Search results for: data infrastructure

Authors: Zeynep Karacor, Rahime Hulya Ozturk

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In recent years, the rise of population density and the problem of aging population took attention to the health expenditures. In Turkey, some regulations and infrastructure changes in health sector have occurred. These changes are called Health Transformation Program. The productivity of health services, patient satisfaction, quality of services are tried to be improved with this program. Some radical changes are applied in Turkish economy in this context. The aim of this paper is to present the effects of Health Transformation Program on health expenditures. In the first part of the paper, some information’s about health system and applications in Turkey are discussed. In the second part, the aims of Health Transformation Program are explained. And in the third part the effects of Health Transformation Program on health expenditures are examined.

Keywords: health transformation program, Turkey, health services, health expenditures

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Authors: Chaitanya Varma, Arpan Mehar

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The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways.

Keywords: highway, mixed traffic flow, modeling, operating speed

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Authors: Nadjah Chergui, Narhimene Boustia

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Intrusion Detection Systems are an essential tool for network security infrastructure. However, IDSs have a serious problem which is the generating of massive number of alerts, most of them are false positive ones which can hide true alerts and make the analyst confused to analyze the right alerts for report the true attacks. The purpose behind this paper is to present a formalism model to perform correlation engine by the reduction of false positive alerts basing on vulnerability contextual information. For that, we propose a formalism model based on non-monotonic JClassicδє description logic augmented with a default (δ) and an exception (є) operator that allows a dynamic inference according to contextual information.

Keywords: context, default, exception, vulnerability

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Authors: Olalekan Adekola, Margaret Gatonye, Paul Orina

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The growing demand for farmed fish by underdeveloped and developing countries as a means of contributing positively towards eradication of hunger, food insecurity, and malnutrition for their fast growing populations has implications to the environment. Likewise, climate change poses both an immediate and future threat to local fish production with capture fisheries already experiencing a global decline. This not only raises fundamental questions concerning how aquaculture practices affect the environment, but also how ready are aquaculture farmers to adapt to climate related hazards. This paper assesses existing aquaculture practices and approaches to adapting to climate hazards in Kenya, where aquaculture has grown rapidly since the year 2009. The growth has seen rise in aquaculture set ups mainly along rivers and streams, importation of seed and feed and intensification with possible environmental implications. The aquaculture value chain in the context of climate change and their implication for practice is further investigated, and the strategies necessary for an improved implementation of resilient aquaculture system in Kenya is examined. Data for the study are collected from interviews, questionnaires, two workshops and document analysis. Despite acclaimed nutritional benefit of fish consumption in Kenya, poor management of effluents enriched with nitrogen, phosphorus, organic matter, and suspended solids has implications not just on the ecosystem, goods, and services, but is also potential source of resource-use conflicts especially in downstream communities and operators in the livestock, horticulture, and industrial sectors. The study concluded that aquaculture focuses on future orientation, climate resilient infrastructure, appropriate site selection and invest on biosafety as the key sustainable strategies against climate hazards.

Keywords: aquaculture, resilience, environment, strategies, Kenya

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Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

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Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing

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Authors: Ade Dwi Utami

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Pedagogic competence is the basic competence of teachers to perform their tasks as educators. The ability to assess has become one of the demands in teachers pedagogic competence. Teachers ability to assess is related to curriculum instructions and applications. This research is aimed at obtaining data concerning teachers ability to assess that comprises of understanding assessment, determining assessment type, tools and procedure, conducting assessment process, and using assessment result information. It uses mixed method of explanatory technique in which qualitative data is used to verify the quantitative data obtained through a survey. The technique of quantitative data collection is by test whereas the qualitative data collection is by observation, interview and documentation. Then, the analyzed data is processed through a proportion study technique to be categorized into high, medium and low. The result of the research shows that teachers ability to assess can be grouped into 3 namely, 2% of high, 4% of medium and 94% of low. The data shows that teachers ability to assess is still relatively low. Teachers are lack of knowledge and comprehension in assessment application. The statement is verified by the qualitative data showing that teachers did not state which aspect was assessed in learning, record children’s behavior, and use the data result as a consideration to design a program. Teachers have assessment documents yet they only serve as means of completing teachers administration for the certification program. Thus, assessment documents were not used with the basis of acquired knowledge. The condition should become a consideration of the education institution of educators and the government to improve teachers pedagogic competence, including the ability to assess.

