise unfeasible. Some researchers define 4G as a significant improvement of 3G, where current cellular network’s issues will be solved and data transfer will play a more significant role. For others, 4G unifies cellular and wireless local area networks, and introduces new routing techniques, efficient solutions for sharing dedicated frequency bands, and an increased mobility and bandwidth capacity. This paper discusses the possible solutions and enhancements probabilities that proposed to improve the performance of Transmission Control Protocol (TCP) over different wireless networks and also the paper investigated each approach in term of advantages and disadvantages.

Keywords: TCP, Wireless Networks, Cellular Systems, WLAN.

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Authors: Wei-hua Ai, Wei Yan, Xiang Li

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Data compression is used operationally to reduce bandwidth and storage requirements. An efficient method for achieving lossless weather radar data compression is presented. The characteristics of the data are taken into account and the optical linear prediction is used for the PPI images in the weather radar data in the proposed method. The next PPI image is identical to the current one and a dramatic reduction in source entropy is achieved by using the prediction algorithm. Some lossless compression methods are used to compress the predicted data. Experimental results show that for the weather radar data, the method proposed in this paper outperforms the other methods.

Keywords: Lossless compression, weather radar data, optical linear prediction, PPI image

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: Data management, digitization, Industry 4.0, knowledge engineering, metamodel.

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Authors: Bogdan Walek, Cyril Klimes

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In present days the area of data migration is very topical. Current tools for data migration in the area of relational database have several disadvantages that are presented in this paper. We propose a methodology for data migration of the database tables and their data between various types of relational database systems (RDBMS). The proposed methodology contains an expert system. The expert system contains a knowledge base that is composed of IFTHEN rules and based on the input data suggests appropriate data types of columns of database tables. The proposed tool, which contains an expert system, also includes the possibility of optimizing the data types in the target RDBMS database tables based on processed data of the source RDBMS database tables. The proposed expert system is shown on data migration of selected database of the source RDBMS to the target RDBMS.

Keywords: Expert system, fuzzy, data migration, database, relational database, data type, relational database management system.

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Authors: K. Hardy, A. Maurushat

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Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.

Keywords: Big data, open data, productivity, transparency.

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Authors: Lee Boudreau, Ash Kumar Khaitu, Laura A. S. MacDonald

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In April 2015, a magnitude 7.8 earthquake struck Nepal, killing, injuring, and displacing thousands of people. The earthquake also damaged water and sanitation service networks, leading to a high risk of diarrheal disease and the associated negative health impacts. In response to the disaster, the Environment and Public Health Organization (ENPHO), a Kathmandu-based non-governmental organization, worked with the Centre for Affordable Water and Sanitation Technology (CAWST), a Canadian education, training and consulting organization, to develop two training programs to educate volunteers on water, sanitation, and hygiene (WASH) needs. The first training program was intended for acute response, with the second focusing on longer term recovery. A key focus was to equip the volunteers with the knowledge and skills to formulate useful WASH advice in the unanticipated circumstances they would encounter when working in affected areas. Within the first two weeks of the disaster, a two-day acute response training was developed, which focused on enabling volunteers to educate those affected by the disaster about local WASH issues, their link to health, and their increased importance immediately following emergency situations. Between March and October 2015, a total of 19 training events took place, with over 470 volunteers trained. The trained volunteers distributed hygiene kits and liquid chlorine for household water treatment. They also facilitated health messaging and WASH awareness activities in affected communities. A three-day recovery phase training was also developed and has been delivered to volunteers in Nepal since October 2015. This training focused on WASH issues during the recovery and reconstruction phases. The interventions and recommendations in the recovery phase training focus on long-term WASH solutions, and so form a link between emergency relief strategies and long-term development goals. ENPHO has trained 226 volunteers during the recovery phase, with training ongoing as of April 2016. In the aftermath of the earthquake, ENPHO found that its existing pool of volunteers were more than willing to help those in their communities who were more in need. By training these and new volunteers, ENPHO was able to reach many more communities in the immediate aftermath of the disaster; together they reached 11 of the 14 earthquake-affected districts. The collaboration between ENPHO and CAWST in developing the training materials was a highly collaborative and iterative process, which enabled the training materials to be developed within a short response time. By training volunteers on basic WASH topics during both the immediate response and the recovery phase, ENPHO and CAWST have been able to link immediate emergency relief to long-term developmental goals. While the recovery phase training continues in Nepal, CAWST is planning to decontextualize the training used in both phases so that it can be applied to other emergency situations in the future. The training materials will become part of the open content materials available on CAWST’s WASH Resources website.

