Data analysis has become an important source of knowledge for organizations.An adequate treatment allows to obtain valuable information.Its massive processing is possible from Big Data technologies.The work is based on the use of an open source platform for the processing of files generated by the communication systems of a mass service institution with three hundred branches in the country that serves more than two million customers.The research addresses the need to consolidate results based on indicators that add value to decision-making and management processes to improve the operational efficiency of information and communication technology (ICT) services.The objective is the development of a control panel based on measurement of different indicators.This allow the monitoring of its operating costs and the level of quality of customer care.For this, the ELK TM (Elasticsearch-Logstash-Kibana) set is used, fed with the call detail records known as CDR (Call Detail Records).
This document presents a line of doctoral research that proposes a cybersecurity strategy that has not been formally standardized up to date, based on knowledge of defense intelligence operations, and applying a combination of static and dynamic approaches, in a context of threat risk, anticipating its effectiveness. In this way, changing the current approach, leaving aside the old concept of "walled" defense, for a more innovative one, where information collectors or "spies" infiltrate "unknown terrain" or external networks to extract data and information, learn from context, analyze and detect patterns, be willing to share the knowledge, and then be able to make defensive deterrent, or offensive decisions in real-time.
Self-healing is an autonomic computing fundamental well-disseminated in standalone computer systems. In distributed systems, e.g. computer networks or mobile networks, the introduction of self-healing capabilities poses some challenges, mainly when software-based networks, e.g. Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV), are involved. Such networks impose new control and management layers, and the adoption of self-healing functions means that all layers must be considered. In this paper, we present the challenges of self-healing in the scope of SDN and NFV, by revising the self-healing concept in computer and mobile networks, and by presenting the thorough difference between a system that applies fault tolerance from one that applies self-healing functions. We also introduce a framework for solving these challenges, by describing four use cases of self-healing, considering control, management, and data layers. The use cases focus on maintaining the health of the network at run-time, considering control, management, and infrastructure layers. Our framework is a novel Operations, Administration, and Maintenance (OAM) tool, based on a self-management network architecture that was introduced in our previous works.
With the ever-growing volume of data from inside and outside of an organization, data-driven decision-making techniques can now be used more often and in more areas than before. IT departments usually do not have the resources to support and implement all requests from users from different departments. Consequently, end-user must be enabled to do activities in the area of business intelligence (BI), statistical analysis, or data science by themselves. This user-centric approach requires an easy-to-use access to the tools, combined with a high availability of the tools or relevant data sources. These tools are called Self-service BI (SSBI) tools. The recent developments in cloud computing support permanent access to SSBI-tools from anywhere, anytime, and from any device. Furthermore, the cloud provides an enormous pool of data that can be used for data analysis, and expanded by corporate data. This paper focusses on cloud-based SSBI tools and their role in data-driven decision-making. This empirical study aims to identify the influence of a deeper understanding of business informatics on (a) the handling of the cloud-based SSBI tools and (b) the data-driven decision making performance. Based on different backgrounds in business informatics, the results show no significant difference in the tool handling, but the decision-making performance is significantly different.
It is the concept of Big data and a variety of domains. The purpose is to encapsulate the structures of big data, examination methods, applications, and experiments in healthcare. Healthcare in big data has its structures, such as heterogeneity, privacy, and ownership, longevity. These structures convey a series of experiments for mining and promote involvement in health-related research. To analysis focusing the approaches on big data and deal with these challenges in healthcare, regulations, and laws need to create for big data in healthcare and identifies and enacted all the possible challenges benefits the realizing of health care. Different healthcare as well as in big data including predictive analytics, prescriptive analytics, and data analytics. A big data presentation of big data study and patient perspective enhanced dealing, and subordinate budgets. In accumulation to government, research, patients, also hospitals advantage from the institutions of big data in health care.