Keywords: assessment, early childhood education, pedagogic competence, teachers

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Authors: Hossein Boromandfar, Ahmad Ghalavandi

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This paper examines the importance of human resources as a strategic resource and the factors that lead to increased Labor productivity in Applied Science Center Andimeshk pay. First, the concepts and definitions of productivity and factors affecting it, and then determine the center Recommendations for improving the productivity of the university at a high level its improvement. What leads to increased productivity of labor is worth. The most competent human resources infrastructure is set, because by moving towards the development and promotion. The use of qualified employees in the university with a focus on specific objectives can be effective on its promotion.

Keywords: productivity, manage, human resources, center for applied science

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Authors: Drazen Simlesa, Jelena Pudjak, Anita Tonkovic Busljeta

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Regarding social economy (SE), Croatia is, on general level, considered as ex-communist country with good tradition, bad performance in second part of 20th Century because of political control in the business sector, which has in transition period (1990-1999) became a problem of ignorance in public administration (policy level). Today, social economy in Croatia is trying to catch up with other EU states on all important levels of SE sector: legislative and institutional framework, financial infrastructure, education and capacity building, and visibility. All four are integral parts of Strategy for the Development of Social Entrepreneurship in the Republic of Croatia for the period of 2015 – 2020. Within iPRESENT project, funded by Croatian Science Foundation, we have mapped social economy actors and after many years there is a clear and up to date social economy base. At the ICSE 2016 we will present main outcomes and results of this process. In the second year of the project we conducted a field research across Croatia carried out 19 focus groups with most influential, innovative and inspirational social economy actors. We divided interview questions in four themes: laws on social economy and public policies, definition/ideology of social economy and cooperation on SE scene, the level of democracy and working conditions, motivation and existence of intrinsic values. The data that are gathered through focus group interviews has been analysed via qualitative data analysis software (Atlas ti.). Major finding that will be presented in ICSA 2016 are: Social economy actors are mostly unsatisfied with legislative and institutional framework in Croatia and consider it as unsupportive and confusing. Social economy actors consider SE to be in the line with WISE model and as a tool for community development. The SE actors that are more active express satisfaction with cooperation amongst SE actors and other partners and stakeholders, but the ones that are in more isolated conditions (spatially) express need for more cooperation and networking. Social economy actors expressed their praise for democratic atmosphere in their organisations and fair working conditions. And finally, they expressed high motivation to continue to work in the social economy and are dedicated to the concept, including even those that were at the beginning interested just in getting a quick job. It means that we can detect intrinsic values for employees in social economy organisations. This research enabled us to describe for the first time in Croatia the view from the inside, attitudes and opinion of employees of social economy organisations.

Keywords: employees, focus groups, mapping, social economy

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Authors: Y. N. Phang, E. F. Loh

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Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling over-dispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling over-dispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling over-dispersed medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling over-dispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian, and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling over-dispersed medical count data when ZIP and ZINB are inadequate.

Keywords: zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit

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Authors: Uzma Bashir, Jamaludin Md. Ali

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This study is concerned with the visualization of monotone data using a piece-wise C1 rational trigonometric interpolating scheme. Four positive shape parameters are incorporated in the structure of rational trigonometric spline. Conditions on two of these parameters are derived to attain the monotonicity of monotone data and other two are left-free. Figures are used widely to exhibit that the proposed scheme produces graphically smooth monotone curves.

Keywords: trigonometric splines, monotone data, shape preserving, C1 monotone interpolant

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Authors: Joshua Buli, David Pietrowski, Samuel Britton