Keywords: Water and sanitation, emergency response, education and training, building resilience.

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Authors: Yu-Mi Song, Sung-Ah Kim, Dongyoun Shin

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Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.

Keywords: Big data, correlation analysis, data recommendation system, urban data network.

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

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With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: Data integration, disease-related malnutrition, expert systems, mobile health.

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Authors: Geeta Sikka, Arvinder Kaur Takkar, Moin Uddin

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Missing data is a persistent problem in almost all areas of empirical research. The missing data must be treated very carefully, as data plays a fundamental role in every analysis. Improper treatment can distort the analysis or generate biased results. In this paper, we compare and contrast various imputation techniques on missing data sets and make an empirical evaluation of these methods so as to construct quality software models. Our empirical study is based on NASA-s two public dataset. KC4 and KC1. The actual data sets of 125 cases and 2107 cases respectively, without any missing values were considered. The data set is used to create Missing at Random (MAR) data Listwise Deletion(LD), Mean Substitution(MS), Interpolation, Regression with an error term and Expectation-Maximization (EM) approaches were used to compare the effects of the various techniques.

Keywords: Missing data, Imputation, Missing Data Techniques.

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Authors: George E. Tsekouras, Evi Sampanikou

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We compare three categorical data clustering algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art-s evolution. To establish this, we use a categorical data set and we study it by employing three categorical data clustering algorithms. The performances of these algorithms are compared each other, while interpretations of the clustering results are also given.

Keywords: Aesthetic judgment, comics artists, cluster analysis, categorical data.

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Authors: Femi Elegbeleye, Seani Rananga

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway, and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. Several results obtained from this study on data privacy models show that when two or more data privacy models are integrated via a fog storage gateway, we often have more secure data. Our main focus in the study is to design a framework for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, including its structure, and its interrelationships.

Keywords: IoT, fog storage, cloud storage, data analysis, data privacy.

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Authors: Raouf Mbarki, Fadi Al Khatib, Malek Adouni

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Knee collateral ligaments play a significant role in restraining excessive frontal motion (varus/valgus rotations). In this investigation, a multiscale frame was developed based on structural hierarchies of the collateral ligaments starting from the bottom (tropocollagen molecule) to up where the fibred reinforced structure established. Experimental data of failure tensile test were considered as the principal driver of the developed model. This model was calibrated statistically using Bayesian calibration due to the high number of unknown parameters. Then the model is scaled up to fit the real structure of the collateral ligaments and simulated under realistic boundary conditions. Predications have been successful in describing the observed transient response of the collateral ligaments during tensile test under pre- and post-damage loading conditions. Collateral ligaments maximum stresses and strengths were observed near to the femoral insertions, a results that is in good agreement with experimental investigations. Also for the first time, damage initiation and propagation were documented with this model as a function of the cross-link density between tropocollagen molecules.

Keywords: Multiscale model, tropocollagen, fibrils, ligaments.

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Authors: Mutuku Muvengei, John Kihiu

