Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010611
Lyubka Pashova, B. Srebrov, O. Kounchev
The paper contains an overview of world data centres as INETMAGNET, SWS, DIAS, IGS, and EUREF, which are repositories of scientific Big Data sets for studying geomagnetic storms, by the means of the available geomagnetic, ionospheric and GNSS data. As an example, the results of a study based on Wavelet Analysis of the local manifestation of the geomagnetic storm on September 7–8, 2017, using time series of observed geophysical parameters obtained from different stations in the Balkans and from satellite observations, are presented. These data include global geomagnetic indexes and local data, in which the H-component of the geomagnetic field, critical frequency foF2 of the ionospheric F2 layer, and VTEC from GPS observations at a separate measuring station are included too. Specific features of the local manifestation of this storm event are outlined, based on the performed joint analysis and comparison of the geophysical parameters.
{"title":"Investigation of Strong Geomagnetic Storms Using Multidisciplinary Big Data Sets","authors":"Lyubka Pashova, B. Srebrov, O. Kounchev","doi":"10.1109/BdKCSE48644.2019.9010611","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010611","url":null,"abstract":"The paper contains an overview of world data centres as INETMAGNET, SWS, DIAS, IGS, and EUREF, which are repositories of scientific Big Data sets for studying geomagnetic storms, by the means of the available geomagnetic, ionospheric and GNSS data. As an example, the results of a study based on Wavelet Analysis of the local manifestation of the geomagnetic storm on September 7–8, 2017, using time series of observed geophysical parameters obtained from different stations in the Balkans and from satellite observations, are presented. These data include global geomagnetic indexes and local data, in which the H-component of the geomagnetic field, critical frequency foF2 of the ionospheric F2 layer, and VTEC from GPS observations at a separate measuring station are included too. Specific features of the local manifestation of this storm event are outlined, based on the performed joint analysis and comparison of the geophysical parameters.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132899637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010596
Ivan Gaidarski, P. Kutinchev
The paper present our approach for protecting sensitive data, using the methods of Big Data. To effectively protect the valuable information within the organization, the following steps are needed: Employing a holistic approach for data classification, identifying sensitive data of the organization, Identifying critical exit points - communication channels, applications and connected devices and protecting the sensitive data by controlling the critical exit points. Our approach is based on creating of component-based architecture framework for ISS, conceptual models for data protection and implementation with COTS IT security products as Data Leak Prevention (DLP) solutions. Our approach is data centric, which is holistic by its nature to protect the meaningful data of the organization.
{"title":"Using Big Data for Data Leak Prevention","authors":"Ivan Gaidarski, P. Kutinchev","doi":"10.1109/BdKCSE48644.2019.9010596","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010596","url":null,"abstract":"The paper present our approach for protecting sensitive data, using the methods of Big Data. To effectively protect the valuable information within the organization, the following steps are needed: Employing a holistic approach for data classification, identifying sensitive data of the organization, Identifying critical exit points - communication channels, applications and connected devices and protecting the sensitive data by controlling the critical exit points. Our approach is based on creating of component-based architecture framework for ISS, conceptual models for data protection and implementation with COTS IT security products as Data Leak Prevention (DLP) solutions. Our approach is data centric, which is holistic by its nature to protect the meaningful data of the organization.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115629990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010608
M. Chernoskutov
The paper describes graph algorithms performance when using different types of data structures. To achieve that, we developed a multi-level graph processing system, which allows to create graph applications independently of any implementation details such as graph data structure or underlying computational architecture. We measure the performance of breadth-first search, max flow and random graph building algorithms when using compressed sparse row and adjacency matrix data structures. Experiments reveal different graph processing rates for different data structures, which indicates the need of using specific data structures for specific algorithms to achieve highest performance.
{"title":"Graph Processing with Different Data Structures","authors":"M. Chernoskutov","doi":"10.1109/BdKCSE48644.2019.9010608","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010608","url":null,"abstract":"The paper describes graph algorithms performance when using different types of data structures. To achieve that, we developed a multi-level graph processing system, which allows to create graph applications independently of any implementation details such as graph data structure or underlying computational architecture. We measure the performance of breadth-first search, max flow and random graph building algorithms when using compressed sparse row and adjacency matrix data structures. Experiments reveal different graph processing rates for different data structures, which indicates the need of using specific data structures for specific algorithms to achieve highest performance.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124819835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010609
E. Sotirova, V. Vasilev, G. Bozova, H. Bozov, S. Sotirov
The aim of this paper is to analyze a statistical data for the registered patients with malignant neoplasms of the digestive organs in Burgas region for the period 2014–2018. The InterCriteria Analysis method is applied. The results are commented from different points of view: relations between age of the patients with malignant neoplasms of the digestive organs, relations between marital status and year of registration of patient and type of Malignant neoplasms of the digestive organs. The obtained results by InterCriteria Analysis method are compared by statistical analysis according to Pearson, Kendall and Spearman.
