Tanıdığım A. Süheyl Ünver

Taha Toros Arşivi, Dosya No: 16-Süheyl Ünverİstanbul Kalkınma Ajansı (TR10/14/YEN/0033) İstanbul Development Agency (TR10/14/YEN/0033

A MapReduce-Based Big Spatial Data Framework for Solving the Problem of Covering a Polygon with Orthogonal Rectangles

The polygon covering problem is an important class of problems in the area of computational geometry. There are slightly different versions of this problem depending on the types of polygons to be addressed. In this paper, we focus on finding an answer to a question of whether an orthogonal rectangle, or spatial query window, is fully covered by a set of orthogonal rectangles which are in smaller sizes. This problem is encountered in many application domains including object recognition/extraction/trace, spatial analyses, topological analyses, and augmented reality applications. In many real-world applications, in the cases of using traditional central computation techniques, working with real world data results in a performance bottlenecks. The work presented in this paper proposes a high performance MapReduce-based big data framework to solve the polygon covering problem in the cases of using a spatial query window and data are represented as a set of orthogonal rectangles. Orthogonal rectangular polygons are represented in the form of minimum bounding boxes. The spatial query windows are also called as range queries. The proposed spatial big data framework is evaluated in terms of horizontal scalability. In addition, efficiency and speed-up performance metrics for the proposed two algorithms are measured

Vectorization of Large Amounts of Raster Satellite Images in a Distributed Architecture Using HIPI

Vectorization process focus on grouping pixels of a raster image into raw line segments, and forming lines, polylines or poligons. To vectorize massive raster images regarding resource and performane problems, weuse a distributed HIPI image processing interface based on MapReduce approach. Apache Hadoop is placed at the core of the framework. To realize such a system, we first define mapper function, and then its input and output formats. In this paper, mappers convert raster mosaics into vector counterparts. Reduc functions are not needed for vectorization. Vector representations of raster images is expected to give better performance in distributed computations by reducing the negative effects of bandwidth problem and horizontal scalability analysis is done.Comment: In Turkish, Proceedings of International Artificial Intelligence and Data Processing Symposium (IDAP) 201

DIFET: Distributed Feature Extraction Tool For High Spatial Resolution Remote Sensing Images

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.Comment: Presented at 4th International GeoAdvances Worksho

INTEGRATION OF OPENGL GRAPHIC LIBRARIES WITH SPATIAL DATABASE AS AN ANALYSIS AND VISUALIZATION TOOL

Geographic and non-geographic attributes of spatial datasets enable them to be integrated and analyzed in many GIS applications through visualization and analysis tools. The coordinate values of spatial datasets are defined by SRS (Spatial Referencing System) and projection together, and converted to the screen (view) coordinates through coordinate transformations. In this study, we approach this issue in reverse order. We create digitized object in view coordinates by interactive tools developed in open source OpenGL graphics libraries and convert them in real world spatial data. Spatial datasets are stored as vector objects such as points, lines and polygons in spatial databases in a predefined SRS and projection system. The effectiveness of the system will be tested through the application of the spatial queries on the stored objects. Analyses include but are not limited to calculating the area and circumference of polygons and determining of the distances between two points (e.g. houses) or polygons (regions)

Hadoop Optimization for Massive Image Processing: Case Study Face Detection

Face detection applications are widely used for searching, tagging and classifying people inside very large image databases. This type of applications requires processing of relatively small sized and large number of images. On the other hand, Hadoop Distributed File System (HDFS) is originally designed for storing and processing largesize files. Huge number of small-size images causes slowdown in HDFS by increasing total initialization time of jobs, scheduling overhead of tasks and memory usage of the file system manager (Namenode). The study in this paper presents two approaches to improve small image file processing performance of HDFS. These are (1) converting the images into single large-size file by merging and (2) combining many images for a single task without merging. We also introduce novel Hadoop file formats and record generation methods (for reading image content) in order to develop these technique

WEB TABANLI AKILLI BİR DURAK SİSTEMİNİN GERÇEKLENMESİ

Haritalar; coğrafi ve mekânsal verilerin görüntülenmesi, yorumlanması ve analizinde kullanılan yaygın araçlardır. İnsanlar haritaları günlük hayatlarında yön ve adres bulmasında kullanırlar. Haritalar deprem araştırmaları, yurtiçi güvenlik ve erken uyarı sistemleri gibi çeşitli disiplinlerde uygulama ve kullanım alanlarına sahiptir. Toplu taşıma araçlarını kullanan birçok insanın duraklarda beklemelerinden kaynaklı zaman kaybı yaşanmaktadır. Bu çalışmada, toplu taşıma uygulamalarında araçların duraklarda bulunması gereken zamanı akıllı bir şekilde web tabanlı olarak kullanıcılara sunan bir sistem önerilmiştir. Ayrıca kullanıcılar her bir hattın güzergâhını harita üzerinde görebilmekte ve SMS veya e-posta yoluyla istediği hattın durağa geliş zamanını öğrenebilmektedir. Sistemin etkinliği, sentetik veriler üzerinde test edilmiş olup kullanıcılara zaman kazandırması açısından çok faydalı sonuçlar verdiği görülmüştür.

Performing DISC Personal inventory analysis in job postings using artificial intelligence methods

One of the application fields of DISC selfevaluation analysis was introduced to predict people's performance and orientation in their working life. Each letter in the word DISC represents an essential personal characteristic, dividing the profiles of people in business life into four essential parts. In the current study, DISC analysis is conducted on job postings to match the person with the job posting. The current study was based on the analysis of 3 different datasets with job postings in English, Turkish and Romanian prepared by using web scraping methods and then labeled in accordance with DISC criteria. Several different machine learning algorithms have been performed on the DISC analysis outputs, and they reached the best results with accuracy values of around over 96% on the English dataset, around over 95% on the Turkish dataset, and around over 96% on the Romanian dataset, for both D, I, S, C models.Aralı