Role Mining - Revealing Business Roles for Security Administration using Data Mining Technology Martin Kuhlmann Dalia Shohat SYSTOR Security Solutions GmbH Hermann-Heinrich-Gossen-Strasse 3 D 50858 Cologne [martin.kuhlmann|dalia.shohat] @systorsecurity.com Gerhard Schimpf SMF TEAM IT-Security Consulting Am Waldweg 23 D 75173 Pforzheim Gerhard.Schimpf@smfteam.de ABSTRACT In this paper we describe the work devising a new technique for role-finding to implement Role-Based Security Administration
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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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Troy Wilson* suggest a way for preserving and enhancing the value of exploration data E very year explorationists‚ industrywide‚ collect billions of dollars worth of data. Yet‚ when it comes time for geologists to extract value from their information‚ they often find that value has been lost through poor practices in data management. There is no reliable record of the data that has been collected or data is not where it should be - it has been misplaced or corrupted. Re-assembling information
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Excellence for Data Mining in Egypt By: Aref Rashad I- Introduction The convergence of computer resources connected via a global network has created an information tool of unprecedented power‚ a tool in its infancy. The global network is awash with data‚ uncoordinated‚ unexplored‚ but potentially containing information and knowledge of immense economic and technical significance. It is the role of data mining technologies arising from many discipline areas to convert that data into information
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Web Mining Data mining is the nontrivial process of identifying valid novel‚ potentially useful‚ and ultimately understandable patterns in data – Fayyad. The most commonly used techniques in data mining is artificial neural networks‚ decision trees‚ genetic algorithm‚ nearest_neighbour method‚ and rule induction. Data mining research has drawn on a number of other fields such as inductive learning‚ machine learning and statistics etc. Machine learning – is the automation of a learning process
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Why Data Mining can Aid HealthCare? Before talking about that why Data Mining can Aid Healthcare‚ there is a need to define that What is Data Mining‚ Why It is useful and Why it is required. What is Data Mining? Data Mining is the method of searching data from a large database. Data Mining is a technique that is used when we have a large database which is to be searched by applying some methods of searching. It is a technique that is applied when we have a concept of Data Warehouse. Data Warehouse
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Title: “Data Mining: The Mushroom Database” Author: Hemendra Pal Singh* In this review “Data Mining: The Mushroom Database” is focuses in the study of database or datasets of a mushroom. The purpose of the research is to broaden the preceding researches by administer new data sets of stylometry‚ keystroke capture‚ and mouse movement data through Weka. Weka stands for Waikato environment for knowledge analysis‚ and it is a popular suite of machine learning software written in Java‚ developed at
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will win is 60% and above.” Null Hypothesis “If X makes the first move then the probability of the player with X will win is less than 60%.” Data Collection and Preparation To prove or refute the hypothesis‚ data has to be collected. As we all know this step requires a great amount of time and effort. Also in order to build an effective model a data mining algorithm must be presented with a few hundred or few thousands relevant/applicable records. As mentioned above there are thousands of winning
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Classification-Based Data Mining Approach For Quality Control In Wine Production GUIDED BY: | | SUBMITTED BY: | Jayshri Patel | | Hardik Barfiwala | INDEX Sr No | Title | Page No. | 1 | Introduction Wine Production | | 2 | Objectives | | 3 | Introduction To Dataset | | 4 | Pre-Processing | | 5 | Statistics Used In Algorithms | | 6 | Algorithms Applied On Dataset | | 7 | Comparison Of Applied Algorithm | | 8 | Applying
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Title: Mining High Dimensional Educational (Student) Data to Extract Patterns for Interestingness An Introduction to Data Mining Overview Data mining‚ the extraction of hidden predictive information from large databases‚ is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors‚ allowing businesses to make proactive‚ knowledge-driven decisions. Data mining tools can
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