Search results for: Online data updates

Authors: T. Teo, S. W. Lye, Y. F. Li, Z. Zakaria

Abstract:

Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is therefore expected to increase. Despite the growing number of EEG research into brain functioning activities of a person, key challenges remain in adopting EEG for real-time operations. These include limited portability, long preparation time, high number of channel dimensionality, intrusiveness, as well as level of accuracy in acquiring neurological data. This paper proposes an approach using a 4-6 EEG channels to determine the cognitive states of a subject when undertaking a set of passive and active monitoring tasks of a subject. Air traffic controller (ATC) dynamic-tasks are used as a proxy. The work found that using a developed channel reduction and identifier algorithm, good trend adherence of 89.1% can be obtained between a commercially available brain computer interface (BCI) 14 channel Emotiv EPOC+ EEG headset and that of a carefully selected set of reduced 4-6 channels. The approach can also identify different levels of engagement activities ranging from general monitoring, ad hoc and repeated active monitoring activities involving information search, extraction, and memory activities.

Keywords: Neurophysiology, monitoring, EEG, outliers, electroencephalography.

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Authors: Wei Lifan, Zhang Huaiyu, Yang Yunbin, Li Jia

Abstract:

Through the analysis of the process digital design based on digital mockup, the fact indicates that a distributed cooperative supporting environment is the foundation conditions to adopt design approach based on DMU. Data access authorization is concerned firstly because the value and sensitivity of the data for the enterprise. The access control for administrators is often rather weak other than business user. So authors established an enhanced system to avoid the administrators accessing the engineering data by potential approach and without authorization. Thus the data security is improved.

Keywords: access control, DMU, PLM, virtual prototype.

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

Abstract:

Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: Die-Map Clustering, Feature Extraction, Pattern Recognition, Semiconductor Manufacturing Process.

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Authors: Ashish Dhamaniya, Satish Chandra

Abstract:

Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85^th and 50^th percentile speed to the difference in 50^th and 15^th percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also.

Keywords: Normal distribution, percentile speed, speed spread ratio, traffic volume.

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Authors: Amel Abdoullah Ahmed Dghais, Mohd Tahir Ismail

Abstract:

In this paper the core objective is to apply discrete wavelet transform and maximal overlap discrete wavelet transform functions namely Haar, Daubechies2, Symmlet4, Coiflet2 and discrete approximation of the Meyer wavelets in non stationary financial time series data from Dow Jones index (DJIA30) of US stock market. The data consists of 2048 daily data of closing index from December 17, 2004 to October 23, 2012. Unit root test affirms that the data is non stationary in the level. A comparison between the results to transform non stationary data to stationary data using aforesaid transforms is given which clearly shows that the decomposition stock market index by discrete wavelet transform is better than maximal overlap discrete wavelet transform for original data.

Keywords: Discrete wavelet transform, maximal overlap discrete wavelet transform, stationarity, autocorrelation function.

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Authors: K. Chinda, P. Luangpaiboon

Abstract:

This paper presents the comparative study of coded data methods for finding the benefit of concealing the natural data which is the mercantile secret. Influential parameters of the number of replicates (rep), treatment effects (τ) and standard deviation (σ) against the efficiency of each transformation method are investigated. The experimental data are generated via computer simulations under the specified condition of the process with the completely randomized design (CRD). Three ways of data transformation consist of Box-Cox, arcsine and logit methods. The difference values of F statistic between coded data and natural data (Fc-Fn) and hypothesis testing results were determined. The experimental results indicate that the Box-Cox results are significantly different from natural data in cases of smaller levels of replicates and seem to be improper when the parameter of minus lambda has been assigned. On the other hand, arcsine and logit transformations are more robust and obviously, provide more precise numerical results. In addition, the alternate ways to select the lambda in the power transformation are also offered to achieve much more appropriate outcomes.

Keywords: Experimental Designs, Box-Cox, Arcsine, Logit Transformations.

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Authors: Barış Can Yalçın

Abstract:

Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor.

Keywords: Calibration of sensors, data acquisition.

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Authors: Manpreet Singh, Parvinder Singh, Suman

Abstract:

The data is available in abundance in any business organization. It includes the records for finance, maintenance, inventory, progress reports etc. As the time progresses, the data keep on accumulating and the challenge is to extract the information from this data bank. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of business. For the development of accurate and required information for particular problem, business analyst needs to develop multidimensional models which give the reliable information so that they can take right decision for particular problem. If the multidimensional model does not possess the advance features, the accuracy cannot be expected. The present work involves the development of a Multidimensional data model incorporating advance features. The criterion of computation is based on the data precision and to include slowly change time dimension. The final results are displayed in graphical form.

Keywords: Multidimensional, data precision.

