Directorate for Space, Security and Migration

In this paper we address the issue of conceptual modeling of data used in multidimensional analysis. We view the problem from the end-user point of view and we describe a set of requirements for the conceptual modeling of realworld OLAP scenarios. Based on those requirements we then define a new conceptual model that intends to capture the static properties of the involved information. In its definition we use a minimal set of well-understood OLAP concepts like dimensions, levels, hierarchies, measures and cubes. The central concept of the model is the Multidimensional Aggregation Cube (MAC), which gives a broad and flexible definition to the notion of a multidimensional cube. We evaluate our model against other existing multidimensional models and show that MAC offers a unique combination of modeling skills. Our main contribution is the definition of the basic concepts of our model; although the set of requirements and the evaluation of all related models against those requirements represent an additional result.

A methodology recently proposed to improve processing of star queries on data warehouses is the clustering and indexing of fact tables using their multidimensional hierarchies [DRSN98, MRB99, KS01]. Due to this improved organization schema, processing of aggregation star queries changes dramatically creating new optimization opportunities. An important optimization technique is the so-called pre-grouping transformation. Although this transformation is expected to improve the query-processing plan in most cases, there are several cases where it is not beneficial.

Star queries are the most prevalent kind of queries in data warehousing, OLAP and business intelligence applications. Thus, there is an imperative need for efficiently processing star queries. To this end, a new class of fact table organizations has emerged that exploits path-based surrogate keys in order to hierarchically cluster the fact table data of a star schema [DRSN98, MRB99, KS01]. In the context of these new organizations, star query processing changes radically. In this paper, we present a complete abstract processing plan that captures all the necessary steps in evaluating such queries over hierarchically clustered fact tables. Furthermore, we present optimizations for surrogate key processing and a novel early grouping transformation for grouping on the dimension hierarchies. Our algorithms have been already implemented in a commercial relational database management system (RDBMS) and the experimental evaluation, as well as customer feedback, indicates speedups of orders of magnitude for typical star queries in real world applications.

Consider a standard statistical hypothesis test, leading to a binary decision made by exploiting a certain dataset. Suppose that, later, part of the data is lost, and we want to refine the test by exploiting both the surviving data and the previous decision. What is the best one can do? Such a question, here referred to as the unlucky broker problem, can be addressed by very standard tools from detection theory, but the solution gives intriguing insights and is by no means obvious. We provide the general form of the optimal detectors and discuss in depth their modus operandi, ranging from simple likelihood ratio tests to more complex behaviors. Limiting cases, where either the surviving data or the initial decision is almost useless, are also discussed.

We formulate, analyze, and solve a novel topic in detection theory, here referred to as the unlucky broker problem. Suppose you have a standard statistical test between two hypotheses, leading to the optimal Bayesian decision made by exploiting a certain dataset. Later, suppose that part of the data is lost, and we want to remake the test by using the surviving data and the previous decision. What is the best we can do?

Consider a standard statistical hypothesis test, leading to a binary decision made by exploiting a certain dataset. Suppose that, later, part of the data is lost, and we want to refine the test by exploiting both the surviving data and the previous decision. What is the best one can do? Such a question, here referred to as the unlucky broker problem, can be addressed by very standard tools from detection theory, but the solution gives intriguing insights and is by no means obvious. We provide the general form of the optimal detectors and discuss in depth their modus operandi, ranging from simple likelihood ratio tests to more complex behaviors. Limiting cases, where either the surviving data or the initial decision is almost useless, are also discussed.

A system was set up to ingest automatic ship position reports (terrestrial and satellite AIS, LRIT) and fuse these into a Maritime Situational Picture, tracking the ships within an ocean basin-wide area of interest in real time. Trial runs were made over several months, collecting reporting data from a number of different sources, over the Gulf of Aden and the Western Indian Ocean. Also satellite radar surveillance was carried out in order to sample the presence of non-reporting ships. The trial showed that satellite AIS is a powerful tool for basin-wide ship traffic monitoring; that multiple AIS satellites are needed for sufficient completeness and update rate; and that coastal AIS and LRIT still provide essential complements to the satellite AIS data. The radar survey showed that about half of the radar-detected ships are not seen in the reporting data. The ultimate purpose of this work is to support the countries around the Horn of Africa in the fight against piracy and to help build their capacity to deliver maritime security and safety. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 7 Number 2

