INTEGRATING CRM SOFTWARE APPLICATIONS

Scientists, end users of CRM applications and producers of CRM software, all come to an agreement when talking about the idea of CRM, the CRM strategy or the term CRM. The main aspect is that CRM can be analyzed from two different points of view: CRM – the marketing strategy and CRM – the software. The first term refers to establishing some personalized relationships with the customers that can be afterwards easily managed. This way, it can determine at any time the past client relations, the products (or services) that the customers bought, the products’ range, the products’ price, the type of delivery, all this information being used in anticipation of the future sales. All these things can be physically achieved through a CRM software application whose main function is to properly manage the information relating to customers and to enable personalized communication with them (via e-mail, phone, fax, mail).CRM, customer, marketing

Optimizing Spatial Databases

This paper describes the best way to improve the optimization of spatial databases: through spatial indexes. The most commune and utilized spatial indexes are R-tree and Quadtree and they are presented, analyzed and compared in this paper. Also there are given a few examples of queries that run in Oracle Spatial and are being supported by an R-tree spatial index. Spatial databases offer special features that can be very helpful when needing to represent such data. But in terms of storage and time costs, spatial data can require a lot of resources. This is why optimizing the database is one of the most important aspects when working with large volumes of data.Spatial Database, Spatial Index, R-tree, Quadtree, Optimization

E-LEARNING STRATEGIES IN THE CONTEXT OF KNOWLEDGE SOCIETY

Given the actual society, called often knowledge society, most organizations are experimenting with some form of computer-assisted instruction, in order to train their employees. This article presents briefly the main characteristics of the knowledge society and of the e-learning systems, and how these two concepts interact and affect each other. Also, there are identified the most important informatics technologies which can be used in the e-learning applications. In order to analyze the actual e-learning systems, some of them are identified and compared based on various criteria.e-learning, Web Based Training, Knowledge Society, informatics technologies

Multi Channel Architecture Model Based on Service Oriented Integration

The volume of data and numerous applications developed within a company can often generate a redundancy difficult to control. In the same time, the homogeneous or heterogeneous management systems of the companies become overcharged for obtaining useful information from databases. For this reason, the organizations develop specialized systems for the integration of existing applications and data. To achieve these systems, there are used a number of technologies, methods and architectures such as SOA architecture. In this article, are presented the components of SOA architecture, its advantages and a solution for integrating applications at the Presentation Tier.application integration, SOA architecture, integration model, architectural levels, information systems.

A model for Business Intelligence Systems’ Development

Often, Business Intelligence Systems (BIS) require historical data or data collected from var-ious sources. The solution is found in data warehouses, which are the main technology used to extract, transform, load and store data in the organizational Business Intelligence projects. The development cycle of a data warehouse involves lots of resources, time, high costs and above all, it is built only for some specific tasks. In this paper, we’ll present some of the aspects of the BI systems’ development such as: architecture, lifecycle, modeling techniques and finally, some evaluation criteria for the system’s performance.BIS (Business Intelligence Systems), Data Warehouses, OLAP (On-Line Analytical Processing), Object-Oriented Modeling

Applications of Spatial Data Using Business Analytics Tools

This paper addresses the possibilities of using spatial data in business analytics tools, with emphasis on SAS software. Various kinds of map data sets containing spatial data are presented and discussed. Examples of map charts illustrating macroeconomic parameters demonstrate the application of spatial data for the creation of map charts in SAS Enterprise Guise. Extended features of map charts are being exemplified by producing charts via SAS programming procedures

Optimizing Spatial Databases

This paper describes the best way to improve the optimization of spatial databases: through spatial indexes. The most commune and utilized spatial indexes are R-tree and Quadtree and they are presented, analyzed and compared in this paper. Also there are given a few examples of queries that run in Oracle Spatial and are being supported by an R-tree spatial index. Spatial databases offer special features that can be very helpful when needing to represent such data. But in terms of storage and time costs, spatial data can require a lot of resources. This is why optimizing the database is one of the most important aspects when working with large volumes of data

A model for Business Intelligence Systems’ Development

Often, Business Intelligence Systems (BIS) require historical data or data collected from var-ious sources. The solution is found in data warehouses, which are the main technology used to extract, transform, load and store data in the organizational Business Intelligence projects. The development cycle of a data warehouse involves lots of resources, time, high costs and above all, it is built only for some specific tasks. In this paper, we’ll present some of the aspects of the BI systems’ development such as: architecture, lifecycle, modeling techniques and finally, some evaluation criteria for the system’s performance

PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context

Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much„ energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to operate unattended. By adding the information collected by sensors managed with powerful technologies such as big data and analytics, the system is able to efficiently react to environmental factors and respond to consumers’ requirements in real time. According to the weather parameters, the output of PV could be symmetric, supplying an asymmetric electricity demand. Thus, a smart adaptive switching module that includes a forecasting component is proposed to improve the symmetry between the PV output and daily load curve. A scaling approach for smaller off-grid systems that provides an accurate forecast of the PV output based on data collected from sensors is developed. The proposed methodology is based on sensor implementation in RES operation and big data technologies are considered for data processing and analytics. In this respect, we analyze data captured from loggers and forecast the PV output with Support Vector Machine (SVM) and linear regression, finding that Root Mean Square Error (RMSE) for prediction is considerably improved when using more parameters in the machine learning process

Internet of Things, Challenges for Demand Side Management

The adoption of any new product means also the apparition of new issues and challenges, and this is especially true when we talk about a mass adoption. The advent of Internet of Things (IoT) devices will be, in the authors of this paper opinion, the largest and the fastest product adoption yet to be seen, as several early sources were predicting a volume of 50 billion IoT devices to be active by 2020 [1][2]. While later forecasts reduced the predicted amount to about 20-30 billion devices [3], even for such “reduced” number, demand side management issues are foreseeable, for the potential economic impact of IoT applications in 2025 will be between 3.9 and $11.1 trillion USD [4]. Not only that new patterns will emerge in energy consumption and Internet traffic, but we predict that the sheer amount of data produced by this quantity of IoT devices will give birth to a new sort of demand side management, the demand side management of IoT data. How will this work is yet to be seen but, at the current moment, one can at least identify the bits and pieces that will constitute it. This paper is intended to serve as short guide regarding the possible challenges raised by the adoption of IoT devices. The data types and structures, lifecycle and patterns will be briefly discussed throughout the following article