research progect for e commerce course at master level Amirkabir University of Technology - Tehran Polytechnic
-professor mehdi shajari and engineer morteza javan
A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized, and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.
The document discusses web design standards and ethics in web development. It introduces the World Wide Web Consortium (W3C) which sets standards for web development to ensure the long-term growth of the web. The W3C's mission is outlined. Web design standards that ensure fast loading, mobile readiness, SEO optimization, and conversion are covered. The document also discusses ethics and professional conduct for web developers, including treating others with respect, avoiding discrimination or harassment, respecting privacy, and promoting an inclusive work environment.
research progect for e commerce course at master level Amirkabir University of Technology - Tehran Polytechnic
-professor mehdi shajari and engineer morteza javan
A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized, and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.
The document discusses web design standards and ethics in web development. It introduces the World Wide Web Consortium (W3C) which sets standards for web development to ensure the long-term growth of the web. The W3C's mission is outlined. Web design standards that ensure fast loading, mobile readiness, SEO optimization, and conversion are covered. The document also discusses ethics and professional conduct for web developers, including treating others with respect, avoiding discrimination or harassment, respecting privacy, and promoting an inclusive work environment.
Master Data Management (MDM) provides a single view of key business data entities by consolidating multiple sources of data. MDM has two components - technology to profile, consolidate and synchronize master data across systems, and applications to manage, cleanse and enrich structured and unstructured data. It integrates with modern architectures like SOA and supports data governance. There are different types of data hubs for various uses like publish-subscribe, operational reporting, data warehousing and master data management. Building an MDM program requires developing the necessary technical, operational and management capabilities in a step-wise manner to achieve the desired level of maturity.
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Master data management and data warehousingZahra Mansoori
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This document discusses master data management (MDM) and its role in data warehousing. It describes how MDM can consolidate and cleanse master data from various transactional systems to create a single version of truth. This unified master data is then used to support both operational and analytical initiatives. The document also provides an overview of key components of a data warehouse, including the extraction, transformation, and loading of data from operational systems. It notes that the ideal information architecture places an MDM component between operational and analytical systems to ensure consistent, high-quality master data is available throughout the organization.
General Packet Radio Service (GPRS) is a data service for GSM networks that provides transmission speeds up to 160 Kbps. GPRS uses a packet-based wireless communication technology and provides continuous connection to the Internet for mobile phone users. The key components of GPRS architecture include the Mobile Station (MS), Base Transceiver Station (BTS), Base Station Controller (BSC), Mobile Switching Center (MSC), Home Location Register (HLR), Serving GPRS Support Node (SGSN), and Gateway GPRS Support Node (GGSN). Together, these components allow mobile users to send and receive data such as emails and web pages over GSM networks.
This document summarizes an evaluation of texture feature extraction methods for content-based image retrieval, including co-occurrence matrices, Tamura features, and Gabor filters. The evaluation tested these methods on a Corel image collection using Manhattan distance as the similarity measure. Co-occurrence matrices performed best with homogeneity as the feature, while Gabor wavelets showed better performance for homogeneous textures of fixed sizes. Tamura features performed poorly with directionality. Overall, co-occurrence matrices provided the best results for general texture retrieval.
Content-based Image Retrieval Using The knowledge of Color, Texture in Binary...Zahra Mansoori
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This document presents a new approach for content-based image retrieval that combines color, texture, and a binary tree structure to describe images and their features. Color histograms in HSV color space and wavelet texture features are extracted as low-level features. A binary tree partitions each image into regions based on color and represents higher-level spatial relationships. The performance of the proposed system is evaluated on a subset of the COREL image database and compared to the SIMPLIcity image retrieval system. Experimental results show the proposed system has better retrieval performance than SIMPLIcity in some categories and comparable performance in others.
This document discusses customer relationship management (CRM) and campaign management. It defines operational, analytical, and collaborative CRM and explains how they are related. It also outlines 10 common CRM functionalities like lead management, account management, and sales activity tracking. Additionally, it defines campaign management and differentiates between inbound and outbound marketing as well as multi-channel and cross-channel marketing. Finally, it references various sources for additional information.
Master data management (MDM) involves managing core business entities that are used across many business processes and systems. These entities include customers, products, suppliers, and more. MDM provides a single source of truth for key business data and ensures consistency. There are different domains of MDM, including customer data integration which manages party data, and product information management which manages product definitions. MDM systems can be used collaboratively to achieve agreement on topics, operationally as transaction systems, or for analytics on the managed data. Common implementation styles include registry, consolidation, transactional hub, and coexistence. MDM systems include repositories to store master data, services to manage it, and integration with other systems and applications.
This document provides an overview of business intelligence (BI). It discusses the evolution of BI from traditional decision support systems to current approaches like the Inmon top-down model and Kimball bottom-up model. It also covers BI concepts like the data warehouse, data marts, ETL process, OLAP, dimensions and facts. Common BI techniques like reporting, dashboards, and algorithms for regression analysis, decision trees, association analysis and cluster analysis are also summarized.