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Authors: Sanjeev Gupta, Mayank Dave

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The issue of real-time and reliable report delivery is extremely important for taking effective decision in a real world mission critical Wireless Sensor Network (WSN) based application. The sensor data behaves differently in many ways from the data in traditional databases. WSNs need a mechanism to register, process queries, and disseminate data. In this paper we propose an architectural framework for data placement and management. We propose a reliable and real time approach for data placement and achieving data integrity using self organized sensor clusters. Instead of storing information in individual cluster heads as suggested in some protocols, in our architecture we suggest storing of information of all clusters within a cell in the corresponding base station. For data dissemination and action in the wireless sensor network we propose to use Action and Relay Stations (ARS). To reduce average energy dissipation of sensor nodes, the data is sent to the nearest ARS rather than base station. We have designed our architecture in such a way so as to achieve greater energy savings, enhanced availability and reliability.

Keywords: Cluster head, data reliability, real time communication, wireless sensor networks.

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: A classifier, Algorithms decision tree, knowledge extraction, Support Vector Machine.

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Authors: C. Ardil

Abstract:

In this paper, we introduce an e-collaborative learning circles methodology which utilizes the information and communication technologies (ICTs) in e-educational processes. In e-collaborative learning circles methodology, the teachers and students announce their research projects on various mailing lists and discussion boards using available ICTs. The teachers & moderators and students who are already members of the e-forums, discuss the project proposals in their classrooms sent out by the potential global partner schools and return the requested feed back to the proposing school(s) about their level of the participation and contribution in the research. In general, an e-collaborative learning circle project is implemented with a small and diverse group (usually 8-10 participants) from around the world. The students meet regularly over a period of weeks/months through the ICTs during the ecollaborative learning process. When the project is completed, a project product (e-book / DVD) is prepared and sent to the circle members. In this research, when taking into account the interests and motivation of the participating students with the facilitating role of the teacher(s), the students in each circle do research to obtain new data and information, thus enabling them to have the opportunity to meet both different cultures and international understandings across the globe. However, while the participants communicate along with the members in the circle they also practice and develop their communication language skills. Finally, teachers and students find the possibility to develop their skills in using the ICTs as well.

Keywords: Distance Education, Online Learning, Web BasedLearning, Learning Circles, e-Collaborative Learning Circles

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Authors: Mohd. Noor Md. Sap, A. Majid Awan

Abstract:

Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents work on developing a software system for predicting crop yield, for example oil-palm yield, from climate and plantation data. At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering the data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.

Keywords: Pattern analysis, clustering, kernel methods, spatial data, crop yield

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Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

Abstract:

Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City.

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Authors: Manal Sultan Alhussein, Xiang Michelle Liu

Abstract:

Telehealth is an advanced technology using digital information and telecommunication facilities that provide access to health services from a distance. It slows the transmission factor of COVID-19, especially for elderly patients and patients with chronic diseases during the pandemic. Therefore, understanding patient perspectives on telehealth services and the factors impacting their option of telehealth service will shed light on the measures that healthcare providers can take to improve the quality of telehealth services. This study aimed to evaluate perceptions of telehealth services among different patient groups and explore various aspects of telehealth utilization in the United States during the COVID-19 pandemic. An online survey distributed via social media platforms was used to collect research data. In addition to the descriptive statistics, both correlation and regression analyses were conducted to test research hypotheses. The empirical results highlighted that the factors such as accessibility to telehealth services and the type of specialty clinics that the patients required play important roles in the effectiveness of telehealth services they received. However, the results found that patients’ waiting time to receive telehealth services and their annual income did not significantly influence their desire to select receiving healthcare services via telehealth. The limitations of the study and future research directions are discussed.

Keywords: Telehealth, patient satisfaction, pandemic, healthcare, remote patient monitor.

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

Abstract:

The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: Data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse.

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Authors: M. Kargar, F. Gharbalchi

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In this paper a new method is suggested for distributed data-mining by the probability patterns. These patterns use decision trees and decision graphs. The patterns are cared to be valid, novel, useful, and understandable. Considering a set of functions, the system reaches to a good pattern or better objectives. By using the suggested method we will be able to extract the useful information from massive and multi-relational data bases.

Keywords: Data-mining, Decision tree, Decision graph, Pattern, Relationship.

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Authors: A. Karaali

Abstract:

This paper will seek to clarify important key terms such as home schooling and home education as well as the legalities attached to such terms. It will reflect on the recent proposed changes to terminology in NSW, Australia. The various pedagogical approaches to home education will be explored including their prominence in the Australian context. There is a strong focus on literature from Australia. The historical background of home education in Australia will be explained as well as the difference between distance education and home education. The future of home education in Australia will be discussed.

Keywords: Alternative education, e-learning, home education, home schooling, online resources, technology.

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Authors: A. M. Fahim, G. Saake, A. M. Salem, F. A. Torkey, M. A. Ramadan

Abstract:

Data clustering is an important data exploration technique with many applications in data mining. The k-means algorithm is well known for its efficiency in clustering large data sets. However, this algorithm is suitable for spherical shaped clusters of similar sizes and densities. The quality of the resulting clusters decreases when the data set contains spherical shaped with large variance in sizes. In this paper, we introduce a competent procedure to overcome this problem. The proposed method is based on shifting the center of the large cluster toward the small cluster, and recomputing the membership of small cluster points, the experimental results reveal that the proposed algorithm produces satisfactory results.