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Processing SAR data usually requires constraints in extent in the Fourier domain as well as approximations and interpolations onto a planar surface to form an exploitable image. This results in a potential loss of data requires several interpolative techniques, and restricts visualization to two-dimensional plane imagery. The data can be interpolated into a ground plane projection, with or without terrain as a component, all to better view SAR data in an image domain comparable to what a human would view, to ease interpretation. An alternate but computationally heavy method to make use of more of the data is the basis of this research. Pre-processing of the SAR data is completed first (matched-filtering, motion compensation, etc.), the data is then range compressed, and lastly, the contribution from each pulse is determined for each specific point in space by searching the time history data for the reflectivity values for each pulse summed over the entire collection. This results in a per-3D-point reflectivity using the entire collection domain. New advances in GPU processing have finally allowed this rapid projection of acquired SAR data onto any desired reference surface (called backprojection). Mathematically, the computations are fast and easy to implement, despite limitations in SAR phase history data size and 3D-point cloud size. Backprojection processing algorithms are embarrassingly parallel since each 3D point in the scene has the same reflectivity calculation applied for all pulses, independent of all other 3D points and pulse data under consideration. Therefore, given the simplicity of the single backprojection calculation, the work can be spread across thousands of GPU threads allowing for accurate reflectivity representation of a scene. Furthermore, because reflectivity values are associated with individual three-dimensional points, a plane is no longer the sole permissible mapping base; a digital elevation model or even a cloud of points (collected from any sensor capable of measuring ground topography) can be used as a basis for the backprojection technique. This technique minimizes any interpolations and modifications of the raw data, maintaining maximum data integrity. This innovative processing will allow for SAR data to be rapidly brought into a common reference frame for immediate exploitation and data fusion with other three-dimensional data and representations.

Keywords: backprojection, data fusion, exploitation, three-dimensional, visualization

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Authors: Jean Christian Ralaivao, Fabrice Razafindraibe, Hasina Rakotonirainy

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This document constitutes a resumption of work carried out in the field of complex data warehouses (DW) relating to the management and formalization of knowledge and metadata. It offers a methodological approach for integrating two concepts, knowledge and metadata, within the framework of a complex DW architecture. The objective of the work considers the use of the technique of knowledge representation by description logics and the extension of Common Warehouse Metamodel (CWM) specifications. This will lead to a fallout in terms of the performance of a complex DW. Three essential aspects of this work are expected, including the representation of knowledge in description logics and the declination of this knowledge into consistent UML diagrams while respecting or extending the CWM specifications and using XML as pivot. The field of application is large but will be adapted to systems with heteroge-neous, complex and unstructured content and moreover requiring a great (re)use of knowledge such as medical data warehouses.

Keywords: data warehouse, description logics, integration, knowledge, metadata

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Authors: Sanjivrao Katakam, Thanumoorthi I., Antony Gerald, Ratan Kulkarni, Shaju Nair

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With increasing energy costs and its impact on the business, sustainability today has evolved from a social expectation to an economic imperative. Therefore, finding methods to reduce cost has become a critical directive for Industry leaders. Effective energy management is the only way to cut costs. However, Energy Management has been a challenge because it requires a change in old habits and legacy systems followed for decades. Today exorbitant levels of energy and operational data is being captured and stored by Industries, but they are unable to convert these structured and unstructured data sets into meaningful business intelligence. It must be noted that for quick decisions, organizations must learn to cope with large volumes of operational data in different formats. Energy analytics not only helps in extracting inferences from these data sets, but also is instrumental in transformation from old approaches of energy management to new. This in turn assists in effective decision making for implementation. It is the requirement of organizations to have an established corporate strategy for reducing operational costs through visibility and optimization of energy usage. Energy analytics play a key role in optimization of operations. The paper describes how today energy data analytics is extensively used in different scenarios like reducing operational costs, predicting energy demands, optimizing network efficiency, asset maintenance, improving customer insights and device data insights. The paper also highlights how analytics helps transform insights obtained from energy data into sustainable solutions. The paper utilizes data from an array of segments such as retail, transportation, and water sectors.

Keywords: energy analytics, energy management, operational data, business intelligence, optimization

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Authors: Hamdi Sana, Emna Bouazizi, Sami Faiz

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In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising.

Keywords: real-time spatial big data, frequent itemset, transaction-based sliding window model, timestamp-based sliding window model, weighted frequent patterns, tree, stream query

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Authors: Iyad Ismail, Fathilatul Abdul Hamid

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This research was mainly investigated to recognize the extent of big data analysis by external auditors. This paper adopts grounded theory as a framework for conducting a series of semi-structured interviews with eighteen external auditors. The research findings comprised the availability extent of big data and big data analysis usage by the external auditors in Palestine, Gaza Strip. Considering the study's outcomes leads to a series of auditing procedures in order to improve the external auditing techniques, which leads to high-quality audit process. Also, this research is crucial for auditing firms by giving an insight into the mechanisms of auditing firms to identify the most important strategies that help in achieving competitive audit quality. These results are aims to instruct the auditing academic and professional institutions in developing techniques for external auditors in order to the big data analysis. This paper provides appropriate information for the decision-making process and a source of future information which affects technological auditing.

