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Öğe Anlamsal sensor verileri için bilgi yönetim istemi(Institute of Electrical and Electronics Engineers, 2020) Aktaş, Özlem; Milli, Mehmet; Lakestani, Sanaz; Milli, MusaThe increasing number of wireless sensor networks, which are used extensively in industrial areas, has led to a tremendous increase in the data obtained from the sensors. This uncontrolled increase makes it difficult for managing and storing of data. Moreover, the lack of infrastructure to ensure the integrity of meaning between the data makes it impossible to share, reuse and interpret the data by machines. These inabilities can cause some problems like cannot managing together between separate wireless sensor networks due to the subtle variations in their sensing methods, operating systems, syntax, and data structure. To address all these problems, a semantic sensor network approach has been introduced which provides a common standard for sensor data and aims to enhance their meaning. In this study, semantic model with realworld usage from sensor networks measuring some laboratory parameters is proposed. In order to evaluate the proposed system, a series of semantic queries were prepared and applied to the obtained sensor data. The results show that sensor data can be managed on a common infrastructure using ontologies.Öğe Big Data and its Future(CRC Press, 2023) Milli, Musa; Milli, MehmetBig data is related to many different fields, and it has been studied frequently in the past decades. Therefore, interdisciplinary cooperation is required to understand what big data says by extracting meaningful patterns on the way from raw data to wisdom. There is a well-known rule in statistics: The more data we have, the closer we are to the real model. Yet in practice, it’s not that simple because of the features that come with big data. The global scientific community agrees that the major challenges are velocity, variety and volume, which are referred to as 3Vs. In addition to these challenges, in some fields of study (surveillance system, intrusion detection system, online recommender system, etc.), the results need to be obtained in real-time with algorithms that have the ability to execute online. However, the algorithms we have been working on for a long time have been developed with the ability to process a single type of data in batches, and scalability is often neglected. Many traditional batch algorithms are insufficient to handle big data. To deal with big data, researchers have investigated new methods and architectures that will not have with these difficulties. Although there are problems, big data offers great opportunities for those who know how to manage it. The goal of this chapter is to provide an in-depth understanding of big data, its challenges, platforms, opportunities, and future research. © 2023 CRC Press.Öğe Knowledge Management System for Semantic Sensor Data(Ieee, 2020) Aktas, Ozlem; Milli, Mehmet; Lakestani, Sanaz; Milli, MusaThe increasing number of wireless sensor networks, which are used extensively in industrial areas, has led to a tremendous increase in the data obtained from the sensors. This uncontrolled increase makes it difficult for managing and storing of data. Moreover, the lack of infrastructure to ensure the integrity of meaning between the data makes it impossible to share, reuse and interpret the data by machines. These inabilities can cause some problems like cannot managing together between separate wireless sensor networks due to the subtle variations in their sensing methods, operating systems, syntax, and data structure. To address all these problems, a semantic sensor network approach has been introduced which provides a common standard for sensor data and aims to enhance their meaning. In this study, semantic model with real-world usage from sensor networks measuring some laboratory parameters is proposed. In order to evaluate the proposed system, a series of semantic queries were prepared and applied to the obtained sensor data. The results show that sensor data can be managed on a common infrastructure using ontologies.