Keywords: K-Means, Data Clustering, Cluster Analysis.

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: Compression properties, uncertainty, uncertain time series, mining technique, weather prediction.

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Authors: Zhenkun Wang, Simon S. Gao

Abstract:

Using a scoring system, this paper provides a comparative assessment of the quality of data between XBRL formatted financial reports and non-XBRL financial reports. It shows a major improvement in the quality of data of XBRL formatted financial reports. Although XBRL formatted financial reports do not show much advantage in the quality at the beginning, XBRL financial reports lately display a large improvement in the quality of data in almost all aspects. With the improved XBRL web data managing, presentation and analysis applications, XBRL formatted financial reports have a much better accessibility, are more accurate and better in timeliness.

Keywords: Data Quality; Financial Report; Information; XBRL

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Authors: A. Habibizad Navin, M. Naghian Fesharaki, M. Mirnia, M. Kargar

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In this paper we introduce Digital Probability Hyper Digraph for modeling random variable as the hierarchical data-oriented model.

Keywords: Data-Oriented Models, Data Structure, DigitalProbability Hyper Digraph, Random Variable, Statistic andProbability.

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Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

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This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: Big data, cooperative jamming, energy balance, physical layer, two-hop transmission, wireless security.

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Authors: Yusof. Mohd Kamir, Mat Deris. Mohd Sufian, Abidin. Ahmad Faisal Amri

Abstract:

The purpose of this paper is to show efficiency and capability LZWµ in data compression. The LZWµ technique is enhancement from existing LZW technique. The modification the existing LZW is needed to produce LZWµ technique. LZW read one by one character at one time. Differ with LZWµ technique, where the LZWµ read three characters at one time. This paper focuses on data compression and tested efficiency and capability LZWµ by different data format such as doc type, pdf type and text type. Several experiments have been done by different types of data format. The results shows LZWµ technique is better compared to existing LZW technique in term of file size.

Keywords: Data Compression, Huffman Encoding, LZW, LZWµ, RLL, Size.

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Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang

Abstract:

Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.

Keywords: Hit rate, Locality of program, Stack cache, and Stack data.

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Authors: Pradeep Singh, Shrish Verma

Abstract:

Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. Earlier we predicted the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven datasets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: Software Metrics, Fault prediction, Cross project, Within project.

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Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph

Abstract:

In this paper, temperature extremes are forecast by employing the block maxima method of the Generalized extreme value(GEV) distribution to analyse temperature data from the Cameroon Development Corporation (C.D.C). By considering two sets of data (Raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data while in the simulated data, the return values show an increasing trend but with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend but with an upper bound. This clearly shows that temperatures in the tropics even-though show a sign of increasing in the future, there is a maximum temperature at which there is no exceedence. The results of this paper are very vital in Agricultural and Environmental research.

Keywords: Return level, Generalized extreme value (GEV), Meteorology, Forecasting.

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Authors: Xu Du, Aparna S. Varde

Abstract:

In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: Data Mining, Environmental Modeling, Sustainability, Urban Planning.

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Authors: Minsoo Lee, Yun-mi Kim, Yearn Jeong Kim, Yoon-kyung Lee, Hyejung Yoon

Abstract:

Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.

Keywords: Ant colony system, biological data, clustering, DNA chip.

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Authors: Gabriela Lindemann, Danilo Schmidt, Thomas Schrader, Dietmar Keune

Abstract:

The amount and heterogeneity of data in biomedical research, notably in interdisciplinary fields, requires new methods for the collection, presentation and analysis of information. Important data from laboratory experiments as well as patient trials are available but come out of distributed resources. The Charité - University Hospital Berlin has established together with the German Research Foundation (DFG) a new information service centre for kidney diseases and transplantation (Open European Nephrology Science Centre - OpEN.SC). Beside a collaborative aspect to create new research groups every single partner or institution of this science information centre making his own data available is allowed to search the whole data pool of the various involved centres. A core task is the implementation of a non-restricting open data structure for the various different data sources. We decided to use a modern RDF model and in a first phase transformed original data coming from the web-based Electronic Patient Record database TBase©.

Keywords: Medical databases, Resource Description Framework (RDF), metadata repository.

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Authors: Hongzhi Wang, Jianzhong Li, Hong Gao

Abstract:

XML is an important standard of data exchange and representation. As a mature database system, using relational database to support XML data may bring some advantages. But storing XML in relational database has obvious redundancy that wastes disk space, bandwidth and disk I/O when querying XML data. For the efficiency of storage and query XML, it is necessary to use compressed XML data in relational database. In this paper, a compressed relational database technology supporting XML data is presented. Original relational storage structure is adaptive to XPath query process. The compression method keeps this feature. Besides traditional relational database techniques, additional query process technologies on compressed relations and for special structure for XML are presented. In this paper, technologies for XQuery process in compressed relational database are presented..

Keywords: XML, compression, query processing

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