Keywords: big data analysis, external auditors, audit reliance, internal audit function

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Authors: Kinjal Parmar, Matteo Lelli

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The usage of IoT technology as a rockfall alert system is relatively new. This paper explains the potential of such an alert system called HelloMac from Maccaferri which provides transportation infrastructure asset owners the way to effectively utilize their resources in the detection of boulder impacts on rockfall barriers. This would ensure a faster assessment of the impacted barrier and subsequently facilitates the implementation of remedial works in an effective and timely manner. In addition, the HelloMac can also be integrated with another warning system to alert vehicle users of the unseen dangers ahead. HelloMac is developed to work also in remote areas, where cell coverage is not available. User gets notified when a rockfall even occurs via mobile app, SMS and email. Using such alarming systems effectively, we can reduce the risk of rockfall hazard.

Keywords: rockfall, barrier, HelloMac, rockfall alert system

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Authors: Poramatdha Chutimant

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The objective of the research was to propose a model of teacher leadership in history instruction for utilization. Everett M. Rogers’ Diffusion of Innovations Theory is applied as theoretical framework. Qualitative method is to be used in the study, and the interview protocol used as an instrument to collect primary data from best practices who awarded by Office of National Education Commission (ONEC). Open-end questions will be used in interview protocol in order to gather the various data. Then, information according to international context of history instruction is the secondary data used to support in the summarizing process (Content Analysis). Dendrogram is a key to interpret and synthesize the primary data. Thus, secondary data comes as the supportive issue in explanation and elaboration. In-depth interview is to be used to collected information from seven experts in educational field. The focal point is to validate a draft model in term of future utilization finally.

Keywords: history study, nationalism, patriotism, responsible citizenship, teacher leadership

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Authors: Mohammad Anwar, Shah Waliullah

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This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models.

Keywords: Bayesian approach, common effect, fixed effect, random effect, Dynamic Random Effect Model

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Authors: Savin Phoeun

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Long time civil war, economic, infrastructure, social, and political structure were destroyed and everything starts from zero. Transport and communication are the key feature of the national economic growth, especially inland transport and other mode take a complementary role which supported by government and international organization both direct and indirect to private sector and small and medium size enterprises. The objectives of this study are to study the general characteristics, capacity and competitive KPIs of Cambodian Inland Transport Operators. Questionnaire and interview were formed from capacity and competitiveness key performance indicators to take apart in survey to Inland Transport Companies in Phnom Penh capital city of Cambodia. And descriptive statistics was applied to identify the data. The result of this study divided into three distinct sectors: 1). Management ability of transport operators – capital management, financial and qualification are in similar level which can compete between local competitors (moderated level). 2). Ability in operation: customer service providing is better but seemed in high cost operation because mostly they are in family size. 3). Local Cambodian Inland Transport Service Providers are able to compete with each other because they are in similar operation level while Thai competitors mostly higher than. The suggestion and recommendation from the result that inland transport companies should access to new technology, improve strategic management, build partnership (join/corporate) to be bigger size of capital and company in order to attract truthfulness from customers and customize the services to satisfy. Inland Service Providers should change characteristic from only cost competitive to cost saving and service enhancement.

Keywords: assessment, competitiveness, inland transport, operator

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Authors: Dimakatso Machetele, Clare Kelso, Thea Schoeman

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In developing countries such as India, China, and South Africa, disposal of household solid waste in rural areas is of great concern. Rural communities face numerous challenges that include the absence of waste collection services and sanitation facilities. The inadequate provision of waste collection and sanitation services results to the occurrence of infectious diseases e.g., malaria. The gap in the management of household solid waste between rural and urban communities, whereby urban communities have better waste management services compared to rural areas is an environmental injustice towards rural communities. The unequal distribution of infrastructure in South Africa’s waste management is a concern that stems from the spatial inequalities of the country’s apartheid history. The Limpopo province has a higher proportion of households without waste collection services from the municipality. The present research objectives are to investigate the public’s perceptions and factors contributing to the management of household solid waste in two contrasting rural Wards in the Greater Tzaneen Municipality. There is limited data and studies that have been conducted to understand the management of household solid waste in rural areas, and specifically, for the Greater Tzaneen Municipality located in the Limpopo province, South Africa. The findings of the study will propose recommendations to the Greater Tzaneen Municipality, rural municipalities in South Africa, and globally to explore sustainable methods to manage household solid waste and explore economic opportunities within the waste management sector to alleviate poverty in rural communities.

