Oya Kalipsiz

Moving Objects Databases In Space Applications

Determining moving objects for example spacecraft and visualizing them in defense simulation rapidly and correctly are important research issues in national defense. In US military C4ISR systems -command, control, communications, computers, intelligence, surveillance and reconnaissance- produce objects distance by layers of systems that combine surveillance with weapons control In military command and control situations the technical requirements of surveillance and reconnaissance involve vast systems and architectures. In this work, moving objects description of geometry over time is concerned in such a military command Moving objects are basically geometries changing over time; hence, this is accomplished in moving object databases which deal with spatial and temporal databases. Spatial databases concern descriptions of geometry in databases and temporal databases address the development of data over time. In specific time or time interval, moving object or objects can be queried in moving object databases efficiently. Moving objects capabilities are researched and an example application is described in this work.

A Comparison Study of Moving Object Index Structures

The task of selecting the most appropriate method for indexing the data according to application requires a careful comparison study of indices of interests. In particular, we consider object movements by tracing their trajectories within a predefined road network. MV3DR-tree and 3DR-tree constitute our first group indexing the objects moving in free movement scenarios. Besides, Mapping and MON-tree are the second group indexing the locations of objects moving over a network of road. Those access methods mainly organize a group of R-tree in order to index the underlying road network and the object movements. Our goal in this study is to evaluate existing proposals under fair circumstances with respect to storage consumption and spatio-temporal query execution performance. In our comparisons, we discuss the structure’s sensibility to query’s spatial and/or temporal extent as well as the tradeoff arising between two groups in terms of reliability and disk access performance. We believe that revealing the vulnerabilities of the selected structures, especially Mapping and MON-tree motivates us to design more robust organizations.

Predicting financial market in big data: Deep learning

Deep Learning is appealing for learning from large amounts of unlabeled/unsupervised data, making it attractive for extracting meaningful representations and patterns from big data. Deep learning, by its simplest definition, is expressed as the application of machine learning methods to the big data. In this study, it was investigated how to apply hierarchical deep learning models for the problems in finance such as prediction and classification. The Design and pricing of securities, construction of portfolios, risk management and stock market forecasting are some of important prediction problems in finance. These kind of problems include large data sets with complex relationship among data and events. It is very difficult or sometimes impossible to represent these complex relationships in a full economic model. Deep learning methods, by representing complex relationships among data, allows the production of more useful results than standard methods in finance. In this study, we introduced and applied deep learning methods to stock market prediction problem and obtained successful results.

Geometric Modeling and Visualization of Moving Objects on a Digital Map

In order to model geographic space, we should consider obviously anything qualifies that might appear on a paper map for example, states, cities, mountains, rivers, highway networks, hospitals, police stations etc. These varied entities can be modeled by using geometric modeling concepts. In many Internet applications and geographical information systems (GIS) digital maps are used to indicate locations of interested static or moving objects which change their futures in spatial and temporal domain. In this paper, we visualized a spatial map which stores its data according to geometric modeling concepts and retrieves its data from database. Then, we simulated moving objects on a digital map according to predefined scenario.

Model Driven Web Application Development with Agile Practices

Model driven development is an effective method due to its benefits such as code transformation, increasing productivity and reducing human based error possibilities. Meanwhile, agile software development increases the software flexibility and customer satisfaction by using iterative method. Can these two development approaches be combined to develop web applications efficiently? What are the challenges and what are the benefits of this approach? In this paper, we answer these two crucial problems; combining model driven development and agile software development results in not only fast development and easiness of the user interface design but also efficient job tracking. We also defined an agile model based approach for web applications whose implementation study has been carried out to support the answers we gave these two crucial problems.

Prediction of project problem effects on software risk factors

Software risks can be defined as uncertainty and loss in project process. Software risk management consists of risk identification, estimation, refinement, mitigation, monitoring and maintenance steps. In our study we focus on prediction of project problem effects that can cause loss in software project in terms of their values on risk factors and also we want to rank our risk factors to observe how they can give detail about project problem effects separately. For these purpose five classification methods for prediction of problem impact and two filter feature selection methods for ranking importance of risk factors are used in this study.

