Overview of Business Intelligence through Data Mining

2016 ◽  
pp. 49-72 ◽  
Author(s):  
Abdulrahman R. Alazemi ◽  
Abdulaziz R. Alazemi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Information Technologies ◽  
Customer Relationships ◽  
The Internet ◽  
Competitive Advantages ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Internet Business ◽  
Future Outcomes

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

Overview of Business Intelligence through Data Mining

2014 ◽  
pp. 272-295
Author(s):  
Abdulrahman R. Alazemi ◽  
Abdulaziz R. Alazemi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Information Technologies ◽  
Customer Relationships ◽  
The Internet ◽  
Competitive Advantages ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Internet Business ◽  
Future Outcomes

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

An Introduction to Information Technology and Business Intelligence

2011 ◽  
pp. 1-21
Author(s):  
Stephan Kudyba ◽  
Richard Hoptroff
Keyword(s):  
Information Technology ◽  
Business Intelligence ◽  
Information Technologies ◽  
State Of The Art ◽  
The Internet ◽  
Information Economy ◽  
Goods And Services ◽  
Software Applications ◽  
The World ◽  
Industry Sectors

The world of commerce has undergone a transformation since the early 1990s, which has increasingly included the utilization of information technologies by firms across industry sectors in order to achieve greater productivity and profitability. In other words, through use of such technologies as mainframes, PCs, telecommunications, state-of-the-art software applications and the Internet, corporations seek to utilize productive resources in a way that augment the efficiency with which they provide the most appropriate mix of goods and services to their ultimate consumer. This process has provided the backbone to the evolution of the information economy which has included increased investment in information technology (IT), the demand for IT labor and the initiation of such new paradigms as e-commerce.

scholarly journals Business Intelligence and competition ability of enterprise

2005 ◽  
Vol 53 (6) ◽  
pp. 85-92
Author(s):  
Vladimír Konečný ◽  
Ivana Rábová
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information And Communication Technologies ◽  
Business Intelligence ◽  
Information Technologies ◽  
Transformation Process ◽  
Strategic Decision ◽  
New Information ◽  
Information And Communication ◽  
Olap Analysis

As far as the current state of the information and communication technologies usage is concerned, the information systems of the companies cover the major part of the transaction processes and the large amount of the processes at the level of the tactical decision-making.Intensive implementation of the information technologies in many areas of the human activities cause gathering of the large amount of the data. The volume of the internal and external databases grows rapidly and the problem is to take advantage of the data they contain. But the problem is not only the growing volume of the databases but also the different and database structures. To get the new information from the large and incompatible database sources is possible but very inefficient. A manager often needs the information very fast to achieve competitive advantage and to solve problems at the level of strategic decision-making. Another problem is the fact that the databases often contain information that is hidden there and there is no way known how to get this information out of the database. In this case, the user needs at least suitable tools in order to perform experiments and to explore and identify patterns and relationships in the data.The transformation process of the data to information and to knowledge that is used in the process of decision-making is called Business Intelligence. Modern database tools offer wide support for building the data warehouse, OLAP analysis and data mining.Our contribution focuses on the application of one of the data mining techniques such as neural networks and artificial intelligence. The application of those methods will be based on the assessment of the food quality and composing of the corresponding trend indicator.

scholarly journals Robust Face Recognition for Data Mining

2008 ◽  
pp. 3621-3629
Author(s):  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Data Mining ◽  
Interactive Television ◽  
Multimedia Data ◽  
The Internet ◽  
Data Types ◽  
Text Documents ◽  
Text Data ◽  
Internet Backbone ◽  
Large Databases ◽  
Robust Face Recognition

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the Internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata, such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them — and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, post-production of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the Internet backbone.

A Semantic Sensor Web Architecture in the Internet of Things

2019 ◽  
pp. 75-97
Author(s):  
Eliot Bytyçi ◽  
Besmir Sejdiu ◽  
Arten Avdiu ◽  
Lule Ahmedi
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Sensor Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Web Architecture ◽  
Hidden Knowledge ◽  
Semantic Sensor Web ◽  
The Internet Of Things ◽  
The Web

The Internet of Things (IoT) vision is connecting uniquely identifiable devices to the internet, best described through ontologies. Furthermore, new emerging technologies such as wireless sensor networks (WSN) are recognized as essential enabling component of the IoT today. Hence, the interest is to provide linked sensor data through the web either following the semantic web enablement (SWE) standard or the linked data approach. Likewise, a need exists to explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture has been developed. It supports linking sensors, other devices and people via a single web by mean of a device-person-activity (DPA) ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and linked WSN data. The architecture could be easily extensible to capture semantics of input sensor data from other domains as well.

