Search results for: linked data

Authors: Kristjan Kuhi, Kati K. Kaare, Ott Koppel

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Modern management in most fields is performance based; both planning and implementation of maintenance and operational activities are driven by appropriately defined performance indicators. Continuous real-time data collection for management is becoming feasible due to technological advancements. Outdated and insufficient input data may result in incorrect decisions. When using deterministic models the uncertainty of the object state is not visible thus applying the deterministic models are more likely to give false diagnosis. Constructing structured probabilistic models of the performance indicators taking into consideration the surrounding indicator environment enables to estimate the trustworthiness of the indicator values. It also assists to fill gaps in data to improve the quality of the performance analysis and management decisions. In this paper authors discuss the application of probabilistic graphical models in the road performance measurement and propose a high-level conceptual model that enables analyzing and predicting more precisely future pavement deterioration based on road utilization.

Keywords: Probabilistic graphical models, performance indicators, road performance management, data collection

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Authors: Iftikhar Ahmed Satti, Wan Ismail Wan Yusoff

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The structural interpretation of a part of eastern Potwar (Missa Keswal) has been carried out with available seismological, seismic and well data. Seismological data contains both the source parameters and fault plane solution (FPS) parameters and seismic data contains ten seismic lines that were re-interpreted by using well data. Structural interpretation depicts two broad types of fault sets namely, thrust and back thrust faults. These faults together give rise to pop up structures in the study area and also responsible for many structural traps and seismicity. Seismic interpretation includes time and depth contour maps of Chorgali Formation while seismological interpretation includes focal mechanism solution (FMS), depth, frequency, magnitude bar graphs and renewal of Seismotectonic map. The Focal Mechanism Solutions (FMS) that surrounds the study area are correlated with the different geological and structural maps of the area for the determination of the nature of subsurface faults. Results of structural interpretation from both seismic and seismological data show good correlation. It is hoped that the present work will help in better understanding of the variations in the subsurface structure and can be a useful tool for earthquake prediction, planning of oil field and reservoir monitoring.

Keywords: Focal mechanism solution (FMS), Fault plane solution (FPS), Reservoir monitoring, earthquake prediction.

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Authors: Asep Saepuloh, Katsuaki Koike

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Field mapping activity for an active volcano mainly in the Torrid Zone is usually hampered by several problems such as steep terrain and bad atmosphere conditions. In this paper we present a simple solution for such problem by a combination Synthetic Aperture Radar (SAR) and geostatistical methods. By this combination, we could reduce the speckle effect from the SAR data and then estimate roughness distribution of the pyroclastic flow deposits. The main purpose of this study is to detect spatial distribution of new pyroclastic flow deposits termed as P-zone accurately using the β°data from two RADARSAT-1 SAR level-0 data. Single scene of Hyperion data and field observation were used for cross-validation of the SAR results. Mt. Merapi in central Java, Indonesia, was chosen as a study site and the eruptions in May-June 2006 were examined. The P-zones were found in the western and southern flanks. The area size and the longest flow distance were calculated as 2.3 km2 and 6.8 km, respectively. The grain size variation of the P-zone was mapped in detail from fine to coarse deposits regarding the C-band wavelength of 5.6 cm.

Keywords: Geostatistical Method, Mt. Merapi, Pyroclastic, RADARSAT-1.

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Authors: Hsin-Chang Yang, Chung-Hong Lee, Kuo-Lung Ke

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The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.

Keywords: Self-organizing map, topic identification, learning algorithm, text clustering.

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Authors: Hae-Won Seo, Yong-Gu Lee

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Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: Connected-car, data modeling, route planning, navigation system.

