Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification

There are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selection process of online network-traffic discriminators. We obtained 24 traffic features that can be processed on the fly and propose them as a base attribute set for future domain-aware online analysis, processing, or classification. For the selection of a set of traffic discriminators, and to avoid the inconveniences mentioned, we carried out three steps. The first step is a context knowledge-based manual selection of traffic features that meet the condition of being obtained on the fly from the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the implementation of several incremental learning algorithms verified the usefulness of such attributes in online traffic classification processes.

Triangulum City Dashboard: An Interactive Data Analytic Platform for Visualizing Smart City Performance

Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its core services to the public in a remarkably efficient manner. City dashboards have drawn increasing interest from both city operators and citizens. Dashboards can gather, visualize, analyze, and inform regional performance to support the sustainable development of smart cities. They provide useful tools for evaluating and facilitating urban infrastructure components and services. This work proposes an interactive web-based data visualization and data analytics toolkit supported by big data aggregation tools. The system proposed is a cloud-based prototype that supports visualization and real-time monitoring of city trends while processing and displaying large data sets on a standard web browser. However, it is capable of supporting online analysis processing by answering analytical queries and producing graphics from multiple resources. The aim of this platform is to improve communication between users and urban service providers and to give citizens an overall view of the city’s state. The conceptual framework and architecture of the proposed platform are explored, highlighting design challenges and providing insight into the development of smart cities. Moreover, results and the potential statistical analysis of important city services offered by the system are introduced. Finally, we present some challenges and opportunities identified through the development of the city data platform.

Marketing Decision and Decision Support System Design Based on Web

This paper analyzes the DSS characteristics about the marketing under the internet as well as the influencing factors of the market decisions, Studying the decision-making functions of marketing decision support system DSS. It proposed the marketing DSS design, logical structure and its implementation based on a data warehouse as the center, online analysis processing and data mining as a means.

Application Research of Computer Technology in the Decision Making of China's Supermarket Logistics Centers

Logistics centers are mainly comprised by a series of operations from purchasing of goods to preservation, allocation, delivery, packaging and distribution. By drawing support from advanced management technology and modernized information exchange network, logistics centers have effectively connected production and consumption altogether, making all goods circulating under an efficient, harmonious and ordered state, which in return achieved best commercial efficiency and social efficiency. Currently, logistics management software is far from enough to satisfy customers' needs, and still remains on the level of purchase, sales and inventory management. This research is conducted in allusion to the actual situation of China's information-based logistics industry, with the objective to improve modern management level of logistics industry. According to the demands of domestic logistics enterprises, we employed OLAP technology for data processing, and data mining technology in decision-making analysis, which realized information-based management in logistics centers. On this basis, we realized basic purchase, sales and inventory management. Furthermore, we as well created the data warehouse, so as to perform online analysis, processing and data mining, to realize intensive processing of data, as well as to provide support for decision-making. Computer-aided decision-making software is the development direction of management software.

Spatial Data in Multidimensional Conceptual Models

Data warehouses (DWs) are used for storing and analyzing high volumes of historical data. The structure of DWs is usually represented as a star schema consisting of fact and dimension tables. A fact table contains numeric data called measures (e.g., quantity). Dimensions are used for exploring measures from different analysis perspectives (e.g., according to products). They usually contain hierarchies required for online analysis processing (OLAP) systems in order to dynamically manipulate DW data. While traversing hierarchy, two operations can be executed: the roll-up operation, which transforms detailed measures into aggregated data (e.g., daily into monthly sales); and the drill-down operation, which does the opposite.