The Macroeconomic Benefits of Intelligent Enterprise

2011 ◽  
pp. 71-86
Author(s):  
Thomas F. Siems
Keyword(s):  
Productivity Growth ◽  
Durable Goods ◽  
Information Technologies ◽  
The Internet ◽  
Competitive Markets ◽  
New Information ◽  
Demand Volatility ◽  
Internet Business ◽  
The U.S

New information technologies, including e-commerce and the Internet, have brought fundamental changes to 21s t century businesses by making more and better information available quickly and inexpensively. Intelligent enterprises are those firms that make the most from new information technologies and Internet business solutions to increase revenue and productivity, hold down costs, and expand markets and opportunities. In this chapter, the macroeconomic benefits that intelligent enterprises can have on the U.S. economy are explored. We find that the U.S. economy has become less volatile, with demand volatility nearly matching sales volatility, particularly in the durable goods sector. Evidence also suggests that firms are utilizing new information technologies to lower inventory levels relative to sales, leading to higher productivity growth, lower prices, and more competitive markets.

Data Mining-Based CBIR System

2018 ◽  
pp. 440-457
Author(s):  
Shruti Kohli ◽  
Vijay Shankar Gupta
Keyword(s):  
Data Mining ◽  
Social Network Sites ◽  
Image Databases ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Multimedia Networks ◽  
Useful Knowledge ◽  
Large Databases ◽  
Efficient Retrieval

Multimedia mining primarily involves information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution (STI). Content Based Image Retrieval (CBIR) is the efficient retrieval of relevant images from large databases based on features extracted from the image. The emergence and proliferation of social network sites such as Facebook, Twitter and LinkedIn and other multimedia networks such as Flickr has further accelerated the need of efficient CBIR systems. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging task. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The need of the day is New Image Mining techniques need to be explored and a self-adaptable CBIR system needs to be developed.

The Macroeconomic Benefits of Intelligent Enterprises

2011 ◽  
pp. 11-28
Author(s):  
Thomas F. Siems
Keyword(s):  
21St Century ◽  
Productivity Growth ◽  
Durable Goods ◽  
Information Technologies ◽  
The Internet ◽  
Competitive Markets ◽  
New Information ◽  
Demand Volatility ◽  
Internet Business ◽  
The U.S

New information technologies, including e-commerce and the Internet, have brought fundamental changes to 21st century businesses by making more and better information available quickly and inexpensively. Intelligent enterprises are those firms that make the most from new information technologies and Internet business solutions to increase revenue and productivity, hold down costs, and expand markets and opportunities. In this chapter, the macroeconomic benefits that intelligent enterprises can have on the U.S. economy are explored. We find that the U.S. economy has become less volatile, with demand volatility nearly matching sales volatility, particularly in the durable goods sector. Evidence also suggests that firms are utilizing new information technologies to lower inventory levels relative to sales, leading to higher productivity growth, lower prices, and more competitive markets.

An Introduction to Information Technology and Business Intelligence

2001 ◽  
pp. 2-22
Author(s):  
Stephan Kudyba ◽  
Richard Hoptroff
Keyword(s):  
Information Technology ◽  
Business Intelligence ◽  
Information Technologies ◽  
State Of The Art ◽  
The Internet ◽  
Information Economy ◽  
Goods And Services ◽  
Software Applications ◽  
The World ◽  
Industry Sectors

The world of commerce has undergone a transformation since the early 1990s, which has increasingly included the utilization of information technologies by firms across industry sectors in order to achieve greater productivity and profitability. In other words, through use of such technologies as mainframes, PCs, telecommunications, state-of-the-art software applications and the Internet, corporations seek to utilize productive resources in a way that augment the efficiency with which they provide the most appropriate mix of goods and services to their ultimate consumer. This process has provided the backbone to the evolution of the information economy which has included increased investment in information technology (IT), the demand for IT labor and the initiation of such new paradigms as e-commerce.

Computational Business Intelligence, Big Data, and Their Role in Business Decisions in the Age of the Internet of Things

2017 ◽  
pp. 249-268 ◽  
Author(s):  
Javier Vidal-García ◽  
Marta Vidal ◽  
Rafael Hernandez Barros
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Business Intelligence ◽  
The Internet ◽  
Self Assessment ◽  
Business Decisions ◽  
Business Operations ◽  
Large Databases ◽  
The Social ◽  
The Internet Of Things

The evolution of the big data and new techniques related to the processing and analysis of large databases is revolutionizing the management of companies in the age of the Internet of Things (IoT). In this chapter, we examine the possibilities of big data to improve the services offered by companies and the customer experience and increase the efficiency of these companies. Companies must accept the challenge of self-assessment and measure the barriers that threaten to prevent them from reaching to get the maximum potential derived from big data and analytics. The combination of big data and computational business intelligence will change completely processes, logistics and distribution strategies, the choice of marketing channels and any aspect of the production and marketing of products and services. A case of GE is presented to showcase the use of the IoT and big data. All companies, regardless of size or sector, will improve their business operations due to big data generated from the social media and IoT applications and its use in computational business intelligence.

Sign in / Sign up

Export Citation Format

Share Document