{"title":"Application of the InterCriteria Analysis Method to a Dataset of Malignant Neoplasms of the Digestive Organs for the Burgas Region for 2014–2018","authors":"E. Sotirova, V. Vasilev, G. Bozova, H. Bozov, S. Sotirov","doi":"10.1109/BdKCSE48644.2019.9010609","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010609","url":null,"abstract":"The aim of this paper is to analyze a statistical data for the registered patients with malignant neoplasms of the digestive organs in Burgas region for the period 2014–2018. The InterCriteria Analysis method is applied. The results are commented from different points of view: relations between age of the patients with malignant neoplasms of the digestive organs, relations between marital status and year of registration of patient and type of Malignant neoplasms of the digestive organs. The obtained results by InterCriteria Analysis method are compared by statistical analysis according to Pearson, Kendall and Spearman.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131332539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010604
G. P. Jesi, Elisabetta Gori, Stefano Micocci, G. Mazzini
This paper is about the design and the implementation of Lepida ScpA BigData infrastructure. Our goal is to provide the Regional PA with proper tools to address future challenges such as planning the allocation of resources and creating new business models involving public and private organizations. Our first design of the infrastructure started from a specific scenario and addresses a particular aspect: ingesting the Regional public WiFi data traffic and gathering interesting analytics. We describe the challenges we faced and the choices we made during the process and the final results we achieved.
{"title":"Building Lepida ScpA BigData Infrastructure","authors":"G. P. Jesi, Elisabetta Gori, Stefano Micocci, G. Mazzini","doi":"10.1109/BdKCSE48644.2019.9010604","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010604","url":null,"abstract":"This paper is about the design and the implementation of Lepida ScpA BigData infrastructure. Our goal is to provide the Regional PA with proper tools to address future challenges such as planning the allocation of resources and creating new business models involving public and private organizations. Our first design of the infrastructure started from a specific scenario and addresses a particular aspect: ingesting the Regional public WiFi data traffic and gathering interesting analytics. We describe the challenges we faced and the choices we made during the process and the final results we achieved.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114656238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010613
V. Sgurev, L. Doukovska, S. Drangajov
Using of a generalized network flow model with gains and losses is proposed for solving the problem of optimal network traffic and data processing from sources to the consumers. The problem for finding traffic of minimal cost is reduced solving the linear network flow problem. A method for network flow optimization is proposed for finding the maximal data flow from sources to consumers. It is shown that this flow is equal to the minimal cut of the network between sources and consumers. Theoretical results, obtained in the present work are confirmed by two appropriate numerical examples.
{"title":"Optimal Data Traffic and Computer Processing by a Generalized Network Flow Model with Gains and Losses","authors":"V. Sgurev, L. Doukovska, S. Drangajov","doi":"10.1109/BdKCSE48644.2019.9010613","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010613","url":null,"abstract":"Using of a generalized network flow model with gains and losses is proposed for solving the problem of optimal network traffic and data processing from sources to the consumers. The problem for finding traffic of minimal cost is reduced solving the linear network flow problem. A method for network flow optimization is proposed for finding the maximal data flow from sources to consumers. It is shown that this flow is equal to the minimal cut of the network between sources and consumers. Theoretical results, obtained in the present work are confirmed by two appropriate numerical examples.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121934532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010651
Kelly Ursenbach, M. Mintchev
Directional drilling is common in oil and natural gas extraction, offering numerous advantages. However, directional drilling requires accurate trajectory measurements. Azimuth is a critical part of the trajectory measurement, but industry standard methods of determining azimuth have drawbacks. Inertial navigation systems (INS) have been proposed as a solution but require periodic calibration. In-drilling alignment (IDA) is a calibration method which uses measured motion of the inertial measurement unit (IMU) to overcome the observability problems that come with a static calibration. This paper presents a novel model which converts IDA motion and inertial measurements into an azimuth estimate. The model is validated using a device designed to carry out IDA under laboratory conditions.