The growing availability of data coming from ship reporting systems, such as Automatic Identification System (AIS) and Long Range Identification and Tracking (LRIT), is originating an unprecedented set of opportunities to enforce maritime surveillance, ensure the security of the traffic at sea, and manage maritime operations. In this paper, a data-driven methodology is proposed to estimate the vessel times of arrival in port areas. The developed approach exploits both AIS and LRIT historical maritime traffic data collected over a desired area of interest and is based on an optimized data-driven path-finding algorithm. The methodology is applied and validated to real scenarios with real data sets, showing how a list of times of arrival can be automatically computed for predefined ports and progressively refined. Such information is expected to increase port operational efficiency and safety. Index Terms-Estimated time of arrival, maritime situational awareness, data-driven path-finding, vessel tracking data, port operations. I. INTRODUCTION T HE understanding of maritime traffic has become an increasingly important topic of research due to the wide range of activities of vessels at sea and for its direct implications on security, safety, environmental and socioeconomic factors. On the one hand, with over 80% of world merchandise trade by volume being carried by sea, maritime transport remains the backbone supporting international trade and globalisation. On the other hand, every year, 400 million passengers embark and disembark in European ports, which represent vital gateways linking transport corridors all around the world [1]. In addition, ports play a key role not only in moving goods but also in generating employment, with 1.5 million workers employed in European ports and the same amount employed indirectly across the EU maritime State Members [2]. As a consequence, maritime transport and ports demand greater monitoring and understanding (e.g. for safety, security and efficiency.) The growing number of ship reporting technologies and remote sensing systems (e.g. Automatic Identification

Endüstriyel alanlarda doğal afetler, kazalar veya kötü işletme uygulamaları nedeniyle oluşan sızıntı ve döküntüler ile başı boş alanlara bilinçli ve kanun dışı olarak yapılan deşarjlar nedeniyle toprağın tehlikeli kimyasallar tarafından kirlenmesi önemli bir çevre sorunu oluşturmaktadır. Avrupa Birliği ülkeleri dahil olmak üzere, bir çok gelişmiş ülke bu tür kirliliklerin önlenmesi ve oluştuğu durumlarda da uygun şekilde temizlenmesi ve bertarafına yönelik yasal mevzuata ve bu mevzuatları destekleyen yönetim sistemlerine sahiptir. Ülkemizde ise bu konuda ciddi bir yasal boşluk bulunmakta, giderek varlıkları ortaya çıkan ve sayıları artan kirlenmiş alanların ne şekilde ele alınması gerektiği net olarak ortaya konulamamaktadır. Bu konudaki eksikliğin giderilmesi amacıyla, endüstriyel kaynaklı kirlenmiş sahaların belirlenmesi, kirlilik durumlarının tespiti ve uygun temizleme yöntemlerinin belirlenmesi için bir çalışma gerçekleştirilmektedir. Gerekli yönetmelik ve teknik tebliğlerin hazırlanmasını da içeren çalışmada, yasal mevzuatın uygulanmasını destekleyecek bir bilgi sistemi geliştirilmiştir. Bu makalede geliştirilen bilgi sisteminin yapısı ve özellikleri ile ilgili bilgi verilmektedir.

Data visualization and analysis tools constitute an important part of today's water resources protection, development, and management studies. Especially, GIS is very widely used in the last decades. U.S. EPA's BASINS and European Union's GREAT-ER systems are sound examples of latest developments in GIS-based systems. Although located at water critical geo-political location and has many on-going and planned water resources related projects, Turkey still has neither national hydrography datasets nor local water quality/quantity data analysis tools. In this study, a GIS-based data visualization and analysis system has been developed for stream flow data collected in Turkey. The system developed using AVENUE scripting language of ArcView 3.2 features a graphical user interface that utilizes dialogs, tables and charts to interact with the user and visualize stored data. A number of spatial and statistical analysis tools are also made available to reveal trends in data, calculate summaries and create thematic maps.

A national hydrographic data set can be defined as a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of water, paths through which water flows, and related entities. In the study, national hydrographic datasets of several countries are reviewed. Based on the reviewed datasets, initial studies are conducted in a small watershed in Turkey, Küçük Menderes Basin, to develop a similar national hydrographic data set for Turkey. Using GIS methods, the drainage network of the watershed has been obtained from the digital elevation model (DEM). Connectivity information, Strahler order, flow length, slope, and similar hydrologic information are calculated and linked to the stream reaches. The boundaries of the catchments are created from the DEM, and a three-level hydrologic cataloging unit hierarchy is formed.

Natural-hazard triggered technological accidents (natechs) at industrial facilities have been recognized as an emerging risk. Adequate preparedness, proper emergency planning, and effective response are crucial for the prevention of natechs and mitigation of the consequences. Under the conditions of a natural disaster, the limited resources, the possible unavailability of mitigation measures, and the lack of adequate communication complicate the management of natechs. The analysis of past natechs is crucial for learning lessons and for preventing or preparing for future natechs. The 17 August 1999, Kocaeli earthquake, which was a devastating disaster hitting one of the most industrialized regions of Turkey, offers opportunities in this respect. Among many natechs that occurred due to the earthquake, the massive fire at the TUPRAS Izmit refinery and the acrylonitrile spill at the AKSA acrylic fiber production plant were especially important and highlight problems in the consideration of natechs in emergency planning, response to industrial emergencies during natural hazards, and information to the public during and following the incidents. The analysis of these events shows that even the largest and seemingly well-prepared facilities can be vulnerable to natechs if risks are not considered adequately.