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This paper focuses on the development of bond graph dynamic model of the mechanical dynamics of an excavating mechanism previously designed to be used with small tractors, which are fabricated in the Engineering Workshops of Jomo Kenyatta University of Agriculture and Technology. To develop a mechanical dynamics model of the manipulator, forward recursive equations similar to those applied in iterative Newton-Euler method were used to obtain kinematic relationships between the time rates of joint variables and the generalized cartesian velocities for the centroids of the links. Representing the obtained kinematic relationships in bondgraphic form, while considering the link weights and momenta as the elements led to a detailed bond graph model of the manipulator. The bond graph method was found to reduce significantly the number of recursive computations performed on a 3 DOF manipulator for a mechanical dynamic model to result, hence indicating that bond graph method is more computationally efficient than the Newton-Euler method in developing dynamic models of 3 DOF planar manipulators. The model was verified by comparing the joint torque expressions of a two link planar manipulator to those obtained using Newton- Euler and Lagrangian methods as analyzed in robotic textbooks. The expressions were found to agree indicating that the model captures the aspects of rigid body dynamics of the manipulator. Based on the model developed, actuator sizing and valve sizing methodologies were developed and used to obtain the optimal sizes of the pistons and spool valve ports respectively. It was found that using the pump with the sized flow rate capacity, the engine of the tractor is able to power the excavating mechanism in digging a sandy-loom soil.

Keywords: Actuators, bond graphs, inverse dynamics, recursive equations, quintic polynomial trajectory.

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Authors: Amal M. Alrayes

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Data and system quality play a central role in organizational success, and the quality of any existing information system has a major influence on the effectiveness of overall system performance. Given the importance of system and data quality to an organization, it is relevant to highlight their importance on organizational performance in the Kingdom of Bahrain. This research aims to discover whether system quality and data quality are related, and to study the impact of system and data quality on organizational success. A theoretical model based on previous research is used to show the relationship between data and system quality, and organizational impact. We hypothesize, first, that system quality is positively associated with organizational impact, secondly that system quality is positively associated with data quality, and finally that data quality is positively associated with organizational impact. A questionnaire was conducted among public and private organizations in the Kingdom of Bahrain. The results show that there is a strong association between data and system quality, that affects organizational success.

Keywords: Data quality, performance, system quality.

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Authors: Baha Hasan, Jan Wikander

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This paper describes a part of the integrating work between assembly design and assembly process planning domains (APP). The work is based, in its first stage, on modelling assembly features to support APP. A multi-layer architecture, based on feature-based modelling, is proposed to establish a dynamic and adaptable link between product design using CAD tools and APP. The proposed approach is based on deriving “specific function” features from the “generic” assembly and form features extracted from the CAD tools. A hierarchal structure from “generic” to “specific” and from “high level geometrical entities” to “low level geometrical entities” is proposed in order to integrate geometrical and assembly data extracted from geometrical and assembly modelers to the required processes and resources in APP. The feature concept, feature-based modelling, and feature recognition techniques are reviewed.

Keywords: Assembly feature, assembly process planning, feature, feature-based modelling, form feature, ontology.

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Authors: K. Jitkue, W. Srisang, C. Yaiprasert, K. Jaroensutasinee, M. Jaroensutasinee

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This study aims at using multi-source data to monitor coral biodiversity and coral bleaching. We used coral reef at Racha Islands, Phuket as a study area. There were three sources of data: coral diversity, sensor based data and satellite data.

Keywords: Coral reefs, Remote sensing, Sea surfacetemperatue, Satellite imagery.

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Authors: Sagaren Pillay

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In this paper bi-annual time series data on unemployment rates (from the Labour Force Survey) are expanded to quarterly rates and linked to quarterly unemployment rates (from the Quarterly Labour Force Survey). The resultant linked series and the consumer price index (CPI) series are examined using Johansen’s cointegration approach and vector error correction modeling. The study finds that both the series are integrated of order one and are cointegrated. A statistically significant co-integrating relationship is found to exist between the time series of unemployment rates and the CPI. Given this significant relationship, the study models this relationship using Vector Error Correction Models (VECM), one with a restriction on the deterministic term and the other with no restriction.

A formal statistical confirmation of the existence of a unique linear and lagged relationship between inflation and unemployment for the period between September 2000 and June 2011 is presented. For the given period, the CPI was found to be an unbiased predictor of the unemployment rate. This relationship can be explored further for the development of appropriate forecasting models incorporating other study variables.

Keywords: Forecasting, lagged, linear, relationship.