Öğe Modelling sensor ontology with the SOSA/SSN frameworks: a case study for laboratory parameters(Tubitak Scientific & Technical Research Council Turkey, 2020) Aktaş, Özlem; Milli, Mehmet; Lakestani, Sanaz; Milli, MusaRecently, the use of sensor-based systems in many areas has led to an exponential increase in the raw sensor data. However, the lack of neither syntactic nor semantic integrity between these sensor data limited their sharing, reusability, and interpretation. These inabilities can cause some problems. For example, different wireless sensor networks may not work together due to the subtle variations in their sensing methods, operating systems, syntax, and data structure. In recent years, to cope with these inabilities, the semantic sensor web approach, which enables us to enrich the meaning of sensor data, has been seen as the critical technology in solving these problems by some researchers. The primary purpose of this study is to create a laboratory environment parameters sensor ontology (LEPSO) that provides a standard data model for heterogeneous sensor data from different platforms by expanding semantic sensor networks (SSN). A case study was conducted using the real-time data collected from Bolu Abant izzet Baysal University, Scientific Industrial Technological Application and Research Center in order to demonstrate that the proposed LEPSO can be used in similar sensor-based applications. A series of semantic queries have been performed on the collected sensor data to evaluate the proposed sensor ontology. The results showed that sensor data, which are heterogeneous by nature, provide benefit results in sensor-based monitoring systems when enriched with semantic web technologies and ontologies. Besides, this study proves that the proposed semantic sensor ontology, which used the semantic sensor network framework, has the capability to provide a common infrastructure for many sensor-based applications. The proposed ontology has the potential to become a more comprehensive ontology by adding different platforms, different sensors, different environments such as school, factory. In the next study, it is aimed to expand the scope of this semantic sensor network, which is formed by including this ontology in the intensive care unit of a hospital.Öğe SOSA/SSN sensör ontoloji çerçevelerini kullanarak laboratuvar ortamlarında semantik tabanlı anomali tespiti(Pamukkale University, 2023) Milli, Musa; Milli, Mehmet; Lakestani, Sanaz; Aktaş, ÖzlemGünümüz modern dünyasında, laboratuvarlar okullarda, hastanelerde ve birçok kurumda, eğitim hayatının, iş hayatının ve gündelik yaşamın vazgeçilmez parçaları haline gelmiştir. Laboratuvarlar gerek eğitim alanında, gerek sağlık alanında veya gerekse endüstriyel alanda kullanılsın en temel prensip çalışanların ve çevrenin güvenliğinin sağlanması olmalıdır. Güvenlik önlemlerin ise en başında insan sağlığını doğrudan etkileyen ve laboratuvarların doğası gereği ortamda bulunmak zorunda olan fiziksel (sıcaklık, nem), kimyasal (gazlar), biyolojik (bakteriler, virüsler) ortam parametrelerinin sürekli izlenmesi, takibinin yapılması ve kontrol altında tutulması gelmektedir. Laboratuvar ortamlarında bu parametrelerin kontrol altında tutulması birçok yerde ya hiç yapılmamaktadır ya da hala klasik ve konvansiyonel yöntemler ile yapılmaktadır. Bu çalışmada laboratuvar ortam parametrelerinin devamlı izlenmesi amacı ile klasik yöntemlerin dezavantajlarını ortadan kaldırmak için sensör tabanlı bir sistem kurulmuştur. Önerilen sensör tabanlı sistem semantik web teknolojileri ile anlamsal olarak zenginleştirilmiştir. Böylelikle önerilen sistemin etkinliği ve sürdürülebilirliği de arttırılmıştır. Özellikle son yıllarda tüm dünyayı etkisi altına alan ve hava yolu ile bulaşan Covid-19 gibi hastalıkların yayılımın azaltmak için iç mekân ortamlarının hava kalitelerinin gözetimi ve iyileştirilmesi şarttır. Önerilen çalışmanın özellikle Covid-19 gibi salgın zamanlarında hastaneler, okullar, toplu taşıma araçları ve yoğun bakım üniteleri gibi kritik öneme sahip alanlarda kullanılma potansiyeli yüksektir. Sonraki çalışmalarda önerilen sisteme yapay zekâ yaklaşımları da eklenerek sisteme ileriye yönelik hava kalitesi tahmin kabiliyeti kazandırılacaktır. Geliştirilen sistem sayesinde kurumlar ve firmalar eylem planlarını daha erken devreye sokarak ortam şartlarının yönetilebilirliği noktasında avantaj sağlayacaklardır.
