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Technology has developed rapidly in the last 20 years. Along with the developing technology, there has been a significant increase in the size of the data in the digital environment. Traditional relational databases have not been able to efficiently store and process the amount of data in the digital environment. Relational databases can associate all the data in the database with each other and can generate detailed queries and reports for their users through the advanced query language. However, relational databases lag far behind non-relational databases in performance and financially. The fact that relational databases are not economically efficient and that the database performance decreases due to the increasing amount of digital data has seriously increased the use of non-relational databases. Non-relational databases are specially prepared structures and have gained popularity with their performance, easy maintainability, and cost-effectiveness. Although non-relational databases cannot be used relationally due to their structure, it is possible to use relationally with today's advanced programming languages and advanced database architecture. In this study, it is emphasized how MongoDB, one of the most advanced and most popular non-relational databases of today, can be used relationally through the C# programming language and what benefits this will provide for us. [ABSTRACT FROM AUTHOR]

Today, software developers and computer scientists frequently use online information sharing platforms. StackOverflow is one of these important platforms. The analysis of the information shared in this environment can offer important implications in revealing current issues and trends. In this context, it is aimed to examine the tags of the posts shared on the StackOverflow platform in 2019. In this study, in which textual content analysis based on word frequency analysis was made, a data set was created with the tags used in StackOverflow posts, and the most used tags among these tags, whose acceleration increased and decreased, were analyzed. Detailed results were given on the fifty most used tags among the tags obtained as a result of this analysis. It has been observed that these 50 most used tags are clustered under six classes. Based on the results of the study, it can be said that current technologies, especially innovative web, mobile and non-relational database technologies, are generally used frequently and their acceleration has increased. In addition, it is clear that there is a strong Python-oriented trend in programming languages, tools and libraries. The results of the study are expected to provide important information to curriculum makers and decision-makers, especially software developers and individuals who have received training in this field and plan their career in this field. [ABSTRACT FROM AUTHOR]

Academic collaboration (co-authorship) networks are networks of researchers working together on the same or different disciplines. These networks are used in areas such as the analysis of the development of a country, the ranking of universities and departments. At the same time, the modeling of academic collaborations and the conclusions and interpretations to be made at the level of collaborations through these models are also of great importance. Since this important topic has not been mathematically modeled, the collaboration graphs have been proposed as a new graph type and the related definitions and algorithms have been presented. The academic collaborations in Turkey are modeled with the new graph type presented. The data used in the modeling was extracted from the Web of Science (WOS) database with an application developed and saved as relational database format. The application covers studies that have been published between 1980 and 2015 (including end points) those have authors studying in institutions of Turkey. The main motivation in choosing this data as modeling data is that such a comprehensive study on Turkey has not been done yet. Turkey's academic collaborations are modeled on a regional basis, using the data obtained and the type of graph proposed. A statistical Bron-Kerbosch (iBK) algorithm was developed based on the Bron-Kerbosch (BK) algorithm. The community detection was done using both algorithms. Outputs of algorithms compared. In the light of the results obtained, conclusions were made about the factors affecting the academic collaborations in Turkey. [ABSTRACT FROM AUTHOR]

