A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures

This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes

Learning-based Analysis on the Exploitability of Security Vulnerabilities

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing the accuracy of each model, dynamically training the models on a rolling window of training data, and filtering the training data by various features. Of the chosen models, the deep neural network and the support vector machine show the highest accuracy (approximately 94% and 93%, respectively), and could be developed by future researchers into an effective tool for vulnerability analysis

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii

Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization

Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on Twitter include reviews and opinions on certain topics such as movie, book, product, politic, and so on. Based on this condition, this research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the movie is good or not based on message opinion. Support Vector Machine (SVM) is supervised learning methods that analyze data and recognize the patterns that are used for classification. This research concerns on binary classification which is classified into two classes. Those classes are positive and negative. The positive class shows good message opinion; otherwise the negative class shows the bad message opinion of certain movies. This justification is based on the accuracy level of SVM with the validation process uses 10-Fold cross validation and confusion matrix. The hybrid Partical Swarm Optimization (PSO) is used to improve the election of best parameter in order to solve the dual optimization problem. The result shows the improvement of accuracy level from 71.87% to 77%

Data Quality Optimization for Decision Making Using Ataccama Toolkit: A Sustainable Perspective

The world of internet has given us the access to explore the different domains of datasets and its usage. We have lots of heterogeneous data available on the digital platform which is meaningless unless we do not make the valuable use of it. What if we say that we can use these datasets in our need for the business requirements? The critical data can be delivered evenly and shared through master data management and integration techniques. However, given the cross-domain and heterogeneous nature of the data, it is still difficult to assess effectiveness and rationality. In this paper we have developed a pipeline using Ataccama and implemented the way of how we can yield the data, synthesize and optimize it using integration and Master data management (MDM) tools. These tools were assessed based on the performance characteristics and types of data quality problems addressed. We have tried to simplify the complexity and used various dictionaries and lookup along with the ruleset to fetch the required data from the dataset via the MDM application. Profiling the dataset and its validation based on different parameters. In results, it is found that the efficiency and the quality of data has been improved and optimized after using the integration techniques

Altmetrics and Open Access

Altmetrics, in contrast to traditional metrics, measure the societal impact research outputs have on the public in general, using social media platforms as their primary data sources. In this study, differences in Altmetric Scores between open and closed access articles of German research institutions in the field of natural sciences have been analyzed. For this investigation data from the years 2013 to 2017 was gathered from Web of Science, Altmetric.com and Unpaywall. Results indicated that articles published in open access gain higher Altmetric Attention Scores compared to articles behind subscription paywalls, although the difference was statistically not significant. Research outputs published in gold open access had the highest scores, followed by articles in green and then hybrid open access. Furthermore, articles by publishers with higher percentages of open access content gained higher Altmetric Attention Scores than articles distributed by those with medium or low percentages. In a future study additional databases could be included as well as data from years to come. Moreover, a comparable study for the field of humanities would be conceivable, including other document types such as books or contributions in anthologies as well.Altmetrics messen, im Gegensatz zu traditionellen Metriken, den Einfluss von Forschungsergebnissen auf die breite Gesellschaft und nutzen dafür vor allem Social- Media-Plattformen als Datenquelle. In dieser Studie wurden Unterschiede in Altmetric Scores von in Open und Closed Access publizierten Artikeln deutscher Forschungseinrichtungen in den Naturwissenschaften untersucht. Hierfür wurden Daten der Jahre 2013 bis 2017 von Web of Science, Altmetric.com und Unpaywall gesammelt. Die Ergebnisse wiesen darauf hin, dass Artikel in Open Access höhere Altmetric Attention Scores erhalten als Artikel hinter Bezahlschranken. Eine statistische Signifikanz dieser Ergebnisse konnte jedoch nicht nachgewiesen werden. In Gold Open Access publizierte Forschungsergebnisse erreichten die höchsten Werte, gefolgt von in Green und Hybrid Open Access publizierten Artikeln. Zudem wiesen Artikel, die von Verlagen mit hohen Anteilen an Open Access-Inhalten veröffentlicht wurden, höhere Scores auf als jene von Verlagen mit mittleren bis niedrigen Anteilen. In zukünftige umfassendere Studien könnten zusätzliche Datenbanken einbezogen werden sowie Daten aus den kommenden Jahren. Zudem wäre eine vergleichbare Studie für die Geisteswissenschaften denkbar, unter Einbezug weiterer Dokumententypen wie Büchern und Beiträgen in Sammelbänden

Managing Metadata in Data Warehouses: Pitfalls and Possibilities

This paper motivates a comprehensive academic study of metadata and the roles that metadata plays in organizational information systems. While the benefits of metadata and challenges in implementing metadata solutions are widely addressed in practitioner publications, explicit discussion of metadata in academic literature is rare. Metadata, when discussed, is perceived primarily as a technology solution. Integrated management of metadata and its business value are not well addressed. This paper discusses both the benefits offered by and the challenges associated with integrating metadata. It also describes solutions for addressing some of these challenges. The inherent complexity of an integrated metadata repository is demonstrated by reviewing the metadata functionality required in a data warehouse: a decision support environment where its importance is acknowledged. Comparing this required functionality with metadata management functionalities offered by data warehousing software products identifies crucial gaps. Based on these analyses, topics for further research on metadata are proposed