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The ever-growing population of smartphone» connected to mobile networks is changing the cellular traffic ecosystem. The traffic volumes and patterns generated by smartphone apps pose complex challenges to cellular network operators, particularly in terms of detection and diagnosis of network anomalies caused by specific apps. The high-dimensionality of network data provided by current network monitoring...
Property-graphs are becoming popular for Intrusion Detection Systems (IDSs) because they allow to leverage distributed graph processing platforms in order to identify malicious network traffic patterns. However, a benchmark for studying their performance when operating on big data has not yet been reported. In general, benchmarking a system involves the execution of workloads on datasets, where both...
The need of smart information retrieval systems is in contrast with the difficulties to deal with huge amount of data. In this paper we present a Big Data Analytics architecture used to implement a semantic similarity search tool for natural language texts in biomedical domain. The implemented methodology is based on Word Embeddings (WEs) models obtained using the word2vec algorithm. The system has...
The application of the Internet of things technology brings opportunity for modern dam safety intelligent inspection. Aiming at the present situation of reservoir dam safety inspection data, information island serious deficiencies and poor timeliness problem, the research applied big data thoughts into water management to solve the numerous data, demand diversifications of related interest groups,...
In big data research related to bioinformatics, one of the most critical areas is proteomics. In this paper, we focus on the protein-protein interactions, especially on pathogen-host protein-protein interactions (PHPPIs), which reveals the critical molecular process in biology. Conventionally, biologists apply in-lab methods, including small-scale biochemical, biophysical, genetic experiments and...
Open Science Big Data is emerging as an important area of research and software development. Although there are several high quality frameworks for Big Data, additional capabilities are needed for Open Science Big Data. These include data provenance, citable reusable data, data sources providing links to research literature, relationships to other data and theories, transparent analysis/reproducibility,...
Current procedure in travel demand estimation models is to separately deal with attraction, production and trip distribution, where the latter typically assumes inverse distance proportionality. We show that this procedure leads to errors in the demand estimation, particularly when dealing with very specific zones and heterogeneous travel behavior. We argue that this traditional procedure is rooted...
The idea of this research is to explore through the web pages how the concept of big data and chronotopos affect the digital press and journalistic company. The theoretical framework tackles both concepts through information theory, network theory and collects the opinions of some authors in different disciplines. The main perspective leads to a theoretical review of the definitions that can be considered...
Water scarcity is one of the serious problems that California is facing today. Water scarcity leads to doughtiness when not properly addressed. In the recent years, California faces a serious drought problem. This provides a strong demand in building a real-time system to support water resources analysis, drought modeling and prediction. Existing models and approaches lack of desirable accuracy in...
A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are accumulated over historical data sets and set up the seed for future Big Data processing, with the final goal to generate added value and discover knowledge. In such computing processes, data are the main resource, however, organizing...
In an Information technology world, the ability to effectively process massive datasets has become integral to a broad range of scientific and other academic disciplines. We are living in an era of data deluge and as a result, the term “Big Data” is appearing in many contexts. It ranges from meteorology, genomics, complex physics simulations, biological and environmental research, finance and business...
The digital transformation enables new business models and enhanced business processes by utilizing available data for analytics, prediction, and decision support. We give an overview of the enabling developments for the digital transformation, the areas of application, and concrete use case examples. We summarize our findings in a framework for the digital transformation and discuss the potential...
Data Science is an emerging field of science, which requires a multi-disciplinary approach and should be built with a strong link to emerging Big Data and data driven technologies, and consequently needs re-thinking and re-design of both traditional educational models and existing courses. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model...
Artificial Intelligence (AI) is a fast developing area that is applied to many daily problems, replacing the tried and true heuristics used by society for a long time. This paper provides a framework to estimate the value added by different steps and components used to create and apply AI models. Such estimations are useful to decide if deploying AI models makes economical sense, reported to the specific...
High performance distributed computing environments have traditionally been designed to meet the compute demands of scientific applications, supercomputers have historically been producers and not consumers of data. The Apache Hadoop ecosystem has evolved to address many of the traditional limitations of HPC platforms. There exist a whole class of scientific applications that need the collective capabilities...
Over the last decade, critical infrastructures have become increasingly complex. They now possess levels of automation which requires the integration of, often, mutually incompatible technologies. In addition, the data sets generated are t, vast and intricate level of interdependency between infrastructures has grown. Any failures, caused by cyber-attacks, have the ability to spread through a system...
The landscape of distributed computing is rapidly evolving, with computers exhibiting increasing processing capabilities with many-core architectures. Almost every field of science is now data driven and requires analysis of massive datasets. The algorithms for analytics such as machine learning can be used to discover properties of a given dataset and make predictions based on it. However, there...
The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, and so on. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data and communicate their findings effectively...
With the recent development of computertechnology, the high frequency financial big data have beengenerated timely and more conveniently. However, theparticularity of high-frequency big data has raised a number ofmajor challenges for data analysis. The existing mathematicalmodels that were designed for analyzing daily financial data mayno longer be suitable for studying high-frequency big data. Totackle...
Driven by innovations such as mass customisation, complex supply chains, smart cities and emerging cyber-physical and Internet of Things systems, Big Data is presenting a fascinating range of challenges to Analytics. New fields are emerging such as Big Data Analytics and Data Science. Modeling & Simulation (M&S) is core to Analytics. Arguably, contemporary M&S practices cannot deal with...
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