{"title":"Effect of In-Drilling Alignment with General Dynamic Error Model on Azimuth Estimation","authors":"Kelly Ursenbach, M. Mintchev","doi":"10.1109/BdKCSE48644.2019.9010651","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010651","url":null,"abstract":"Directional drilling is common in oil and natural gas extraction, offering numerous advantages. However, directional drilling requires accurate trajectory measurements. Azimuth is a critical part of the trajectory measurement, but industry standard methods of determining azimuth have drawbacks. Inertial navigation systems (INS) have been proposed as a solution but require periodic calibration. In-drilling alignment (IDA) is a calibration method which uses measured motion of the inertial measurement unit (IMU) to overcome the observability problems that come with a static calibration. This paper presents a novel model which converts IDA motion and inertial measurements into an azimuth estimate. The model is validated using a device designed to carry out IDA under laboratory conditions.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124571904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010606
S. Ilchev, Z. Ilcheva
In this paper, we present our new design of a thermoelectric cooling driver for laser projection systems. It is a reliable, effective cooling solution for semiconductor laser diodes that are used instead of traditional displays or projectors when higher brightness or contrast is required. Typical applications are multimedia shows, quality improvement in production facilities or marketing in public areas. The laser animations usually require rapid changes in the output power that are difficult to predict. Our driver is capable of reacting to them by actively pumping the excess power generated by the diodes into the main heatsink of the system. The heat transfer speed is regulated by the driver in real time to maintain an optimum working temperature.
{"title":"Thermoelectric Cooling Driver for Laser Projection Systems","authors":"S. Ilchev, Z. Ilcheva","doi":"10.1109/BdKCSE48644.2019.9010606","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010606","url":null,"abstract":"In this paper, we present our new design of a thermoelectric cooling driver for laser projection systems. It is a reliable, effective cooling solution for semiconductor laser diodes that are used instead of traditional displays or projectors when higher brightness or contrast is required. Typical applications are multimedia shows, quality improvement in production facilities or marketing in public areas. The laser animations usually require rapid changes in the output power that are difficult to predict. Our driver is capable of reacting to them by actively pumping the excess power generated by the diodes into the main heatsink of the system. The heat transfer speed is regulated by the driver in real time to maintain an optimum working temperature.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010667
S. Slavov, Andrey Tagarev, Nikola Tulechki, S. Boytcheva
This paper compares different solutions for the task of classifying companies with an industry classification scheme. Recent advances in deep learning methods show better performance in the text classification task. The dataset consists of short textual descriptions of companies and their economic activities. Target classification schemes are built by mapping related open data in a semi-controlled manner. Target classes are built from the bottom up by DBpedia. For the experiments are used modifications of methods BERT, XLNet, Glove and ULMfit with pre-trained models for English. Two simple models with perceptron architecture are used as the baseline. The results show that the best performance for multi-label classification of DBpedia companies abstracts is achieved by BERT and XLnet models, even for unbalanced classes.
{"title":"Company Industry Classification with Neural and Attention-Based Learning Models","authors":"S. Slavov, Andrey Tagarev, Nikola Tulechki, S. Boytcheva","doi":"10.1109/BdKCSE48644.2019.9010667","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010667","url":null,"abstract":"This paper compares different solutions for the task of classifying companies with an industry classification scheme. Recent advances in deep learning methods show better performance in the text classification task. The dataset consists of short textual descriptions of companies and their economic activities. Target classification schemes are built by mapping related open data in a semi-controlled manner. Target classes are built from the bottom up by DBpedia. For the experiments are used modifications of methods BERT, XLNet, Glove and ULMfit with pre-trained models for English. Two simple models with perceptron architecture are used as the baseline. The results show that the best performance for multi-label classification of DBpedia companies abstracts is achieved by BERT and XLnet models, even for unbalanced classes.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/BdKCSE48644.2019.9010607
Willian Dimitrov, Svetlana Syarova
The basic step in the design of a security operations center (SOC) is identifying the necessary functions it needs to perform. The article offers an analysis of the ICS SOC functionalities and is focused to create a part of the concept of operations before the real design of Shared ICS SOC. We offer a complex of functionalities of Shared ICS SOC and analyze their effectiveness. The survey is based on a review of the legal framework, the ICS security incidents, research on the gaps between cybersecurity products and real needs for the ICS and SCADA community. Shared SOC performs role of community service hub with integrated experience, supplying security services for multiple ICS. By outsourcing these services, a company can reduce security staff and focus on its core business.
{"title":"Analysis of the Functionalities of a Shared ICS Security Operations Center","authors":"Willian Dimitrov, Svetlana Syarova","doi":"10.1109/BdKCSE48644.2019.9010607","DOIUrl":"https://doi.org/10.1109/BdKCSE48644.2019.9010607","url":null,"abstract":"The basic step in the design of a security operations center (SOC) is identifying the necessary functions it needs to perform. The article offers an analysis of the ICS SOC functionalities and is focused to create a part of the concept of operations before the real design of Shared ICS SOC. We offer a complex of functionalities of Shared ICS SOC and analyze their effectiveness. The survey is based on a review of the legal framework, the ICS security incidents, research on the gaps between cybersecurity products and real needs for the ICS and SCADA community. Shared SOC performs role of community service hub with integrated experience, supplying security services for multiple ICS. By outsourcing these services, a company can reduce security staff and focus on its core business.","PeriodicalId":206080,"journal":{"name":"2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115694763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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