The Research of Intelligence Data Mining Oriented to Battlefield Situation Assessment

2013 ◽  
Vol 427-429 ◽  
pp. 1662-1665
Author(s):  
Jing Xue Liu ◽  
Wei Tang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Warehouse ◽  
Data Warehouses ◽  
Situation Assessment ◽  
Basic Construction ◽  
Holistic Conception

Battlefield situation assessment has a positive significance on improving the efficiency of commanding decision-making; moreover, battlefield situation assessment cannot be made successfully without the support of some integrated and exact intelligence data. In this paper, basing on the demand of identifying the battlefield situation, the corresponding knowledge context database was first discussed; on this basic, construction of the intelligence data warehouses framework was explored. Then, the study of data mining based on the intelligence data warehouse was made from the view of a holistic conception, and a detailed arithmetic was presented by making use of the tactic from data mining driven fishbone.

The Research of Intelligence Data Mining Oriented to Battlefield Situation Assessment

2013 ◽  
Vol 380-384 ◽  
pp. 1282-1285
Author(s):  
Jing Xue Liu ◽  
Wei Tang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Warehouse ◽  
Data Warehouses ◽  
Situation Assessment ◽  
Basic Construction ◽  
Holistic Conception

Battlefield situation assessment (BSA) has a positive significance on improving the efficiency of commanding decision-making, moreover, BSA cannot be made successfully without the support of some integrated and exact intelligence data. In this paper, basing on the demand of identifying the battlefield situation, the corresponding knowledge context database is first discussed; on this basic, construction of the intelligence data warehouses framework is explored. Then, from the view of a holistic conception, the study of data mining based on the intelligence data warehouse is made, and a detailed arithmetic is presented by making use of the tactic from data mining driven fishbone (DMDF).

Data Warehouse Performance

2011 ◽  
pp. 580-585 ◽  
Author(s):  
Beixin ("Betsy") Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Performance Tuning ◽  
Data Warehouses ◽  
User Query ◽  
Data Volume ◽  
Predictive Data Mining ◽  
Support Decision Making ◽  
Over Time

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon et al., 2001). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003). Dramatic increases in data volumes over time and the mixed quality of data can adversely affect the performance of a data warehouse. Some data may become outdated over time and can be mixed with data that are still valid for decision making. In addition, data are often collected to meet potential requirements, but may never be used. Data warehouses also contain external data (e.g. demographic, psychographic, etc.) to support a variety of predictive data mining activities. All these factors contribute to the massive growth of data volume. As a result, even a simple query may become burdensome to process and cause overflowing system indices (Inmon et al., 1998). Thus, exploring the techniques of performance tuning becomes an important subject in data warehouse management.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

2013 ◽  
Vol 9 (2) ◽  
pp. 89-109 ◽  
Author(s):  
Marie-Aude Aufaure ◽  
Alfredo Cuzzocrea ◽  
Cécile Favre ◽  
Patrick Marcel ◽  
Rokia Missaoui
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Query Processing ◽  
Data Warehouse ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Cooperative Query Answering ◽  
User Centric ◽  
Use Of Knowledge

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

Decision-Making Methods in MIS Outsourcing

2011 ◽  
pp. 134-152
Author(s):  
Jeanette Nasem Morgan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Case Studies ◽  
Cost Benefit ◽  
Information Services ◽  
Data Warehouses ◽  
Government Decision ◽  
Trade Offs ◽  
Decision Making Processes

This chapter commences with a discussion of corporate and government decision-making processes and the management sciences that support development of decisions. Special decision-making considerations, trade-offs analyses, and cost-benefit studies all figure into decisions that result in outsourcing. Technologies that support different methods of decision-making include data warehouses and data mining, rules-based logic, heuristical processes, fuzzy logic, and expert-based reasoning are presented. The chapter presents case studies and current and evolving technologies. The following sections will address the decision-making methods that are used in considering, executing and monitoring outsourced MIS projects or in service lines related to provision of information services in the organization.

