2 based chapter decision knowledge pdf support system

A Model-Driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-Driven DSS use data and parameters provided by DSS users to aid decision makers in analyzing a situation, but they are not necessarily data intensive.

Document-Driven DSS manage, retrieve and manipulate unstructured information in a variety of electronic formats. Finally, Knowledge-Driven DSS provide specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.

Enterprise-wide DSS are linked to large data warehouses and serve many managers in a company. We recommend reading the first Chapter of Power Architectures Once again, different authors identify different components in a DSS. Building upon the various existing architectures, Marakas proposes a generalized architecture made of five distinct parts: a the data management system, b the model management system, c the knowledge engine, d the user interface, and e the user s Figure 2.

Figure 2. Decision support systems : current practice and continuing challenges. Reading, Mass. Druzdzel, M. Flynn Encyclopedia of Library and Information Science. Kent, Marcel Dekker, Inc. Finlay, P. Introducing decision support systems.

Oxford, UK Cambridge, Mass. Haettenschwiler, P. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: Decision support systems: a research perspective. Decision support systems : issues and challenges. Fick and R. Oxford ; New York, Pergamon Press. Keen, P. Scott Morton Decision support systems : an organizational perspective. Marakas, G. Instant access upon order completion.

Free Content. More Information. Villazon-Terrazas, B. Management Association Ed. IGI Global. Available In. DOI: Current Special Offers. No Current Special Offers. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes i theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and ii empirical knowledge extracted from real patient electronic health records.

The empirical knowledge is encoded in an empirical knowledge graph EKG. One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge. Knowledge components objects are cataloged and stored in the knowledge warehouse for reuse by reporting, documentation, execution the knowledge or query and reassembling which are accomplished and organized by instructional designers or technical writers.

The idea of knowledge warehouse is similar to that of data warehouse. As in the data warehouse, the knowledge warehouse also provides answers for ad-hoc queries, and knowledge in the knowledge warehouse can reside in several physical places [10].

Where data DSS and it consists of several phases as shown in the comes in, possibly from many sources. It is integrated figure 2. These phases are: and placed in some common data store like data 1. Collect data from different sources, these sources warehouse. Part of it is then selected and pre-processed can be different files such as Excel, Access, Word, into a standard format. These are then interpreted to give new and a Data integration potentially useful knowledge.

Although the data mining b Data reduction Year algorithms are central to knowledge discovery, they are not the whole story. The pre-processing of the data and c Data consistency the interpretation of the results are both of great 3. Loading the cleaning data after performing importance [13].

Data selection for knowledge discovery phase 37 V. Data Mining Technique 5. Knowledge discovery by applying Data Mining and association rule mining task in particular.

Interpret the association rules to discover and gain knowledge for intended enterprise. Data mining derives its name from the 7. Represent the result which is knowledge using one similarities between searching for valuable information in of the visualization tools a large database and mining rocks for a vein of valuable 8. Make decisions by investment and benefit from the ore. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information [15].

Data mining is the process of discovering interesting knowledge, such as patterns , associations, changes, anomalies, and significant structures from large amount of data stored in databases, data warehouse, or other information repositories [16]. Data mining is defined as the extraction of patterns or models from observed data [12]. Data Mining, also popularly known as Knowledge Discovery in Databases KDD , refers to the Figure 2 : The Proposed knowledge-driven DSS System nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases.

In the first step of the proposed system which is While data mining and knowledge discovery in Data Gathering and Integrating phase , we have databases or KDD are frequently treated as synonyms, collected data about items sales of a building items data mining is actually part of the knowledge discovery market from several sources and files such as text file, process [14]. Where collecting data to improve its marketing, sales, and customer support from different sources usually presents many operations through a better understanding of its challenges, because different departments will use customers.

Data mining, transforms data into actionable different styles of record keeping, different conventions, results [17]. Other similar terms referring to data mining different time periods, different degrees of data are: data dredging, knowledge extraction and pattern aggregation, different primary keys, and will have discovery [14].

In our proposed system, integration step led to The discovered knowledge in our system refers emerging duplicated records transactions and to the predicted ratios of sales for the items during a inconsistent attributes which are processed in the data specified month in the next year based on statistic pre-processing phase by applying proposed algorithms analysis applied on items' sales through the previous of reduction and consistency techniques that are years stored in our marketing Data Warehouse DW.

Removing Duplication Reduction Algorithm and Figure 3 shows the discovered knowledge. Resolving Inconsistency Algorithm. The cleaned and prepared data from pre-processing phase are loaded into the data warehouse DW which is a wide data store Year of the market that contains historical data and complete information about building items and has capability of modifying its data and ready for processing phase. In order to mine vast amounts of data in the data 38 warehouse for discovering knowledge, part of the data should be selected and customized in the Data Selection phase, where we use the concept of data mart Global Journal of Management and Business Research E Volume XIII Issue X Version I to select and customize the data for processing phase depending on the technique used for knowledge discovery.

In Data Selection phase the set of items is selected for Data Mining and as input of the proposed Index-based Apriori Algorithm because the used technique is Data Mining and specifically the Association functionality. In the discovering knowledge phase, we use Data Mining and apply its Association functionality.

The selected set of items is entered to the proposed algorithm Index-based Apriori for mining association rules. The number of mining association rules are different based on specified and entered min.

The market manager to be able of taking decisions and managing the market resources, The visualized results that have been illustrated these rules must be interpreted for discovering clearly in figures 4 , 5 , 6 are for "January" of the next knowledge to support the process of decision making. It is important to mention that these predicted In the Association Rules Interpretation phase, ratios of items sales are being different by differing the we proposed and used an algorithm named min.

Therefore, we executed our system substituting and counting the items in the antecedent and got various results ratios for "January" using three and consequent of the association rules.