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Authors: Manisha A. Hira, Arup Rakshit

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Textile substrates are endowed with flexibility and ease of making–up, but are non-conductors of electricity. Conductive materials like carbon can be incorporated into textile structures to make flexible conductive materials. Such conductive textiles find applications as electrostatic discharge materials, electromagnetic shielding materials and flexible materials to carry current or signals. This work focuses on use of carbon fiber as conductor of electricity. Carbon fibers in staple or tow form can be incorporated in textile yarn structure to conduct electricity. The paper highlights the process for development of these conductive yarns of polyester/carbon using Friction spinning (DREF) as well as ring spinning. The optimized process parameters for processing hybrid structure of polyester with carbon tow on DREF spinning and polyester with carbon staple fiber using ring spinning have been presented. The studies have been linked to highlight the electrical conductivity of the developed yarns. Further, the developed yarns have been incorporated as weft in fabric and their electrical conductivity has been evaluated. The paper demonstrates the structure and properties of fabrics developed from such polyester/carbon blend yarns and their suitability as electrically dissipative fabrics.

Keywords: Carbon fiber, hybrid yarns, electrostatic dissipative fabrics.

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Authors: Jing Zhou, Steven Su, Aihuang Guo

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COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.

Keywords: BP Neural Network, Exercising Testing, Fault Detection and Identification, Principal Component Analysis.

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Authors: S. Praveen, P. Sojan Lal

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Obfuscation is a low cost software protection methodology to avoid reverse engineering and re engineering of applications. Source code obfuscation aims in obscuring the source code to hide the functionality of the codes. This paper proposes an Array data transformation in order to obfuscate the source code which uses arrays. The applications using the proposed data structures force the programmer to obscure the logic manually. It makes the developed obscured codes hard to reverse engineer and also protects the functionality of the codes.

Keywords: Reverse Engineering, Source Code Obfuscation.

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Authors: Chien-Hua Wang, Wei-Hsuan Lee, Chin-Tzong Pang

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In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to derive from fuzzy association rules. At first, we apply fuzzy partition methods and decide a membership function of quantitative value for each transaction item. Next, we implement FFP-growth to deal with the process of data mining. In addition, in order to understand the impact of Apriori algorithm and FFP-growth algorithm on the execution time and the number of generated association rules, the experiment will be performed by using different sizes of databases and thresholds. Lastly, the experiment results show FFPgrowth algorithm is more efficient than other existing methods.

Keywords: Data mining, association rule, fuzzy frequent patterngrowth.

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: Remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction.

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Authors: Donna R. Tabangin, Jacqueline C. Flores, Nelson F. Emperador

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Information is power. Geographical information is an emerging science that is advancing the development of knowledge to further help in the understanding of the relationship of “place" with other disciplines such as crime. The researchers used crime data for the years 2004 to 2007 from the Baguio City Police Office to determine the incidence and actual locations of crime hotspots. Combined qualitative and quantitative research methodology was employed through extensive fieldwork and observation, geographic visualization with Geographic Information Systems (GIS) and Global Positioning Systems (GPS), and data mining. The paper discusses emerging geographic visualization and data mining tools and methodologies that can be used to generate baseline data for environmental initiatives such as urban renewal and rejuvenation. The study was able to demonstrate that crime hotspots can be computed and were seen to be occurring to some select places in the Central Business District (CBD) of Baguio City. It was observed that some characteristics of the hotspot places- physical design and milieu may play an important role in creating opportunities for crime. A list of these environmental attributes was generated. This derived information may be used to guide the design or redesign of the urban environment of the City to be able to reduce crime and at the same time improve it physically.

Keywords: Crime mapping, data mining, environmental design, geographic visualization, GIS.

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Authors: Ilyes Jenhani, Salem Benferhat, Zied Elouedi

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This paper investigates the issue of building decision trees from data with imprecise class values where imprecision is encoded in the form of possibility distributions. The Information Affinity similarity measure is introduced into the well-known gain ratio criterion in order to assess the homogeneity of a set of possibility distributions representing instances-s classes belonging to a given training partition. For the experimental study, we proposed an information affinity based performance criterion which we have used in order to show the performance of the approach on well-known benchmarks.

Keywords: Data mining from uncertain data, Decision Trees, Possibility Theory.

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance empirical formula, typical SQL query tasks.

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Authors: Hamdi Melih Saraoğlu, Muhlis Yıldırım, Abdurrahman Özbeyaz, Feyzullah Temurtas

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In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.