Probabilistic point queries over network-based movements

Spatial-temporal environments contain large number of continuously moving objects on which variety of queries are executed, preferably within a real time. In such a dynamic system, processing location-based queries is a challenging problem. In this paper, the dynamic system has objects moving over a road of network. This paper proposes a new uncertainty model for this type of dynamic system. We applied this model on the indexing scheme that has recently been proposed, namely MON-tree. With our uncertainty model, we also explored a probabilistic query execution algorithm. For the purpose of an experimental evaluation, we set up a simulation environment that visualizes the movements and also provides a query processing capability. To achieve a better index storage utilization and query execution performance, we decreased the location update frequency of the dataset.

Application of Small Ruminant Database and Data Analysis System (Desktop and Android Application)

I n t o d a y ’ s w o r l d , v a r i o u s i n f o r m a t i o n s y st e m s h a v e b een d e s i g n ed f o r h e l p i n g t h e p h y s i c i a n s i n m e d i c i n e f i e l d . B y m ea n s o f t h e s e s y s t e m s , t h e d a t a a ss o c i a t ed w i t h t h e p a t i e n t s are s t o re d , s h ar e d e a s il y o r t h e a n a l y s i s a b o u t t h e p a t i e n t s c a n b e d o n e. I n a n i m al h ea l t h , w h i ch i s a l s o a s i m p o r t a n t a s hu m an h e a l t h , i t i s s e en t h at a n a l y s e s a b o u t t h e i l l n e ss e s a r e q u i t e l i mi t e d . I n o u r o p i n i o n , o n e o f t h e m o s t i m p o r t a n t re a s o n s f o r t h i s i s t h e l ack o f a s y st em w i t h w h i c h t h e d a t a o n t h e a n i m a l s a re st o r e d a n d s h a r e d . B e ca u s e o f t h e l ack o f t h i s k i n d o f s y st e m , l i m i t ed s t ud i es h a v e b e en d o n e o n a n i m a l i l l n e s s es a n d a l t er n a t i v e s o l u t i o n s c o u l d n o t b e p r ov i d ed i n d e t a i l f o r i l l n e ss e s a n d d ea t h s . T h u s , t h e d e c r ea s e i n a n i m a l s a n d a n i m al p r o du c t i o n s h a s b e en r i s i n g d ay b y d ay a n d t h e e c o n o m y o f t h e c o un t ry h a s b e e n a f f e c t e d n eg a t i v e l y . I n t h i s st ud y , a s y st em h as b een p r ov i d ed i n o r d e r t o st o r e , s h a r e a n d r u n d a t a a n a l y s i s r e gar d i n g t h e rec o r d s o f a n i m a l s . I n a d d i t i o n , v i s u al i n t e r f a c es h a v e b e en p r e p a r ed t o f a c i l i t a t e t h e d a t a b a s e p r o ce d u r e s .

Integration issues on user interface based web development

User interface prototypes are effective structure for describing as models for web application development thanks to their graphical and dynamic architecture. It provides the benefits such as language independent development, less error-prone and increased quality. The executable programming codes are created by code transformation feature of model-driven development tools as well. Nevertheless, this technique brings the problems along all its benefits. First of all, code transformation from user interfaces by development tools, creates complicated structure to analyze and modify. Another challenge is to deal with the processes which are triggering each other. In addition, data connected process adaptation is a problem as well. All these issues eventually require the integration of discrete modules. This study provides not only a proposal structure to deal with integration problem in web development, but also provides the evaluation criteria for classification of the integration processes.

Solving test suite reduction problem using greedy and genetic algorithms

Regression testing is an important process for software quality. Test case reduction is one of the widely used techniques for regression testing, which can dramatically decrease the testing costs. However, it is an NP-complete problem and big test cases cannot be accomplished in reasonable amount of time. For this reason, we propose a test suite reduction approach by using greedy and genetic algorithms. The greedy algorithm found a wide usage in previous studies thanks to its simplicity, but we already know that it sticks to local optima and does not benefit from metaheuristics. Thus, we used genetic algorithm to overcome its weaknesses. Our experimental results prove that metaheuristics and evolutionary algorithms perform better than the greedy approaches.