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Authors: Yang Bao, LuJing Zhang

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Typical Intelligent Decision Support System is 4-based, its design composes of Data Warehouse, Online Analytical Processing, Data Mining and Decision Supporting based on models, which is called Decision Support System Based on Data Warehouse (DSSBDW). This way takes ETL,OLAP and DM as its implementing means, and integrates traditional model-driving DSS and data-driving DSS into a whole. For this kind of problem, this paper analyzes the DSSBDW architecture and DW model, and discusses the following key issues: ETL designing and Realization; metadata managing technology using XML; SQL implementing, optimizing performance, data mapping in OLAP; lastly, it illustrates the designing principle and method of DW in DSSBDW.

Keywords: Decision Support System, Data Warehouse, Data Mining.

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Authors: Songdi Li, Louise Spry, Tony Woodall

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Nowadays, Corporate Social responsibility (CSR) is becoming a buzz word, and more and more academics are putting efforts on CSR studies. It is believed that CSR could influence Corporate Reputation (CR), and they hold a favourable view that CSR leads to a positive CR. To be specific, the CSR related activities in the reputational context have been regarded as ways that associate to excellent financial performance, value creation, etc. Also, it is argued that CSR and CR are two sides of one coin; hence, to some extent, doing CSR is equal to establishing a good reputation. Still, there is no consensus of the CSR-CR relationship in the literature; thus, a systematic literature review is highly in need. This research conducts a systematic literature review with both bibliometric and content analysis. Data are selected from English language sources, and academic journal articles only, then, keyword combinations are applied to identify relevant sources. Data from Scopus and WoS are gathered for bibliometric analysis. Scopus search results were saved in RIS and CSV formats, and Web of Science (WoS) data were saved in TXT format and CSV formats in order to process data in the Bibexcel software for further analysis which later will be visualised by the software VOSviewer. Also, content analysis was applied to analyse the data clusters and the key articles. In terms of the topic of CSR-CR, this literature review with bibliometric analysis has made four achievements. First, this paper has developed a systematic study which quantitatively depicts the knowledge structure of CSR and CR by identifying terms closely related to CSR-CR (such as ‘corporate governance’) and clustering subtopics emerged in co-citation analysis. Second, content analysis is performed to acquire insight on the findings of bibliometric analysis in the discussion section. And it highlights some insightful implications for the future research agenda, for example, a psychological link between CSR-CR is identified from the result; also, emerging economies and qualitative research methods are new elements emerged in the CSR-CR big picture. Third, a multidisciplinary perspective presents through the whole bibliometric analysis mapping and co-word and co-citation analysis; hence, this work builds a structure of interdisciplinary perspective which potentially leads to an integrated conceptual framework in the future. Finally, Scopus and WoS are compared and contrasted in this paper; as a result, Scopus which has more depth and comprehensive data is suggested as a tool for future bibliometric analysis studies. Overall, this paper has fulfilled its initial purposes and contributed to the literature. To the author’s best knowledge, this paper conducted the first literature review of CSR-CR researches that applied both bibliometric analysis and content analysis; therefore, this paper achieves its methodological originality. And this dual approach brings advantages of carrying out a comprehensive and semantic exploration in the area of CSR-CR in a scientific and realistic method. Admittedly, its work might exist subjective bias in terms of search terms selection and paper selection; hence triangulation could reduce the subjective bias to some degree.

Keywords: Corporate social responsibility, corporate reputation, bibliometric analysis, software data analysis.

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Authors: Diganta Kumar Pathak, Manashjyoti Bhuyan, Vaskar Deka

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Vertical Handover(VHO) among different communication technologies ensuring uninterruption and service continuity is one of the most important performance parameter in Heterogenous networks environment. In an integrated Universal Mobile Telecommunicatin System(UMTS) and Wireless Local Area Network(WLAN), WLAN is given an inherent priority over UMTS because of its high data rates with low cost. Therefore mobile users want to be associated with WLAN maximum of the time while roaming, to enjoy best possible services with low cost. That encourages reduction of number of VHO. In this work the reduction of number of VHO with respect to varying number of WLAN Access Points(APs) in an integrated UMTS and WLAN network is investigated through simulation to provide best possible cost effective service to the users. The simulation has been carried out for an area (7800 × 9006)m2 where COST-231 Hata model and 3GPP (TR 101 112 V 3.1.0) specified models are used for WLAN and UMTS path loss models respectively. The handover decision is triggered based on the received signal level as compared to the fade margin. Fade margin gives a probabilistic measure of the reliability of the communication link. A relationship between number of WLAN APs and the number of VHO is also established in this work.