Keywords: Palm perspiration, Diabetes, Support Vector Machine, Classification.

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Authors: Yasmeen A. S. Essawy, Khaled Nassar

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With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

Keywords: Building information modeling, elemental graph data model, geometric and topological data models, and graph theory.

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Authors: Minsoo Lee, Julee Choi, Sookyung Song

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The increasing interest on processing data created by sensor networks has evolved into approaches to implement sensor networks as databases. The aggregation operator, which calculates a value from a large group of data such as computing averages or sums, etc. is an essential function that needs to be provided when implementing such sensor network databases. This work proposes to add the DURING clause into TinySQL to calculate values during a specific long period and suggests a way to implement the aggregation service in sensor networks by applying materialized view and incremental view maintenance techniques that is used in data warehouses. In sensor networks, data values are passed from child nodes to parent nodes and an aggregation value is computed at the root node. As such root nodes need to be memory efficient and low powered, it becomes a problem to recompute aggregate values from all past and current data. Therefore, applying incremental view maintenance techniques can reduce the memory consumption and support fast computation of aggregate values.

Keywords: Aggregation, Incremental View Maintenance, Materialized view, Sensor Network.

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Authors: Adil Loya, Ali Haider, Arslan A. Ghaffor, Abubaker Siddique

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Simulation and modelling of Unmanned Aerial Vehicle (UAV) has gained wide popularity in front of aerospace community. The demand of designing and modelling optimized control system for UAV has increased ten folds since last decade, as next generation warfare is dependent on unmanned technologies. Therefore, this research focuses on the simulation of nonlinear UAV dynamics on Simulink and its integration with Flightgear. There has been lots of research on implementation of optimizing control using Simulink, however, there are fewer known techniques to simulate these dynamics over Flightgear and a tedious technique of acquiring data has been tackled in this research horizon. Sending data to Flightgear is easy but receiving it from Simulink is not that straight forward, i.e. we can only receive control data on the output. However, in this research we have managed to get the data out from the Flightgear by implementation of level 2 s-function block within Simulink. Moreover, the results captured from Flightgear over a Universal Datagram Protocol (UDP) communication are then compared with the attitude signal that were sent previously. This provide useful information regarding the difference in outputs attained from Simulink to Flightgear. It was found that values received on Simulink were in high agreement with that of the Flightgear output. And complete study has been conducted in a discrete way.

Keywords: aerospace, flight control, FlightGear, communication, Simulink

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Authors: Ana Cázares

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Aims for this study: first, to compare the expertise level in data analysis, communication and information technologies in undergraduate psychology students. Second, to verify the factor structure of E-ETICA (Escala de Experticia en Tecnologias de la Informacion, la Comunicacion y el Análisis or Data Analysis, Communication and Information'Expertise Scale) which had shown an excellent internal consistency (α= 0.92) as well as a simple factor structure. Three factors, Complex, Basic Information and Communications Technologies and E-Searching and Download Abilities, explains 63% of variance. In the present study, 260 students (119 juniors and 141 seniors) were asked to respond to ETICA (16 items Likert scale of five points 1: null domain to 5: total domain). The results show that both junior and senior students report having very similar expertise level; however, E-ETICA presents a different factor structure for juniors and four factors explained also 63% of variance: Information E-Searching, Download and Process; Data analysis; Organization; and Communication technologies.

Keywords: Data analysis, Information, Communications Technologies, Expertise'Levels.

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Authors: Emanuele A. Solagna, Ricardo S, Tozetto, Roberto dos S. Rabello

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Nowadays, physical processes are becoming digitized by the evolution of communication, sensing and storage technologies which promote the development of smart cities. The evolution of this technology has generated multiple challenges related to the generation of big data and the active participation of electronic devices in society. Thus, devices can send information that is captured and processed over large areas, but there is no guarantee that all the obtained data amount will be effectively stored and correctly persisted. Because, depending on the technology which is used, there are parameters that has huge influence on the full delivery of information. This article aims to characterize the project, currently under development, of a platform that based on data science will perform a performance and effectiveness evaluation of an industrial network that implements LoRaWAN technology considering its main parameters configuration relating these parameters to the information loss.