Günümüzde bilgisayar destekli veri analizi ve görselleştirme araçlari, su kaynaklarinin korunmasi, geliştirilmesi ve yönetimi çalişmalarinda önemli rol oynamaktadir. Özellikle Coğrafi Bilgi Sistemleri (CBS) bu amaçla son yillarda dünya çapinda oldukça yaygin olarak kullanilmaktadir. Su kaynaklari açisindan kritik bir jeopolitik konumda bulunan ve su kaynaklarinin geliştirilmesi için gerek orta gerekse büyük çapli bir çok projenin yürütüldüğü ve geleceğe yönelik olarak planlandiği ülkemizde de bu tür çalişmalarda CBS kullanimi giderek önem kazanmakta ve yayginlaşmaktadir. Ancak bu çalişmalari destekleyecek ve kolaylaştiracak, yerel koşullara uygun, yüksek nitelikli ve kolay ulaşilabilir hidrolojik veri setlerinin halen mevcut bulunmamasi önemli bir eksiklik olarak dikkat çekmektedir. Benzer şekilde böylesi veri setlerini altlik olarak kullanarak, mevcut su kalitesi, akim gözlem, vb. hidrolojik ve çevresel veriler üzerinde gerek temel gerekse ileri seviyede analiz imkanlari sunacak yerel analiz araçlari da halihazirda mevcut bulunmamaktadir. Bu eksikliklerin giderilmesi amaciyla gerçekleştirilen çalişmalar kapsaminda Devlet Su İşleri Genel Müdürlüğü (DSİ) ve Elektrik İşleri Etüd İdaresi Genel Müdürlüğü (EİEİ) tarafindan toplanmakta olan su kalitesi verilerine CBS ortaminda ulaşilabilmesini ve bu veriler üzerinde istatistiksel analizler yapilabilmesini sağlayan bir uygulama geliştirilmiştir. ArcView 3.2 ortaminda AVENUE programlama dili kullanilarak geliştirilen uygulama mevcut verilerin yapisi ile uyumlu bir veri tabani, veri tabanindaki verilere kolay ulaşim sağlayan grafik kullanici arabirimi, ve coğrafi analiz araçlarindan oluşmaktadir. Uygulama sayesinde su kalitesi gözlem istasyonlarina ait genel bilgilere ulaşilabilmekte, aylik su kalitesi ölçümleri gerek tablo gerekse grafik olarak elde edilebilmekte, bu veriler üzerinde istatistiksel analizler yapilabilmekte ve çeşitli kriterlere göre su kalitesine yönelik tematik haritalar oluşturulabilmektedir. Uygulama ayrica kullanici tarafindan veri girişini kolaylaştiracak araçlar da içermektedir.

Turkey has been divided into 26 basins for studies related to water resources development. Surface areas of the basins range between 6,300 – 127,500 km2, the average being approximately 30,000 km2. Compared with nested watershed classification of U.S., the average size of Turkish national basins corresponds to the third-level (basins). Currently, a forth-level (sub-basins) with an average size of 1,820 km2 reside below the basins in U.S. classification. Even these sub-basins are found to be too large to adequately serve many water resource management needs. Two additional levels (watersheds and sub-watersheds) are currently under development in order to solve this problem. Taking this fact into consideration, currently existing basins of Turkey are supposed to be too large for effective water resources management and there is a need for delineation of sub-basins in a scientific manner. In this study, the boundaries of proto-type sub-basins are determined by using DEM-based automated delineation techniques. For this purpose, 30 arc-seconds SRTM30 DEM is re-projected into an equal area projection, hydrologic sinks are filled and inaccurate elevation values are corrected. In order to prevent invalid delineation of closed basins that cover approximately 10% of the country, water bodies located at the center of these basins are extracted from Global Land Cover Characteristics dataset of USGS and excluded from the process. Among the alternatives, Deterministic-8 (D-8) algorithm with modifications by Garbrecht and Martz is used to calculate flow direction and flow accumulation grids. Using these grids, a set of candidate sub-basins are generated by applying different area thresholds. A threshold of 1,000 km2 resulted in 359 sub-basins with an average size of 1,858 km2, both of which are found to be appropriate and manageable for nationwide usage. Similarity of the average size of the delineated sub-basins to that of U.S. sub-basins also supports this result.

Natural-hazard triggered accidents at industrial facilities (natechs) are recognized as an emerging risk with possibly serious consequences. Risk maps are helpful to identify natech hot spots. However, recent surveys showed that hardly any natech risk maps exist in the OECD and EU. A probabilistic natech risk mapping methodology for earthquakes was developed to till this gap and was implemented as a web-based software tool: RAPID-N. The tool allows rapid natech risk assessment and mapping with minimum data input. It can calculate on-site natural hazard parameters and estimate site, process equipment, and substance properties. A basic set of damage states and fragility curves is provided for damage assessment. Custom damage states and fragility curves can also be defined for different process equipment. Conditional probabilistic relationships can be specified between damage states and possible natech event scenarios. The consequences of the natech events are assessed using the Risk Management Program methodology of US EPA and the results are presented as summary reports and interactive risk maps. The tool can be used for land-use and emergency planning.