Keywords: VHO, UMTS, WLAN, MT, AP, BS.

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Authors: Hossein Morshedlou, Ahmad Abdollahzade Barforoush

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Recent developments in storage technology and networking architectures have made it possible for broad areas of applications to rely on data streams for quick response and accurate decision making. Data streams are generated from events of real world so existence of associations, which are among the occurrence of these events in real world, among concepts of data streams is logical. Extraction of these hidden associations can be useful for prediction of subsequent concepts in concept shifting data streams. In this paper we present a new method for learning association among concepts of data stream and prediction of what the next concept will be. Knowing the next concept, an informed update of data model will be possible. The results of conducted experiments show that the proposed method is proper for classification of concept shifting data streams.

Keywords: Data Stream, Classification, Concept Shift, History.

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: Text mining, topic extraction, independent, incremental, independent component analysis.

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Authors: Duangkamol Thitivesa, Abigail Melad Essien

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This paper explores the use of project work in a content-based instruction in a Rajabhat University, a teacher college, where student teachers are instructed to perform teaching roles mainly in basic education level. Its aim is to link theory to practice, and to help language teachers maximize the full potential of project work for genuine communication and give real meaning to writing activity. Two research questions are formulated to guide this study: a) What is the academic achievement of the students- writing skill against the 70% attainment target after the use of project to enhance the skill? and b) To what degree is the development of the students- writing skills during the course of project to enhance the skill? The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of achievement test, student writing works, and project diary. The scores in the summative achievement test were analyzed by mean score, standard deviation, and t-test. Project diary serves as students- record of the language acquired during the project. List of structures and vocabulary noted in the diary has shown students- ability to attend to, recognize, and focus on meaningful patterns of language forms.

Keywords: EFL classroom, Project-Based Learning, project work, writing skill.

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Authors: H. Baazaoui Zghal, S. Faiz, H. Ben Ghezala

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Data mining is an extraordinarily demanding field referring to extraction of implicit knowledge and relationships, which are not explicitly stored in databases. A wide variety of methods of data mining have been introduced (classification, characterization, generalization...). Each one of these methods includes more than algorithm. A system of data mining implies different user categories,, which mean that the user-s behavior must be a component of the system. The problem at this level is to know which algorithm of which method to employ for an exploratory end, which one for a decisional end, and how can they collaborate and communicate. Agent paradigm presents a new way of conception and realizing of data mining system. The purpose is to combine different algorithms of data mining to prepare elements for decision-makers, benefiting from the possibilities offered by the multi-agent systems. In this paper the agent framework for data mining is introduced, and its overall architecture and functionality are presented. The validation is made on spatial data. Principal results will be presented.

Keywords: Databases, data mining, multi-agent, spatial datamart.

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Authors: Samra Bajwa, Muhammad W. Siddiqi

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The study investigates the causal link between trade openness and economic growth for four South Asian countries for period 1972-1985 and 1986-2007 to examine the scenario before and after the implementation of SAARC. Panel cointegration and FMOLS techniques are employed for short run and long run estimates. In 1972-85 short run unidirectional causality from GDP to openness is found whereas, in 1986-2007 there exists bi-directional causality between GDP and openness. The long run elasticity magnitude between GDP and openness contains negative sign in 1972-85 which shows that there exists long run negative relationship. While in time period 1986-2007 the elasticity magnitude has positive sign that indicates positive causation between GDP and openness. So it can be concluded that after the implementation of SAARC overall situation of selected countries got better. Also long run coefficient of error term suggests that short term equilibrium adjustments are driven by adjustment back to long run equilibrium.

Keywords: Causality, Economic Growth, Panel Co-integration, SAARC, Trade Openness.