Keywords: rural, household solid wase, perceptions, waste management

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Nika GorgoShadze, Anri Shainidze, Bachuki Katamadze

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In parallel to the development of the digital economy in the world, electronic commerce is also widely developing. Internet and ICT (information and communication technology) have created new business models as well as promoted to market consolidation, sustainability of the business environment, creation of digital economy, facilitation of business and trade, business dynamism, higher competitiveness, etc. Electronic commerce involves internet technology which is sold via the internet. Nowadays electronic commerce is a field of business which is used by leading world brands very effectively. After the research of internet market in Georgia, it was found out that quality of internet is high in Tbilisi and is low in the regions. The internet market of Tbilisi can be evaluated as high-speed internet service, competitive and cost effective internet market. Development of electronic commerce in Georgia is connected with organizational and methodological as well as legal problems. First of all, a legal framework should be developed which will regulate responsibilities of organizations. The Ministry of Economy and Sustainable Development will play a crucial role in creating legal framework. Ministry of Justice will also be involved in this process as well as agency for data exchange. Measures should be taken in order to make electronic commerce in Georgia easier. Business companies may be offered some model to get low-cost and complex service. A service centre should be created which will provide all kinds of online-shopping. This will be a rather interesting innovation which will facilitate online-shopping in Georgia. Development of electronic business in Georgia requires modernized infrastructure of telecommunications (especially in the regions) as well as solution of institutional and socio-economic problems. Issues concerning internet availability and computer skills are also important.

Keywords: electronic commerce, internet market, electronic business, information technology, information society, electronic systems

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Authors: Phumelele Kubheka, Pius Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model.

Keywords: big data, latent Dirichlet allocation, latent semantic indexing, telco, topic modeling, twitter

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

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Authors: Peter M. Maurer Maurer

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DGL is a context free grammar-based tool for generating random data. Many types of simulator input data require some computation to be placed in the proper format. For example, it might be necessary to generate ordered triples in which the third element is the sum of the first two elements, or it might be necessary to generate random numbers in some sorted order. Although DGL is universal in computational power, generating these types of data is extremely difficult. To overcome this problem, we have enhanced DGL to include features that permit direct computation within the structure of a context free grammar. The features have been implemented as special types of productions, preserving the context free flavor of DGL specifications.

Keywords: DGL, Enhanced Context Free Grammars, Programming Constructs, Random Data Generation

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Authors: Ouzayr Rabhi, Ibtissam Arrassen

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To provide a complete analysis of the organization and to help decision-making, leaders need to have relevant data; Data Warehouses (DW) are designed to meet such needs. However, designing DW is not trivial and there is no formal method to derive a multidimensional schema from heterogeneous databases. In this article, we present a Model-Driven based approach concerning the design of data warehouses. We describe a multidimensional meta-model and also specify a set of transformations starting from a Unified Modeling Language (UML) metamodel. In this approach, the UML metamodel and the multidimensional one are both considered as a platform-independent model (PIM). The first meta-model is mapped into the second one through transformation rules carried out by the Query View Transformation (QVT) language. This proposal is validated through the application of our approach to generating a multidimensional schema of a Balanced Scorecard (BSC) DW. We are interested in the BSC perspectives, which are highly linked to the vision and the strategies of an organization.

Keywords: data warehouse, meta-model, model-driven architecture, transformation, UML

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Authors: Butta Singh

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This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.

Keywords: chaotic maps, ECG steganography, data embedding, electrocardiogram

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Authors: S. Turkan

Abstract:

In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: data envelopment analysis, super efficiency, logistic regression, financial ratios

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Authors: Yassine Meraihi, Dalila Acheli, Rabah Meraihi

Abstract:

MANET for Mobile Ad hoc NETwork is a collection of wireless mobile nodes that communicates with each other without using any existing infrastructure, access point or centralized administration, due to the higher mobility and limited radio transmission range, routing is an important issue in ad hoc network, so in order to ensure reliable and efficient route between to communicating nodes quickly, an appropriate routing protocol is needed. In this paper, we present the performance analysis of two mobile ad hoc network routing protocols namely DSR and OLSR using NS2.34, the performance is determined on the basis of packet delivery ratio, throughput, average jitter and end to end delay with varying pause time.

Keywords: DSR, OLSR, quality of service, routing protocols, MANET

Procedia PDF Downloads 537