Data Mining Fundamental Concepts and Critical Issues

2011 ◽  
pp. 418-423
Author(s):  
John Wang ◽  
Qiyang Chen ◽  
James Yao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Critical Issues ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

Web Page Extension of Data Warehouses

2011 ◽  
pp. 2090-2095
Author(s):  
Anthony Scime
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Decision Maker ◽  
Search Engine ◽  
External Information ◽  
Data Warehouses ◽  
Strategic Decisions ◽  
Web Searches ◽  
To Come ◽  
The Web

Data warehouses are constructed to provide valuable and current information for decision-making. Typically this information is derived from the organization’s functional databases. The data warehouse is then providing a consolidated, convenient source of data for the decision-maker. However, the available organizational information may not be sufficient to come to a decision. Information external to the organization is also often necessary for management to arrive at strategic decisions. Such external information may be available on the World Wide Web; and when added to the data warehouse extends decision-making power. The Web can be considered as a large repository of data. This data is on the whole unstructured and must be gathered and extracted to be made into something valuable for the organizational decision maker. To gather this data and place it into the organization’s data warehouse requires an understanding of the data warehouse metadata and the use of Web mining techniques (Laware, 2005). Typically when conducting a search on the Web, a user initiates the search by using a search engine to find documents that refer to the desired subject. This requires the user to define the domain of interest as a keyword or a collection of keywords that can be processed by the search engine. The searcher may not know how to break the domain down, thus limiting the search to the domain name. However, even given the ability to break down the domain and conduct a search, the search results have two significant problems. One, Web searches return information about a very large number of documents. Two, much of the returned information may be marginally relevant or completely irrelevant to the domain. The decision maker may not have time to sift through results to find the meaningful information. A data warehouse that has already found domain relevant Web pages can relieve the decision maker from having to decide on search keywords and having to determine the relevant documents from those found in a search. Such a data warehouse requires previously conducted searches to add Web information.

Marketing Decision and Decision Support System Design Based on Web

2013 ◽  
Vol 850-851 ◽  
pp. 1048-1051
Author(s):  
Guang Yu Peng
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Data Warehouse ◽  
Support System ◽  
Logical Structure ◽  
The Internet ◽  
Marketing Decision ◽  
Online Analysis Processing

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.

scholarly journals CLASSIFICATION DATA EXPLORATION METHODS IN MODERN REALTIME DATA WAREHOUSE

2014 ◽  
pp. 6-12
Author(s):  
Jakub Chłapiński ◽  
Piotr Mazur ◽  
Jan Murlewski ◽  
Marek Kamiński ◽  
Bartosz Sakowicz
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Incremental Learning ◽  
Data Warehousing ◽  
Data Exploration ◽  
Classification Methods ◽  
Data Warehouses ◽  
Learning Methods ◽  
Classification Techniques ◽  
Primary Focus

The goal of this article is to introduce problems that may arise during analysis of classification methods used in data mining applications. In the following sections some of the most common classification techniques are described along with several proposed extensions which allow these methods to be used in incremental data warehouses. The primary focus was aimed at the problem of performing incremental learning methods that may be used in near realtime data warehousing applications.

scholarly journals Construction and functioning for the data warehouse in the enterprise in the context of the effective decision making

Equilibrium ◽  
2009 ◽  
Vol 2 (1) ◽  
pp. 171-180
Author(s):  
Michał Kukliński
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Warehouses ◽  
Economic Decisions ◽  
Business Intelligence Systems ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Answering Questions

In the twenty-four hours of computerised enterprises, recruiting huge amounts of data, processing them in the traditional way would be highly ineffective and it will not deliver to us so much interesting information, forecasts and the relation, as Business Intelligence systems, of which Data Warehouses are a basis. The publication is answering questions: what the data warehouse is what is serving for and what are examples of applying. Stages of the build of the Data Warehouse and factors assuring achieving success in taking economic decisions will be introduced.

scholarly journals Decision making based on data analyses using data warehouses

2018 ◽  
Vol 6 (3) ◽  
pp. 1-6
Author(s):  
Valdrin Haxhiu
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Relational Databases ◽  
Daily Basis ◽  
Transformation Process ◽  
Sql Server ◽  
Data Warehouses ◽  
Data Analyses ◽  
Weak Points ◽  
Using Data

Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making.

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