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Nowadays, in order to be competitive in the context of dynamic market changes, companies concern themselves with the adoption of novel business models for the optimization of resource consumption. The decision to adopt energy efficiency measures within a company is often hard to substantiate, given the multitude of factors that influence the feasibility of the project, such as regional laws and regulations, ambient weather conditions, energy pricing, company's activity profile, building structure and characteristics, consumption infrastructure and availability of funding sources. The combination of these factors is unique for each company, thus the adoption of any energy efficiency measures, especially when the investment costs are high, should be rigorously evaluated. The purpose of this paper is to present the conceptual model of a Cloud-based energy management platform that aims to help SMEs monitor energy consumption and associated costs in real time, analyze consumption patterns, assess the economic efficiency of EPCbased projects, benefit from recommendations, generate energy reports in compliance with international standards and find suitable business partners in the field of energy.

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The current information age has led, globally, to an exponential increase regarding the availability and the use of the information, both structured and unstructured, a phenomenon known as Big Data. The term Big Data refers not only to the massive volume and variety of data itself, but also to the set of technologies that surround it, in order to collect, store, retrieve, manage, process and analyze data in order to solve complex problems in society, respectively for increasing the quality of life in all its aspects. Given that approximately 80% of the data generated daily has a spatial component, and studies indicate that more than 150 zettabytes (150 trillion gigabytes) of data will require analysis by 2025, it is necessary to create Big Data solutions for storage, organizing, manipulating, viewing, and retrieving relevant information. Today, in the midst of the �data revolution�, more and more countries are launching ambitious programs aimed at developing their use. These programs test the ability of decisionmakers to recognize, structure and exploit data, which is considered a valuable resource, and create the means to generate value from it by facilitating access. The Big Data phenomenon has also conquered the military field, in which the current and emerging object of large-scale data analysis areas is the exploitation of classical techniques such as rule-based systems, shape analysis, tree structures and other analysis technologies in order to develop efficient tools. In this paper we will start from the investigation of the basic characteristics of Big Data and continued with technical details that involves the generation, collection, storage and analysis of geospatial Big Data needed to transform these data into an actionable intelligence.

Nowadays data are generated by different sources, at an incredible rate, and the traditional approaches for their collection, storage and analysis are not suitable. Big Data is analyzed and used by state institutions, business environment, transportation, health, communications, banking system, utilities, defense and other components of modern society in order to support their decisions as well as the human activities. Geospatial data is an important component of Big Data and aerial/satellite images offer a lot of details about the environment, events and their evolution in time. This paper presents the significant techniques used in three main stages of Geospatial Big Data lifecycle, namely data collection, storage and analysis. Geospatial data collections are mainly executed by using web crawlers in order to find meaningful data and, during this stage some preprocessing operations can be done (standardization, completion and integration). Cloud storage and distributed file systems are widely used for Geospatial Big Data storage and new types of non-relational and relational databases are developed. The very challenging aspects for Big Data analysis are related to feature identification and extraction from aerial or satellite images, using feature-based extraction and deep learning algorithms.

Nowadays, most organizations that aim to optimize their energy consumption are experiencing a lack of Business Intelligence (BI) solutions for supporting their decision-making process in order to implement viable enterprise systems for energy-efficiency. BI technologies can include data storage, querying, data processing, rule-based simulation, and advanced visualization capabilities, including processing data from the Internet of Things (IoT) monitoring platforms. Thus, most of companies face different challenges in monitoring the energy consumption inside buildings since each company has to face an unique combination of factors which needs to be properly evaluated. Another degree of complexity is added if the company uses energy production solutions since the reliability of the investment is influenced by the energy demand of the company, changes in the consumption patterns, local weather conditions and unforeseen costs related to the exploitation of the equipment. This paper provides an example of a Cloud-based BI energy monitoring and simulation platform which aims to help organizations monitor and forecast the energy production and consumption within a building and to simulate the economic efficiency for multiple investment scenarios. Also, it presents an overview on the Verbund device, which was used to measure the power consumption inside the building, and MQTT (Message Queuing Telemetry Transport) protocol which was used to collect the data from the device and to display data in the Grafana dashboard.