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Pavements deteriorate under traffic loads and climatic effects. Flexible pavements are formed by multiple layers. Each layer distributes and transfers the effecting loads to its subordinate. When road construction projects are prepared, the single axle load value is considered. Even though projects for State Highways in Turkey are prepared for 8.2 tons single axle load, the maximum axle load was initially increased to 10 tons and subsequently to 13 tons later on. Even though maximum value per single axel load was decreased to 11.5 tons in 1996, it is still over the initial project preparation value for the roads built before the adjustment. In addition, drivers load their vehicles above the allowed limits which results in faster deterioration of the pavement. Even if the pavement is not to be adversely affected by heavy traffic volume, it will still continue to deteriorate naturally due to climatic variables. In Turkey road condition assessments and traffic volume measurements are carried out by the General Directorate of Highways, which is also referred to as KGM. In Turkey, KGM is responsible for the condition evaluation, maintenance, rehabilitation and reconstruction of the road network totaling more than 64000 kilometers. This time consuming and expensive process is conducted by using expensive evaluation equipment known as road profilometre. However KGM only owns a few of these to make necessary measurements and evaluations on its road network. The deterioration measurements are calculated as "Ride Number" (RN) which varies from zero to five, with five representing the best condition of a pavement. KGM starts a rehabilitation process when the measured RN value for a road section drops below the critical limit of 2.5. Every vehicle type has a different effect on the road depending on its number of axles and its weight. Traffic volumes for all road sections in Turkey are measured for four different vehicle categories. These categories are car, bus, truck and trailers. To be able to evaluate the total effect, different vehicle categories on a road must be reduced into one. Each category's volume must be multiplied with its respective coefficient determined by the General Directorate of Highways. The resulting number is called "Equivalent Single Axle Load" (ESAL) value. In this study, a multiple regression model was introduced that can predict the RN value by using cumulative equivalent single axle load for the period of analysis and climate variables of the area where the pavement resides. Modeling is a process that requires extensive amount of data and complex calculations. This data evaluation process has the need for information technology support. In pavement engineering, the use of information technology has an immense power when data storage, process, and evaluation are concerned. The role of suitable, userfriendly software is essential for a fast, accurate, and applicable decision making process. Programming knowledge will not be sufficient by itself to develop such a useful software application. An effective result can be achieved by using the right hardware with an interface, programmed by an expert in the field of study. In this study a decision support system was used to process the high data volume. This decision support system has a large relational database management system (RDBMS), managed by MySQL. This database consists of traffic volume data for all State Highways along with climatic variables for each city in Turkey. This data was gathered by using the measurements conducted by KGM for the period beginning in 1993 through 2007 and detailed climatic data measured by Turkish State Meteorological Service. Forty road sections were determined for the multiple regression model to represent the State Highways. The dataset for these road sections includes cumulative traffic loads for the analysis period, and climatic data for the area upon which the road resides. Using SPSS statistical data analysis software, numerous combinations of multiple regression analysis were applied to different variations of the dataset. In addition, the stepwise method was also applied to determine the independent variables that predict the dependent variable RN with highest accuracy. The multiple regression analysis results indicate that cumulative ESAL, minimum temperature, solar exposure and precipitation levels were significant independent variables for predicting the dependent variable RN at a 95% confidence level. Detailed statistical test results verified the model validity, and revealed that the model was sufficient enough to predict the value RN. [ABSTRACT FROM AUTHOR]

Windows are the most complex and interesting elements in residential design and owners are often confused about how to decide the most efficient window for their residence since there are many complex issues that are difficult to balance. Understanding the energy and associated cost implications of different window systems will help the users, owners or contractors to make the best decision for their particular case, whether it is a new building or a window replacement. However up-to-date, reliable and accurate, time and cost data can be provided from various sources such as manufacturers, contractors, and research institutions, time and cost data regarding the residential window systems are not well-organised in Turkey. A research project was completed to develop a dynamic model to select energy and cost efficient windows for residential buildings, namely "Energy and cost efficient window selection model for residential buildings in different climatic regions of Turkey - HiPerWin". The challenge was to attain a basic source which supports the user to provide the energy and cost data required for the decision making in the selection of the residential window systems. The ultimate objective of the research project was to develop a Relational Database Management System (RDBMS) which incorporated the whole data and processed data into information regarding the window systems and helped the comparison of the alternatives. Hence query parameters were presented for helping the users to define the built environment and housing unit characteristics of their own case to find out the appropriate window alternatives by comparing the total annual heating/cooling energy consumption and associated capital and ownership costs. In the context of the research project, the alternatives of the Insulated Glazing Units (IGU) with different optical and physical properties were generated. The performance values which demonstrate the insulation and solar control capabilities of the IGU's were calculated by using Window5 software. In accordance with having many parameters such as climate, building type, orientation, window area, comprehensive parametric study was conducted for providing the energy use and associated cost data of each case by using a powerful whole building simulation tool EnergyPlus. The analysis was performed for the biggest cities; Ankara, Istanbul and Izmir having the highest degree of mass housing potential and representing temperate-arid, temperate-humid and hothumid climates, respectively. Standard representations of buildings (i.e. building model) required for simulations, involving geometrical and semantic properties were stored in the HiPerWin data base and with the help of the query parameters which were associated with the entire data of the building model a particular case can be defined. … [ABSTRACT FROM AUTHOR]

Electronic Warfare Integrated Reprogramming DataBase (EWIRDB) is the largest data source in the area of electronic warfare. The information in this data source is stored and managed as plain files. The structure of this data source has the following characteristics; dynamic data expandability, ability to store over 2400 parameters, ability to accommodate incomplete data, supporting hierarchically modeled structure. It is difficult to design hierarchical data structure using the relational or object oriented database system; therefore EWIRDB remains as a legacy system and its data is still stored in plain files. When this large structure is modeled using the relational or object oriented databases, the complexity of development increases and the out-coming product is hard to use. The goal of this study is to enable EWIRDB data source to be managed and used more effectively. As an alternative of relational and object oriented database, XML database, which has superior capability of managing hierarchical data structures, is used for better management of data. [ABSTRACT FROM AUTHOR]