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Authors: Jian-hua Yeh, Shi-yi Kuo

Abstract:

This paper discusses the classification process for medical data. In this paper, we use the data from ACM KDDCup 2008 to demonstrate our classification process based on latent topic discovery. In this data set, the target set and outliers are quite different in their nature: target set is only 0.6% size in total, while the outliers consist of 99.4% of the data set. We use this data set as an example to show how we dealt with this extremely biased data set with latent topic discovery and noise reduction techniques. Our experiment faces two major challenge: (1) extremely distributed outliers, and (2) positive samples are far smaller than negative ones. We try to propose a suitable process flow to deal with these issues and get a best AUC result of 0.98.

Keywords: classification, latent topics, outlier adjustment, feature scaling

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Authors: Nouha Mhimdi, Wahiba Ben Abdessalem Karaa, Henda Ben Ghezala

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Many methods are used to collect data like questionnaires, surveys, focus group interviews. Or the collection of poor-quality data resulting, for example, from poorly designed questionnaires, the absence of good translators or interpreters, and the incorrect recording of data allow conclusions to be drawn that are not supported by the data or to focus only on the average effect of the program or policy. There are several solutions to avoid or minimize the most frequent errors, including obtaining expert advice on the design or adaptation of data collection instruments; or use technologies allowing better "anonymity" in the responses. In this context, and to overcome the aforementioned problems, we suggest in this paper an approach to achieve the collection of relevant data, by carrying out a large-scale questionnaire-based survey. We have been able to collect good quality, consistent and practical data on hospital emergencies to improve emergency services in hospitals, especially in the case of epidemics or pandemics.

Keywords: Data collection, survey, database, data analysis, hospital emergencies.

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Authors: J. D. D. Daniel, K. N. Goh, S. M. Yusop

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Trends in business intelligence, e-commerce and remote access make it necessary and practical to store data in different ways on multiple systems with different operating systems. As business evolve and grow, they require efficient computerized solution to perform data update and to access data from diverse enterprise business applications. The objective of this paper is to demonstrate the capability of DTS [1] as a database solution for automatic data transfer and update in solving business problem. This DTS package is developed for the sales of variety of plants and eventually expanded into commercial supply and landscaping business. Dimension data modeling is used in DTS package to extract, transform and load data from heterogeneous database systems such as MySQL, Microsoft Access and Oracle that consolidates into a Data Mart residing in SQL Server. Hence, the data transfer from various databases is scheduled to run automatically every quarter of the year to review the efficient sales analysis. Therefore, DTS is absolutely an attractive solution for automatic data transfer and update which meeting today-s business needs.

Keywords: Data Transformation Services (DTS), ObjectLinking and Embedding Database (OLEDB), Data Mart, OnlineAnalytical Processing (OLAP), Online Transactional Processing(OLTP).

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Authors: P. S. Hiremath, Siddu P. Algur

Abstract:

With the explosive growth of information sources available on the World Wide Web, it has become increasingly difficult to identify the relevant pieces of information, since web pages are often cluttered with irrelevant content like advertisements, navigation-panels, copyright notices etc., surrounding the main content of the web page. Hence, tools for the mining of data regions, data records and data items need to be developed in order to provide value-added services. Currently available automatic techniques to mine data regions from web pages are still unsatisfactory because of their poor performance and tag-dependence. In this paper a novel method to extract data items from the web pages automatically is proposed. It comprises of two steps: (1) Identification and Extraction of the data regions based on visual clues information. (2) Identification of data records and extraction of data items from a data region. For step1, a novel and more effective method is proposed based on visual clues, which finds the data regions formed by all types of tags using visual clues. For step2 a more effective method namely, Extraction of Data Items from web Pages (EDIP), is adopted to mine data items. The EDIP technique is a list-based approach in which the list is a linear data structure. The proposed technique is able to mine the non-contiguous data records and can correctly identify data regions, irrespective of the type of tag in which it is bound. Our experimental results show that the proposed technique performs better than the existing techniques.

Keywords: Web data records, web data regions, web mining.

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