Keywords: Internet of Things, LoRa, LoRaWAN, smart cities.

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Authors: V. Balanica, W. I. D. Rae, M. Caramihai, S. Acho, C. P. Herbst

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Mammographic images and data analysis to facilitate modelling or computer aided diagnostic (CAD) software development should best be done using a common database that can handle various mammographic image file formats and relate these to other patient information. This would optimize the use of the data as both primary reporting and enhanced information extraction of research data could be performed from the single dataset. One desired improvement is the integration of DICOM file header information into the database, as an efficient and reliable source of supplementary patient information intrinsically available in the images. The purpose of this paper was to design a suitable database to link and integrate different types of image files and gather common information that can be further used for research purposes. An interface was developed for accessing, adding, updating, modifying and extracting data from the common database, enhancing the future possible application of the data in CAD processing. Technically, future developments envisaged include the creation of an advanced search function to selects image files based on descriptor combinations. Results can be further used for specific CAD processing and other research. Design of a user friendly configuration utility for importing of the required fields from the DICOM files must be done.

Keywords: Database Integration, Mammogram Classification, Tumour Classification, Computer Aided Diagnosis.

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Authors: Fan Gao, Lior Pachter

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The primary tool currently used to pre-process 10X chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 15 to 18 times faster than Cell Ranger on mouse and human samples. Our tool can also calculate chromatin interaction potential matrices and generate open chromatin signal and interaction traces for cell groups. We use scATAK tool to explore the chromatin regulatory landscape of a healthy adult human brain and unveil cell-type specific features, and show that it provides a convenient and computational efficient approach for pre-processing single-cell ATAC-seq data.

Keywords: single-cell, ATAC-seq, bioinformatics, open chromatin landscape, chromatin interactome

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: Big data, building-value analysis, machine learning, price prediction.

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Authors: Prakash Singh

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The aim of this study was to investigate the extent of teachers’ experiences of tobephobia (TBP) in their heterogeneous classrooms and what impact this had on their emotions and job satisfaction. The expansive and continuously changing demands for quality and equal education for all students in educational organisations that have limited resources connotes that the negative effects of TBP cannot be simply ignored as being non-existent in the educational environment. As this quantitative study reveals, teachers disliking their job with low expectations, lack of motivation in their workplace and pessimism, result in their low self-esteem. When there is pessimism in the workplace, then the employees’ self-esteem will inevitably be low, as pointed out by 97.1% of the respondents in this study. Self-esteem is a reliable indicator of whether employees are happy or not in their jobs and the majority of the respondents in this study agreed that their experiences of TBP negatively impacted on their self-esteem. Hence, this exploratory study strongly indicates that productivity in the workplace is directly linked to the employees’ expectations, self-confidence and their self-esteem. It is therefore inconceivable for teachers to be productive in their regular classrooms if their genuine professional concerns, anxieties, and curriculum challenges are not adequately addressed. This empirical study contributes to our knowledge on TBP because it clearly outlines some of the teaching problems that we are grappling with and constantly experience in our schools in this century. Therefore, it is imperative that the tobephobic experiences of teachers are not merely documented, but appropriately addressed with relevant action by every stakeholder associated with education so that our teachers’ emotions and job satisfaction needs are fully taken care of.

Keywords: Demotivated teachers’ pessimism, low expectations of teachers’ job satisfaction, Self-esteem, Tobephobia.

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Authors: Taner Ersoz, Filiz Ersoz

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This study was designed enable application of multivariate technique in the interpretation of categorical data for measuring health care services satisfaction in Turkey. The data was collected from a total of 17726 respondents. The establishment of the sample group and collection of the data were carried out by a joint team from The Ministry of Health and Turkish Statistical Institute (Turk Stat) of Turkey. The multiple correspondence analysis (MCA) was used on the data of 2882 respondents who answered the questionnaire in full. The multiple correspondence analysis indicated that, in the evaluation of health services females, public employees, younger and more highly educated individuals were more concerned and complainant than males, private sector employees, older and less educated individuals. Overall 53 % of the respondents were pleased with the improvements in health care services in the past three years. This study demonstrates the public consciousness in health services and health care satisfaction in Turkey. It was found that most the respondents were pleased with the improvements in health care services over the past three years. Awareness of health service quality increases with education levels. Older individuals and males would appear to have lower expectancies in health services.

Keywords: Multiple correspondence analysis, optimal scaling, multivariate categorical data, health care services, health satisfaction survey, statistical visualizing, Turkey.

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Authors: Alejandro Gomez Suarez, H. Srikanth Kamath

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In this paper, SFQ (Start Time Fair Queuing) algorithm is analyzed when this is applied in computer networks to know what kind of behavior the traffic in the net has when different data sources are managed by the scheduler. Using the NS2 software the computer networks were simulated to be able to get the graphs showing the performance of the scheduler. Different traffic sources were introduced in the scripts, trying to establish the real scenario. Finally the results were that depending on the data source, the traffic can be affected in different levels, when Constant Bite Rate is applied, the scheduler ensures a constant level of data sent and received, but the truth is that in the real life it is impossible to ensure a level that resists the changes in work load.

Keywords: Cbq, Cbr, Nam, Ns2.

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

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STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to real-world data

Keywords: Rule induction, decision table, missing data, noise.

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Authors: Radek Němec

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Today's business environment requires that companies have access to highly relevant information in a matter of seconds. Modern Business Intelligence tools rely on data structured mostly in traditional dimensional database schemas, typically represented by star schemas. Dimensional modeling is already recognized as a leading industry standard in the field of data warehousing although several drawbacks and pitfalls were reported. This paper focuses on the analysis of another data warehouse modeling technique - the anchor modeling, and its characteristics in context with the standardized dimensional modeling technique from a query performance perspective. The results of the analysis show information about performance of queries executed on database schemas structured according to principles of each database modeling technique.

Keywords: Data warehousing, anchor modeling, star schema, anchor schema, query performance.

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Authors: Enerit Saçdanaku, Idriz Haxhiu

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This study was conducted in the area of Vlora Bay, Albania. Data about Sea Turtles Caretta caretta and Chelonia mydas, belonging to two periods of time (1984 – 1991; 2008 – 2014) are given. All data gathered were analyzed using recent methodologies. For all turtles captured (as by catch), the Curve Carapace Length (CCL) and Curved Carapace Width (CCW) were measured. These data were statistically analyzed, where the mean was 67.11 cm for CCL and 57.57 cm for CCW of all individuals studied (n=13). All untagged individuals of marine turtles were tagged using metallic tags (Stockbrand’s titanium tag) with an Albanian address. Sex was determined and resulted that 45.4% of individuals were females, 27.3% males and 27.3% juveniles. All turtles were studied for the presence of the epibionts. The area of Vlora Bay is used from marine turtles (Caretta caretta) as a migratory corridor to pass from Mediterranean to the northern part of the Adriatic Sea.

Keywords: Caretta caretta, Chelonia mydas, CCL, CCW, tagging, Vlora Bay.

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Authors: Adekitan I. Aderibigbe, Fakolujo A. Olaosebikan

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The parameters of a two-layer soil can be determined by processing resistivity data obtained from resistivity measurements carried out on the soil of interest. The processing usually entails applying the resistivity data as inputs to an optimisation function. This paper proposes an algorithm which utilises the square error as an optimisation function. Resistivity data from previous works were applied to test the accuracy of the new algorithm developed and the result obtained conforms significantly to results from previous works.

 

Keywords: Algorithm, earthing, resistivity, two-layer soil-model.

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Authors: Youngji Yoo, Seung Hwan Park, Daewoong An, Sung-Shick Kim, Jun-Geol Baek

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The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns.

Keywords: Semiconductor, wafer bin map (WBM), feature extraction, spatial point patterns, contour map.

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